diff --git a/common/speculative.cpp b/common/speculative.cpp
index 843bd1ddb..5a67b46e1 100644
--- a/common/speculative.cpp
+++ b/common/speculative.cpp
@@ -278,3 +278,153 @@ llama_tokens common_speculative_gen_draft(
 
     return result;
 }
+
+llama_tokens common_speculative_gen_draft_eagle(
+        struct common_speculative * spec,
+        struct common_speculative_params params,
+        const llama_tokens & prompt_tgt,
+        llama_token id_last,
+        std::vector<uint8_t> & data) {
+    auto & batch  = spec->batch;
+    auto & ctx    = spec->ctx;
+    auto & smpl   = spec->smpl;
+    auto & prompt = spec->prompt;
+
+    auto * mem = llama_get_memory(ctx);
+
+    int reuse_i = 0;
+    int reuse_n = 0;
+
+    const int n_ctx = llama_n_ctx(ctx) - params.n_draft;
+
+    const int i_start = std::max<int>(1, (int) prompt_tgt.size() - n_ctx);
+
+    int n_accepted_draft_tokens = data.size() / sizeof(float) / llama_model_n_embd(llama_get_model(ctx)) - 1;
+
+    // reuse as much as possible from the old draft context
+    // ideally, the draft context should be as big as the target context and we will always reuse the entire prompt
+    for (int i = 0; i < (int) prompt.size(); ++i) {
+        int cur = 0;
+        while (i_start + cur < (int) prompt_tgt.size() &&
+            i       + cur < (int) prompt.size() &&
+            prompt_tgt[i_start + cur] == prompt[i + cur]) {
+            cur++;
+        }
+
+        cur = (cur - n_accepted_draft_tokens) > 0 ? (cur - n_accepted_draft_tokens) : cur;
+
+        if ((cur >= params.n_reuse || n_ctx >= (int) prompt_tgt.size()) && cur > reuse_n) {
+            reuse_i = i;
+            reuse_n = cur;
+        }
+    }
+
+    LOG_DBG("%s: reuse_i = %d, reuse_n = %d, prompt = %d\n", __func__, reuse_i, reuse_n, (int) prompt.size());
+
+    llama_tokens result;
+    result.reserve(params.n_draft);
+
+    if (reuse_n == 0) {
+        llama_memory_clear(mem, false);
+
+        prompt.clear();
+    } else {
+        // this happens when a previous draft has been discarded (for example, due to being too small), but the
+        // target model agreed with it. in this case, we simply pass back the previous results to save compute
+        if (reuse_i + reuse_n < (int) prompt.size() && prompt[reuse_i + reuse_n] == id_last) {
+            for (int i = reuse_i + reuse_n + 1; i < (int) prompt.size(); ++i) {
+                result.push_back(prompt[i]);
+
+                if (params.n_draft <= (int) result.size()) {
+                    break;
+                }
+            }
+
+            return result;
+        }
+
+        if (reuse_i > 0) {
+            llama_memory_seq_rm (mem, 0, 0, reuse_i);
+            llama_memory_seq_add(mem, 0, reuse_i, -1, -reuse_i);
+
+            prompt.erase(prompt.begin(), prompt.begin() + reuse_i);
+        }
+
+        if (reuse_n < (int) prompt.size()) {
+            llama_memory_seq_rm (mem, 0, reuse_n, -1);
+
+            prompt.erase(prompt.begin() + reuse_n, prompt.end());
+        }
+    }
+
+    // prepare a batch to evaluate any new tokens in the prompt
+    common_batch_clear(batch);
+
+    for (size_t i = i_start + reuse_n; i < prompt_tgt.size(); ++i) {
+        //LOG_DBG("i = %d, i_start = %d, reuse_n = %d, i - i_start = %d, id = %6d\n", i, i_start, reuse_n, i - i_start, prompt_tgt[i]);
+        common_batch_add(batch, prompt_tgt[i], i - i_start, { 0 }, (i < prompt_tgt.size() - 1) ? false : true);
+
+        prompt.push_back(prompt_tgt[i]);
+    }
+
+    // we should rarely end-up here during normal decoding
+    if (batch.n_tokens > 0) {
+        //LOG_DBG("%s: draft prompt batch: %s\n", __func__, string_from(ctx, batch).c_str());
+
+        llama_decode_eagle(ctx, batch, data.data());
+    }
+
+    const llama_pos n_past = prompt.size();
+
+    LOG_DBG("%s: n_past = %d\n", __func__, n_past);
+
+    common_batch_clear(batch);
+    common_batch_add  (batch, id_last, n_past, { 0 }, true);
+
+    prompt.push_back(id_last);
+
+    //LOG_DBG("%s: draft prompt: %s\n", __func__, string_from(ctx, prompt).c_str());
+
+    llama_decode_eagle(ctx, batch, data.data());
+
+    common_sampler_reset(smpl);
+
+    // sample n_draft tokens from the draft model
+    for (int i = 0; i < params.n_draft; ++i) {
+        common_batch_clear(batch);
+
+        common_sampler_sample(smpl, ctx, -1, true);
+
+        const auto * cur_p = common_sampler_get_candidates(smpl);
+
+        for (int k = 0; k < std::min(1, (int) cur_p->size); ++k) {
+            LOG_DBG(" - draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
+                    k, i, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(ctx, cur_p->data[k].id).c_str());
+        }
+
+        // add drafted token for each sequence
+        const llama_token id = cur_p->data[0].id;
+
+        common_sampler_accept(smpl, id, true);
+
+        result.push_back(id);
+
+        if (params.n_draft <= (int) result.size()) {
+            break;
+        }
+
+        // only collect very high-confidence draft tokens
+        if (cur_p->data[0].p < params.p_min) {
+            break;
+        }
+
+        common_batch_add(batch, id, n_past + i + 1, { 0 }, true);
+
+        // evaluate the drafted tokens on the draft model
+        llama_decode_eagle(ctx, batch, data.data());
+
+        prompt.push_back(id);
+    }
+
+    return result;
+}
diff --git a/common/speculative.h b/common/speculative.h
index 2b51a70ca..4f1725f1e 100644
--- a/common/speculative.h
+++ b/common/speculative.h
@@ -26,3 +26,10 @@ llama_tokens common_speculative_gen_draft(
         struct common_speculative_params   params,
                       const llama_tokens & prompt,
                              llama_token   id_last);
+
+llama_tokens common_speculative_gen_draft_eagle(
+               struct common_speculative * spec,
+        struct common_speculative_params   params,
+                      const llama_tokens & prompt,
+                             llama_token   id_last,
+                    std::vector<uint8_t> & data);
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 49e4d2cf8..0f313bff1 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -31,6 +31,7 @@ else()
     add_subdirectory(simple-chat)
     add_subdirectory(speculative)
     add_subdirectory(speculative-simple)
+    add_subdirectory(speculative-simple-eagle)
     add_subdirectory(gen-docs)
     add_subdirectory(training)
     if (NOT GGML_BACKEND_DL)
diff --git a/examples/speculative-simple-eagle/CMakeLists.txt b/examples/speculative-simple-eagle/CMakeLists.txt
new file mode 100644
index 000000000..1013ca88e
--- /dev/null
+++ b/examples/speculative-simple-eagle/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(TARGET llama-speculative-simple-eagle)
+add_executable(${TARGET} speculative-simple-eagle.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
diff --git a/examples/speculative-simple-eagle/README.md b/examples/speculative-simple-eagle/README.md
new file mode 100644
index 000000000..43cf9d81d
--- /dev/null
+++ b/examples/speculative-simple-eagle/README.md
@@ -0,0 +1,12 @@
+# llama.cpp/examples/speculative-simple-eagle
+
+Demonstration of basic greedy speculative decoding for EAGLE
+
+```bash
+./bin/llama-speculative-simple-eagle \
+    -m  ../models/qwen2.5-32b-coder-instruct/ggml-model-q8_0.gguf \
+    -md ../models/qwen2.5-1.5b-coder-instruct/ggml-model-q4_0.gguf \
+    -f test.txt -c 0 -ngl 99 --color \
+    --sampling-seq k --top-k 1 -fa --temp 0.0 \
+    -ngld 99 --draft-max 16 --draft-min 5 --draft-p-min 0.9
+```
diff --git a/examples/speculative-simple-eagle/speculative-simple-eagle.cpp b/examples/speculative-simple-eagle/speculative-simple-eagle.cpp
new file mode 100644
index 000000000..ceecb4fb5
--- /dev/null
+++ b/examples/speculative-simple-eagle/speculative-simple-eagle.cpp
@@ -0,0 +1,301 @@
+#include "arg.h"
+#include "common.h"
+#include "sampling.h"
+#include "speculative.h"
+#include "log.h"
+#include "llama.h"
+#include "../src/llama-model.h"
+
+#include <cstdio>
+#include <cstring>
+#include <string>
+#include <vector>
+
+struct callback_data {
+    std::vector<uint8_t> data;
+};
+
+static bool cb_get_hidden(struct ggml_tensor * tensor, bool ask, void * user_data) {
+    if (ask) {
+        static const char * result_norm_name = "result_norm";
+        const bool is_result_norm = strcmp(tensor->name, result_norm_name) == 0;
+        return is_result_norm;
+    }
+
+    auto * cb_data = (struct callback_data *) user_data;
+    auto n_bytes = ggml_nbytes(tensor);
+    cb_data->data.resize(n_bytes);
+    ggml_backend_tensor_get(tensor, cb_data->data.data(), 0, n_bytes);
+
+    return true;
+}
+
+int main(int argc, char ** argv) {
+    common_params params;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) {
+        return 1;
+    }
+
+    if (params.n_predict < -1) {
+        LOG_ERR("%s: --n-predict must be >= -1\n", __func__);
+        return 1;
+    }
+
+    common_init();
+
+    if (params.speculative.model.path.empty()) {
+        LOG_ERR("%s: --model-draft is required\n", __func__);
+        return 1;
+    }
+
+    // init llama.cpp
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    callback_data cb_data;
+    params.cb_eval = cb_get_hidden;
+    params.cb_eval_user_data = &cb_data;
+
+    llama_model * model_tgt = NULL;
+    //llama_model * model_dft = NULL;
+
+    llama_context * ctx_tgt = NULL;
+    llama_context * ctx_dft = NULL;
+
+    // load the target model
+    common_init_result llama_init_tgt = common_init_from_params(params);
+
+    model_tgt = llama_init_tgt.model.get();
+    ctx_tgt   = llama_init_tgt.context.get();
+
+    const llama_vocab * vocab = llama_model_get_vocab(model_tgt);
+
+    // load the draft model
+    params.devices      = params.speculative.devices;
+    params.model        = params.speculative.model;
+    params.n_ctx        = params.speculative.n_ctx;
+    params.n_batch      = params.speculative.n_ctx > 0 ? params.speculative.n_ctx : params.n_batch;
+    params.n_gpu_layers = params.speculative.n_gpu_layers;
+
+    if (params.speculative.cpuparams.n_threads > 0) {
+        params.cpuparams.n_threads = params.speculative.cpuparams.n_threads;
+    }
+
+    params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
+    common_init_result llama_init_dft = common_init_from_params(params);
+
+    //model_dft = llama_init_dft.model.get();
+    ctx_dft   = llama_init_dft.context.get();
+
+    // Trick: if the output buffer is in host memory, we need to allocate a new buffer for the draft model
+    if (ggml_backend_buffer_is_host(llama_get_model(ctx_dft)->output->buffer)) {
+        void * data = malloc(ggml_nbytes(llama_get_model(ctx_tgt)->output));
+        llama_get_model(ctx_dft)->output->data = data;
+    }
+    // copy output parameters from target to draft
+    ggml_backend_tensor_copy(llama_get_model(ctx_tgt)->output, llama_get_model(ctx_dft)->output);
+
+    if (!common_speculative_are_compatible(ctx_tgt, ctx_dft)) {
+        return 1;
+    }
+
+    // Tokenize the prompt
+    std::vector<llama_token> inp;
+    inp = common_tokenize(ctx_tgt, params.prompt, true, true);
+
+    if (llama_n_ctx(ctx_tgt) < (uint32_t) inp.size()) {
+        LOG_ERR("%s: the prompt exceeds the context size (%d tokens, ctx %d)\n", __func__, (int) inp.size(), llama_n_ctx(ctx_tgt));
+
+        return 1;
+    }
+
+    if (llama_n_batch(ctx_tgt) < (uint32_t) inp.size()) {
+        LOG_ERR("%s: the prompt exceeds the batch size (%d tokens, batch %d)\n", __func__, (int) inp.size(), llama_n_batch(ctx_tgt));
+
+        return 1;
+    }
+
+    LOG("\n\n");
+
+    for (auto id : inp) {
+        LOG("%s", common_token_to_piece(ctx_tgt, id).c_str());
+    }
+
+    // how many tokens to draft each time
+    int n_draft     = params.speculative.n_max;
+    int n_draft_min = params.speculative.n_min;
+
+    float p_min = params.speculative.p_min;
+
+    int n_predict = 0;
+    int n_drafted = 0;
+    int n_accept  = 0;
+
+    // used to determine end of generation
+    bool has_eos = false;
+
+    // ================================================
+    // everything until here is standard initialization
+    // the relevant stuff for speculative decoding starts here
+
+    const auto t_enc_start = ggml_time_us();
+
+    // target model sampling context
+    struct common_sampler * smpl = common_sampler_init(model_tgt, params.sampling);
+
+    llama_batch temp_batch_tgt = llama_batch_init(llama_n_batch(ctx_tgt), 0, 1);
+    int temp_n_past = 0;
+    for (size_t i = 0; i < inp.size(); i++) {
+        common_batch_add(temp_batch_tgt, inp[i], temp_n_past++, { 0 }, true);
+    }
+
+    // eval the prompt
+    llama_decode(ctx_tgt, temp_batch_tgt);
+
+    // note: keep the last token separate!
+    llama_token id_last = inp.back();
+
+    // all tokens currently in the target context
+    llama_tokens prompt_tgt(inp.begin(), inp.end());
+    prompt_tgt.reserve(llama_n_ctx(ctx_tgt));
+
+    int n_past = inp.size() + 1;
+
+    // init the speculator
+    struct common_speculative_params params_spec;
+    params_spec.n_draft = n_draft;
+    params_spec.n_reuse = llama_n_ctx(ctx_dft) - n_draft;
+    params_spec.p_min   = p_min;
+
+    struct common_speculative * spec = common_speculative_init(ctx_dft);
+
+    llama_batch batch_tgt = llama_batch_init(llama_n_batch(ctx_tgt), 0, 1);
+
+    const auto t_enc_end = ggml_time_us();
+
+    const auto t_dec_start = ggml_time_us();
+
+    while (true) {
+        // optionally, generate draft tokens that can be appended to the target batch
+        //
+        // this is the most important part of the speculation. the more probable tokens that are provided here
+        // the better the performance will be. in theory, this computation can be performed asynchronously and even
+        // offloaded to a remote device. it doesn't even have to be based on an LLM. instead, it can provide tokens
+        // from a cache or lookup tables.
+        //
+        llama_tokens draft = common_speculative_gen_draft_eagle(spec, params_spec, prompt_tgt, id_last, cb_data.data);
+
+        //LOG_DBG("draft: %s\n", string_from(ctx_dft, draft).c_str());
+
+        // always have a token to evaluate from before - id_last
+        common_batch_clear(batch_tgt);
+        common_batch_add  (batch_tgt, id_last, n_past++, { 0 }, true);
+
+        // evaluate the target model on [id_last, draft0, draft1, ..., draftN-1]
+        {
+            // do not waste time on small drafts
+            if (draft.size() < (size_t) n_draft_min) {
+                draft.clear();
+            }
+
+            for (size_t i = 0; i < draft.size(); ++i) {
+                common_batch_add(batch_tgt, draft[i], n_past + i, { 0 }, true);
+            }
+
+            //LOG_DBG("target batch: %s\n", string_from(ctx_tgt, batch_tgt).c_str());
+
+            llama_decode(ctx_tgt, batch_tgt);
+        }
+
+        // sample from the full target batch and return the accepted tokens based on the target sampler
+        //
+        // for each token to be accepted, the sampler would have to sample that same token
+        // in such cases, instead of decoding the sampled token as we normally do, we simply continue with the
+        // available logits from the batch and sample the next token until we run out of logits or the sampler
+        // disagrees with the draft
+        //
+        const auto ids = common_sampler_sample_and_accept_n(smpl, ctx_tgt, draft);
+
+        //LOG_DBG("ids: %s\n", string_from(ctx_tgt, ids).c_str());
+
+        GGML_ASSERT(ids.size() > 0); // there will always be at least one accepted token
+
+        n_past    += ids.size() - 1;
+        n_drafted += draft.size(); // note: we ignore the discarded small drafts
+        n_accept  += ids.size() - 1;
+        n_predict += ids.size();
+
+        // process the accepted tokens and update contexts
+        //
+        // this is the standard token post-processing that we normally do
+        // in this case, we do it for a group of accepted tokens at once
+        //
+        for (size_t i = 0; i < ids.size(); ++i) {
+            prompt_tgt.push_back(id_last);
+
+            id_last = ids[i];
+
+            if (llama_vocab_is_eog(vocab, id_last)) {
+                has_eos = true;
+                break;
+            }
+
+            const std::string token_str = common_token_to_piece(ctx_tgt, id_last);
+
+            if (params.use_color && i + 1 < ids.size()) {
+                LOG("\u001b[%dm%s\u001b[37m", (36 - 0 % 6), token_str.c_str());
+            } else {
+                LOG("%s", token_str.c_str());
+            }
+        }
+
+        cb_data.data.resize(ids.size() * sizeof(float) * llama_model_n_embd(model_tgt));
+
+        LOG_DBG("accepted %d/%d draft tokens, the last target token is: (%d)\n", (int) ids.size() - 1, (int) draft.size(), id_last);
+
+        {
+            LOG_DBG("clear kv cache from any extra tokens, n_past = %d\n", n_past);
+
+            llama_memory_seq_rm(llama_get_memory(ctx_tgt), 0, n_past, -1);
+        }
+
+        if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
+            break;
+        }
+    }
+
+    auto t_dec_end = ggml_time_us();
+
+    const int n_input = inp.size();
+
+    LOG("\n\n");
+
+    LOG_INF("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input,   (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
+    LOG_INF("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict  / ((t_dec_end - t_dec_start) / 1e6f));
+
+    LOG_INF("\n");
+    LOG_INF("n_draft   = %d\n", n_draft);
+    LOG_INF("n_predict = %d\n", n_predict);
+    LOG_INF("n_drafted = %d\n", n_drafted);
+    LOG_INF("n_accept  = %d\n", n_accept);
+    LOG_INF("accept    = %.3f%%\n", 100.0f * n_accept / n_drafted);
+
+    LOG_INF("\n");
+    LOG_INF("draft:\n\n");
+
+    llama_perf_context_print(ctx_dft);
+
+    LOG_INF("\n");
+    LOG_INF("target:\n\n");
+    common_perf_print(ctx_tgt, smpl);
+
+    common_sampler_free(smpl);
+    common_speculative_free(spec);
+
+    llama_backend_free();
+
+    LOG("\n\n");
+
+    return 0;
+}
diff --git a/include/llama.h b/include/llama.h
index 635508b10..4842d90d3 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -953,6 +953,11 @@ extern "C" {
             struct llama_context * ctx,
               struct llama_batch   batch);
 
+    LLAMA_API int32_t llama_decode_eagle(
+            struct llama_context * ctx,
+              struct llama_batch   batch,
+                            void * data);
+
     // Set the number of threads used for decoding
     // n_threads is the number of threads used for generation (single token)
     // n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 0bc60565d..ac1a7d298 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -75,6 +75,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_BAILINGMOE,       "bailingmoe"       },
     { LLM_ARCH_DOTS1,            "dots1"            },
     { LLM_ARCH_ARCEE,            "arcee"            },
+    { LLM_ARCH_EAGLE,            "eagle"            },
     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
 };
 
@@ -1620,6 +1621,23 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_EXP_PROBS_B,    "blk.%d.exp_probs_b" },
         }
     },
+    {
+        LLM_ARCH_EAGLE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_EMBD_FC,         "fc" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_UNKNOWN,
         {
@@ -1633,6 +1651,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_POS_EMBD,                   {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
     {LLM_TENSOR_TOKEN_EMBD_NORM,            {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
     {LLM_TENSOR_TOKEN_TYPES,                {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
+    {LLM_TENSOR_EMBD_FC,                    {LLM_TENSOR_LAYER_INPUT, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_OUTPUT,                     {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_CLS,                        {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_CLS_OUT,                    {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 51b242c66..ee65100a9 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -79,6 +79,7 @@ enum llm_arch {
     LLM_ARCH_BAILINGMOE,
     LLM_ARCH_DOTS1,
     LLM_ARCH_ARCEE,
+    LLM_ARCH_EAGLE,
     LLM_ARCH_UNKNOWN,
 };
 
@@ -229,6 +230,7 @@ enum llm_tensor {
     LLM_TENSOR_TOKEN_EMBD_NORM,
     LLM_TENSOR_TOKEN_TYPES,
     LLM_TENSOR_POS_EMBD,
+    LLM_TENSOR_EMBD_FC,
     LLM_TENSOR_OUTPUT,
     LLM_TENSOR_OUTPUT_NORM,
     LLM_TENSOR_ROPE_FREQS,
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index f56a58e9b..60f4441c7 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -721,6 +721,50 @@ llm_graph_result_ptr llama_context::process_ubatch(const llama_ubatch & ubatch,
     return res;
 }
 
+llm_graph_result_ptr llama_context::process_ubatch_eagle(const llama_ubatch & ubatch, llm_graph_type gtype, llama_memory_state_i * mstate, ggml_status & ret, void * data) {
+    if (mstate && !mstate->apply()) {
+        LLAMA_LOG_ERROR("%s: failed to apply memory state\n", __func__);
+        ret = GGML_STATUS_FAILED;
+        return nullptr;
+    }
+
+    auto * gf = graph_init();
+    if (!gf) {
+        LLAMA_LOG_ERROR("%s: failed to initialize graph\n", __func__);
+        ret = GGML_STATUS_FAILED;
+        return nullptr;
+    }
+
+    auto res = graph_build(ctx_compute.get(), gf, ubatch, gtype, mstate);
+    if (!res) {
+        LLAMA_LOG_ERROR("%s: failed to build graph\n", __func__);
+        ret = GGML_STATUS_FAILED;
+        return nullptr;
+    }
+
+    // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
+
+    if (!ggml_backend_sched_alloc_graph(sched.get(), gf)) {
+        LLAMA_LOG_ERROR("%s: failed to allocate graph\n", __func__);
+        ret = GGML_STATUS_ALLOC_FAILED;
+        return nullptr;
+    }
+
+    res->set_inputs(&ubatch);
+    ggml_backend_tensor_set(res->get_hidden_states(), data, 0, ggml_element_size(res->get_hidden_states()) * model.hparams.n_embd * ubatch.n_tokens);
+
+    const auto status = graph_compute(gf, ubatch.n_tokens > 1);
+    if (status != GGML_STATUS_SUCCESS) {
+        LLAMA_LOG_ERROR("%s: failed to compute graph, compute status: %d\n", __func__, status);
+        ret = status;
+        return nullptr;
+    }
+
+    ret = GGML_STATUS_SUCCESS;
+
+    return res;
+}
+
 int llama_context::encode(const llama_batch & batch_inp) {
     if (batch_inp.n_tokens == 0) {
         LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__);
@@ -1208,6 +1252,331 @@ int llama_context::decode(const llama_batch & batch_inp) {
     return 0;
 }
 
+int llama_context::decode_eagle(const llama_batch & batch_inp, void * data) {
+    if (!memory) {
+        LLAMA_LOG_DEBUG("%s: cannot decode batches with this context (calling encode() instead)\n", __func__);
+        return encode(batch_inp);
+    }
+
+    if (batch_inp.n_tokens == 0) {
+        LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__);
+        return -1;
+    }
+
+    // when computing embeddings, all tokens are output
+    const bool embd_all = cparams.embeddings;
+
+    if (!batch_allocr->init(batch_inp, model.vocab, memory.get(), embd_all)) {
+        LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__);
+        return -1;
+    }
+
+    const llama_batch & batch = batch_allocr->get_batch();
+
+    const auto & vocab   = model.vocab;
+    const auto & hparams = model.hparams;
+
+    const int32_t n_vocab = vocab.n_tokens();
+    const int64_t n_embd  = hparams.n_embd;
+
+    const uint32_t n_tokens_all = batch.n_tokens;
+
+    GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
+
+    const uint32_t n_outputs_all = batch_allocr->get_n_outputs();
+
+    if (embd_all) {
+        // require that all tokens are output
+        if (n_outputs_all != n_tokens_all) {
+            LLAMA_LOG_ERROR("%s: pooled embedding requires that all tokens are output (n_outputs_all = %d, n_tokens_all = %d)\n",
+                    __func__, n_outputs_all, n_tokens_all);
+            return -1;
+        }
+    }
+
+    GGML_ASSERT(n_tokens_all <= cparams.n_batch);
+
+    GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens");
+
+    if (t_compute_start_us == 0) {
+        t_compute_start_us = ggml_time_us();
+    }
+    n_queued_tokens += n_tokens_all;
+
+    // TODO: this clear of the buffer can easily be forgotten - need something better
+    embd_seq.clear();
+
+    bool did_optimize = false;
+
+    // handle any pending defrags/shifts
+    kv_self_update(false);
+
+    llama_memory_state_ptr mstate;
+
+    while (true) {
+        mstate = memory->init_batch(batch, cparams.n_ubatch, embd_all);
+        if (!mstate) {
+            return -2;
+        }
+
+        switch (mstate->get_status()) {
+            case LLAMA_MEMORY_STATUS_SUCCESS:
+                {
+                } break;
+            case LLAMA_MEMORY_STATUS_NO_UPDATE:
+                {
+                    LLAMA_LOG_ERROR("%s: unexpected memory state status: %d\n", __func__, mstate->get_status());
+
+                    return -2;
+                }
+            case LLAMA_MEMORY_STATUS_FAILED_PREPARE:
+                {
+                    if (!did_optimize) {
+                        did_optimize = true;
+
+                        if (kv_self_update(true)) {
+                            LLAMA_LOG_DEBUG("%s: retrying batch size %d after cache optimization\n", __func__, batch.n_tokens);
+
+                            continue;
+                        }
+                    }
+
+                    LLAMA_LOG_WARN("%s: failed to find a memory slot for batch of size %d\n", __func__, batch.n_tokens);
+
+                    return 1;
+                }
+            case LLAMA_MEMORY_STATUS_FAILED_COMPUTE:
+                {
+                    LLAMA_LOG_ERROR("%s: compute failed while preparing batch of size %d\n", __func__, batch.n_tokens);
+
+                    return -2;
+                }
+        }
+
+        break;
+    }
+
+    // reserve output buffer
+    if (output_reserve(n_outputs_all) < n_outputs_all) {
+        LLAMA_LOG_ERROR("%s: could not reserve space for batch with %d outputs\n", __func__, n_outputs_all);
+        return -2;
+    };
+
+    int64_t n_outputs_prev = 0;
+
+    do {
+        const auto & ubatch = mstate->get_ubatch();
+
+        // count the outputs in this ubatch
+        {
+            int32_t n_outputs_new = 0;
+
+            if (n_outputs_all == n_tokens_all) {
+                n_outputs_new = ubatch.n_tokens;
+            } else {
+                GGML_ASSERT(ubatch.output);
+                for (uint32_t i = 0; i < ubatch.n_tokens; i++) {
+                    n_outputs_new += (int32_t) (ubatch.output[i] != 0);
+                }
+            }
+
+            // needs to happen before the graph is built
+            n_outputs = n_outputs_new;
+        }
+
+        ggml_backend_sched_reset(sched.get());
+        ggml_backend_sched_set_eval_callback(sched.get(), cparams.cb_eval, cparams.cb_eval_user_data);
+
+        ggml_status status;
+        const auto res = process_ubatch_eagle(ubatch, LLM_GRAPH_TYPE_DECODER, mstate.get(), status, data);
+
+        if (!res) {
+            // the last ubatch failed or was aborted -> remove all positions of that ubatch from the KV cache
+            llama_pos pos_min[LLAMA_MAX_SEQ];
+            for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
+                pos_min[s] = std::numeric_limits<llama_pos>::max();
+            }
+
+            // TODO: fix sequence indexing
+            for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
+                const auto & seq_id = ubatch.seq_id[i][0];
+
+                pos_min[seq_id] = std::min(pos_min[seq_id], ubatch.pos[i]);
+            }
+
+            for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
+                if (pos_min[s] == std::numeric_limits<llama_pos>::max()) {
+                    continue;
+                }
+
+                LLAMA_LOG_WARN("%s: removing KV cache entries for seq_id = %d, pos = [%d, +inf)\n", __func__, s, pos_min[s]);
+
+                memory->seq_rm(s, pos_min[s], -1);
+            }
+
+            switch (status) {
+                case GGML_STATUS_ABORTED:      return  2;
+                case GGML_STATUS_ALLOC_FAILED: return -2;
+                case GGML_STATUS_FAILED:       return -3;
+                case GGML_STATUS_SUCCESS:      GGML_ABORT("should not happen");
+            }
+        }
+
+        // plot the computation graph in dot format (for debugging purposes)
+        //if (n_past%100 == 0) {
+        //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
+        //}
+
+        auto * t_logits = res->get_logits();
+        auto * t_embd   = cparams.embeddings ? res->get_embd() : nullptr;
+
+        if (t_embd && res->get_embd_pooled()) {
+            t_embd = res->get_embd_pooled();
+        }
+
+        // extract logits
+        if (t_logits && n_outputs > 0) {
+            ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
+            GGML_ASSERT(backend_res != nullptr);
+            GGML_ASSERT(logits != nullptr);
+
+            float * logits_out = logits + n_outputs_prev*n_vocab;
+
+            if (n_outputs) {
+                GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
+                GGML_ASSERT((n_outputs_prev + n_outputs)*n_vocab <= (int64_t) logits_size);
+                ggml_backend_tensor_get_async(backend_res, t_logits, logits_out, 0, n_outputs*n_vocab*sizeof(float));
+            }
+        }
+
+        // extract embeddings
+        if (t_embd && n_outputs > 0) {
+            ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(sched.get(), t_embd);
+            GGML_ASSERT(backend_embd != nullptr);
+
+            switch (cparams.pooling_type) {
+                case LLAMA_POOLING_TYPE_NONE:
+                    {
+                        // extract token embeddings
+                        GGML_ASSERT(embd != nullptr);
+                        float * embd_out = embd + n_outputs_prev*n_embd;
+
+                        if (n_outputs) {
+                            GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
+                            GGML_ASSERT((n_outputs_prev + n_outputs)*n_embd <= (int64_t) embd_size);
+                            ggml_backend_tensor_get_async(backend_embd, t_embd, embd_out, 0, n_outputs*n_embd*sizeof(float));
+                        }
+                    } break;
+                case LLAMA_POOLING_TYPE_MEAN:
+                case LLAMA_POOLING_TYPE_CLS:
+                case LLAMA_POOLING_TYPE_LAST:
+                    {
+                        // extract sequence embeddings (cleared before processing each batch)
+                        auto & embd_seq_out = embd_seq;
+
+                        for (uint32_t s = 0; s < ubatch.n_seqs; ++s) {
+                            const llama_seq_id seq_id = ubatch.seq_id[s][0];
+                            if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
+                                continue;
+                            }
+                            embd_seq_out[seq_id].resize(n_embd);
+                            ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
+                        }
+                    } break;
+                case LLAMA_POOLING_TYPE_RANK:
+                    {
+                        // extract the rerank score - a single float per sequence
+                        auto & embd_seq_out = embd_seq;
+
+                        for (uint32_t s = 0; s < ubatch.n_seqs; ++s) {
+                            const llama_seq_id seq_id = ubatch.seq_id[s][0];
+                            if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
+                                continue;
+                            }
+                            embd_seq_out[seq_id].resize(1);
+                            ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (seq_id)*sizeof(float), sizeof(float));
+                        }
+                    } break;
+                case LLAMA_POOLING_TYPE_UNSPECIFIED:
+                    {
+                        GGML_ABORT("unknown pooling type");
+                    }
+            }
+        }
+
+        n_outputs_prev += n_outputs;
+    } while (mstate->next());
+
+    // set to total number of outputs in the batch, for use in llama_get_logits_ith
+    n_outputs = n_outputs_all;
+
+    // set output mappings
+    if (n_outputs > 0) {
+        bool sorted_output = true;
+
+        auto & out_ids = mstate->out_ids();
+
+        GGML_ASSERT(out_ids.size() == (size_t) n_outputs);
+
+        for (int64_t i = 0; i < n_outputs; ++i) {
+            int64_t out_id = out_ids[i];
+            output_ids[out_id] = i;
+            if (out_id != i) {
+                sorted_output = false;
+            }
+        }
+
+        // make the outputs have the same order they had in the user-provided batch
+        // note: this is mostly relevant for recurrent models atm
+        if (!sorted_output) {
+            const uint32_t n_vocab = model.vocab.n_tokens();
+            const uint64_t n_embd  = model.hparams.n_embd;
+
+            GGML_ASSERT((size_t) n_outputs == out_ids.size());
+
+            // TODO: is there something more efficient which also minimizes swaps?
+            // selection sort, to minimize swaps (from https://en.wikipedia.org/wiki/Selection_sort)
+            for (uint32_t i = 0; i < n_outputs - 1; ++i) {
+                uint32_t j_min = i;
+                for (uint32_t j = i + 1; j < n_outputs; ++j) {
+                    if (out_ids[j] < out_ids[j_min]) {
+                        j_min = j;
+                    }
+                }
+                if (j_min == i) {
+                    continue;
+                }
+                std::swap(out_ids[i], out_ids[j_min]);
+                if (logits_size > 0) {
+                    for (uint32_t k = 0; k < n_vocab; k++) {
+                        std::swap(logits[i*n_vocab + k], logits[j_min*n_vocab + k]);
+                    }
+                }
+                if (embd_size > 0) {
+                    for (uint32_t k = 0; k < n_embd; k++) {
+                        std::swap(embd[i*n_embd + k], embd[j_min*n_embd + k]);
+                    }
+                }
+            }
+
+            std::fill(output_ids.begin(), output_ids.end(), -1);
+
+            for (uint32_t i = 0; i < n_outputs; ++i) {
+                output_ids[out_ids[i]] = i;
+            }
+        }
+    }
+
+    // wait for the computation to finish (automatically done when obtaining the model output)
+    //synchronize();
+
+    // Reset state for the next token before backend sync, to allow the CPU activities in the reset to
+    // overlap with device computation.
+    ggml_backend_sched_reset(sched.get());
+
+    return 0;
+}
+
 //
 // output
 //
@@ -2786,6 +3155,18 @@ int32_t llama_decode(
     return ret;
 }
 
+int32_t llama_decode_eagle(
+        llama_context * ctx,
+          llama_batch   batch,
+                 void * data) {
+    const int ret = ctx->decode_eagle(batch, data);
+    if (ret != 0 && ret != 1) {
+        LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
+    }
+
+    return ret;
+}
+
 //
 // perf
 //
diff --git a/src/llama-context.h b/src/llama-context.h
index 040f03ae4..367a61cac 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -102,9 +102,18 @@ struct llama_context {
             llama_memory_state_i * mstate,
                      ggml_status & ret);
 
+    llm_graph_result_ptr process_ubatch_eagle(
+              const llama_ubatch & ubatch,
+                  llm_graph_type   gtype,
+            llama_memory_state_i * mstate,
+                     ggml_status & ret,
+                            void * data);
+
     int encode(const llama_batch & batch_inp);
     int decode(const llama_batch & batch_inp);
 
+    int decode_eagle(const llama_batch & batch_inp, void * data);
+
     //
     // state save/load
     //
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 65d98cbbb..79bac1b91 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -914,6 +914,26 @@ ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const {
     return cur;
 }
 
+ggml_tensor * llm_graph_context::build_inp_embd_fc(ggml_tensor * embd, ggml_tensor * fc, ggml_tensor * fc_b) const {
+    ggml_tensor * cur = nullptr;
+
+    ggml_tensor * hidden_states = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, ubatch.n_tokens);
+    ggml_set_input(hidden_states);
+
+    cur = ggml_concat(ctx0, embd, hidden_states, 0);
+    cur = ggml_mul_mat(ctx0, fc, cur);
+    if (fc_b) {
+        cur = ggml_add(ctx0, cur, fc_b);
+    }
+
+    cb(cur, "inp_embd_fc_out", -1);
+
+    res->set_hidden_states(hidden_states);
+
+    return cur;
+}
+
+
 ggml_tensor * llm_graph_context::build_inp_pos() const {
     auto inp = std::make_unique<llm_graph_input_pos>(n_pos_per_embd());
 
diff --git a/src/llama-graph.h b/src/llama-graph.h
index 58845e284..c8182843e 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -347,6 +347,9 @@ class llm_graph_result_i {
     virtual ggml_tensor * get_embd()        = 0;
     virtual ggml_tensor * get_embd_pooled() = 0;
 
+    virtual void set_hidden_states(ggml_tensor * hidden_states) = 0;
+    virtual ggml_tensor * get_hidden_states() = 0;
+
     virtual void set_inputs(const llama_ubatch * ubatch) = 0;
 };
 
@@ -362,6 +365,13 @@ class llm_graph_result : public llm_graph_result_i {
     ggml_tensor * get_embd()        override { return t_embd; }
     ggml_tensor * get_embd_pooled() override { return t_embd_pooled; }
 
+    void set_hidden_states(ggml_tensor * hidden_states) override {
+        t_hidden_states = hidden_states;
+    }
+    ggml_tensor * get_hidden_states() override {
+        return t_hidden_states;
+    }
+
     void set_inputs(const llama_ubatch * ubatch) override {
         for (auto & input : inputs) {
             input->set_input(ubatch);
@@ -379,6 +389,8 @@ class llm_graph_result : public llm_graph_result_i {
     ggml_tensor * t_embd        = nullptr;
     ggml_tensor * t_embd_pooled = nullptr;
 
+    ggml_tensor * t_hidden_states = nullptr;
+
     std::vector<llm_graph_input_ptr> inputs;
 };
 
@@ -531,6 +543,7 @@ struct llm_graph_context {
     //
 
     ggml_tensor * build_inp_embd(ggml_tensor * tok_embd) const;
+    ggml_tensor * build_inp_embd_fc(ggml_tensor * embd, ggml_tensor * fc, ggml_tensor * fc_b) const;
     ggml_tensor * build_inp_pos() const;
     ggml_tensor * build_inp_attn_scale() const;
     ggml_tensor * build_inp_out_ids() const;
diff --git a/src/llama-model-loader.h b/src/llama-model-loader.h
index 0f52b011b..5e6f5f65e 100644
--- a/src/llama-model-loader.h
+++ b/src/llama-model-loader.h
@@ -60,6 +60,7 @@ struct llama_model_loader {
 
     static const int TENSOR_NOT_REQUIRED = 1;
     static const int TENSOR_DUPLICATED   = 2;
+    static const int TENSOR_DUPLICATED_EAGLE = 4;
 
     int n_kv      = 0;
     int n_tensors = 0;
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index a5853f8b1..1c52dc9fa 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1484,6 +1484,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_EAGLE:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+            } break;
         default: throw std::runtime_error("unsupported model architecture");
     }
 
@@ -1613,6 +1617,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
 
     const auto TENSOR_DUPLICATED   = llama_model_loader::TENSOR_DUPLICATED;
     const auto TENSOR_NOT_REQUIRED = llama_model_loader::TENSOR_NOT_REQUIRED;
+    const auto TENSOR_DUPLICATED_EAGLE = llama_model_loader::TENSOR_DUPLICATED_EAGLE;
 
     // create tensors for the weights
     {
@@ -1660,6 +1665,10 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                 tn_tensor = LLM_TENSOR_OUTPUT;
             }
 
+            if (tn.tensor == LLM_TENSOR_TOKEN_EMBD && flags & TENSOR_DUPLICATED_EAGLE) {
+                tn_tensor = LLM_TENSOR_OUTPUT;
+            }
+
             llm_tensor_info info;
             try {
                 info = llm_tensor_info_for(tn_tensor);
@@ -4268,6 +4277,38 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
 
                         layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
 
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                    }
+                } break;
+             case LLM_ARCH_EAGLE:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    embd_fc   = create_tensor(tn(LLM_TENSOR_EMBD_FC, "weight"), {n_embd * 2, n_embd}, 0);
+                    embd_fc_b = create_tensor(tn(LLM_TENSOR_EMBD_FC, "bias"),   {n_embd}, 0);
+
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED_EAGLE);
+                        ml.n_created--;
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight"), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                         layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                         layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                     }
@@ -13731,6 +13772,161 @@ struct llm_build_arcee : public llm_graph_context {
     }
 };
 
+struct llm_build_eagle : public llm_graph_context {
+    llm_build_eagle(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // For EAGLE architecture
+        inpL = build_inp_embd_fc(inpL, model.embd_fc, model.embd_fc_b);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv_unified();
+
+        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            cur = inpL;
+
+            // self-attention
+            {
+                // rope freq factors for llama3; may return nullptr for llama2 and other models
+                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, rope_factors,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow
+                        );
+
+                Kcur = ggml_rope_ext(
+                        ctx0, Kcur, inp_pos, rope_factors,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow
+                        );
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+                cb(cur, "attn_out", il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network (non-MoE)
+            if (model.layers[il].ffn_gate_inp == nullptr) {
+
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+            } else {
+                // MoE branch
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = build_moe_ffn(cur,
+                        model.layers[il].ffn_gate_inp,
+                        model.layers[il].ffn_up_exps,
+                        model.layers[il].ffn_gate_exps,
+                        model.layers[il].ffn_down_exps,
+                        nullptr,
+                        n_expert, n_expert_used,
+                        LLM_FFN_SILU, true,
+                        false, 0.0,
+                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                        il);
+                cb(cur, "ffn_moe_out", il);
+            }
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+};
+
 llama_memory_i * llama_model::create_memory(const llama_memory_params & params, llama_cparams & cparams) const {
     llama_memory_i * res;
 
@@ -14102,6 +14298,10 @@ llm_graph_result_ptr llama_model::build_graph(
             {
                 llm = std::make_unique<llm_build_arcee>(*this, params, gf);
             } break;
+        case LLM_ARCH_EAGLE:
+            {
+                llm = std::make_unique<llm_build_eagle>(*this, params, gf);
+            } break;
         default:
             GGML_ABORT("fatal error");
     }
@@ -14227,6 +14427,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_RWKV7:
         case LLM_ARCH_ARWKV7:
         case LLM_ARCH_WAVTOKENIZER_DEC:
+        case LLM_ARCH_EAGLE:
             return LLAMA_ROPE_TYPE_NONE;
 
         // use what we call a normal RoPE, operating on pairs of consecutive head values
diff --git a/src/llama-model.h b/src/llama-model.h
index 06e6c6879..12469254e 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -338,6 +338,8 @@ struct llama_model {
     struct ggml_tensor * pos_embd   = nullptr;
     struct ggml_tensor * tok_norm   = nullptr;
     struct ggml_tensor * tok_norm_b = nullptr;
+    struct ggml_tensor * embd_fc     = nullptr;
+    struct ggml_tensor * embd_fc_b   = nullptr;
 
     struct ggml_tensor * output_norm     = nullptr;
     struct ggml_tensor * output_norm_b   = nullptr;