diff --git a/README.md b/README.md
index d5d1750..d259e82 100644
--- a/README.md
+++ b/README.md
@@ -129,6 +129,12 @@ make sure you have `uv` installed.
    ```
    This will install additional dependencies for development and building documentation.
    The (experimental) auto-kernel application is also under a separate group for now. 
+4. *(Optional)* Intall Support for MLX optimised LLMs:
+    ```bash
+    uv sync --group group_name .... --extra apple-silicon --prerelease=allow
+    ```
+    Select all the groups required, and append it with `--extra apple-silicon --prerelease=allow`, to install
+    libraries that enable MLX Optimised LLMs to be used in iohblade.
 
 ## 💻 Quick Start
 
diff --git a/docs/Installation.rst b/docs/Installation.rst
index 1d81bf1..73ace46 100644
--- a/docs/Installation.rst
+++ b/docs/Installation.rst
@@ -30,4 +30,10 @@ You can also install the package from source using **uv** (0.7.9).
 
    .. code-block:: bash
 
-      uv install --group dev --group docs
+      uv sync --group dev --group docs
+
+4. (Optional) Install support for running MLX optimised LLMs locally on apple silicon machine:
+
+   .. code-block:: bash
+
+      uv sync --group dev --group docs --group --extra apple_silicon --prerelease=allow
diff --git a/iohblade/benchmarks/geometry/heilbronn_triangle.py b/iohblade/benchmarks/geometry/heilbronn_triangle.py
index 3300820..45f2bca 100644
--- a/iohblade/benchmarks/geometry/heilbronn_triangle.py
+++ b/iohblade/benchmarks/geometry/heilbronn_triangle.py
@@ -56,8 +56,7 @@ def __init__(
   - (None, points) where points is ndarray (n,2) interpreted inside a default unit-area triangle, or
   - (triangle, points): with triangle shape (3,2), both of which we rescale similarly as to have area of triangle = 1.
     - Upon scaling points must lie inside the tringle, within the given tolerance.
-- The solution is scored as minimum triangle area formed by picking 3 of the n points.
-- The optimisation goal is to maximise the score.
+- The optimisation goal is to maximise the area of the smallest triangle, formed by picking 3 of the n points.
 """
         self.task_prompt += (
             f"- The tolerence of the solution is set to {self.tolerance}"
@@ -119,12 +118,15 @@ def evaluate(self, solution, explogger=None):
             safe = prepare_namespace(code, self.dependencies)
             local_ns = {}
             exec(code, safe, local_ns)
-            local_ns = clean_local_namespace(local_ns, safe)
-            cls = next(v for v in local_ns.values() if isinstance(v, type))
-            try:
-                triangle, points = cls(self.n_points)()
-            except:
+            cls = local_ns[solution.name]
+            if self.best_solution is None:
                 triangle, points = cls(self.n_points)()
+            else:
+                triangle, points = cls(
+                    self.n_points,
+                    best_known_configuration=self.best_solution,
+                    in_triangle=self.triangle_best_solution,
+                )()
         except Exception as e:
             # tb = e.__traceback__
             solution.set_scores(
@@ -154,7 +156,11 @@ def evaluate(self, solution, explogger=None):
                 f"Area of Smallest Triangle={min_area:.6g}, best known={self.best_known}",
             )
         except Exception as e:
-            solution.set_scores(float("-inf"), f"calc-error {e}", "calc-failed")
+            solution.set_scores(
+                float("-inf"),
+                f"calc-error {e}.",
+                f"Values Returned: Triangle {triangle}, points: {points}",
+            )
         return solution
 
     def test(self, solution):
diff --git a/iohblade/llm.py b/iohblade/llm.py
index d94fcb8..ca18596 100644
--- a/iohblade/llm.py
+++ b/iohblade/llm.py
@@ -13,6 +13,16 @@
 import anthropic
 import ollama
 import openai
+try:
+    import lmstudio as lms                      # Platform dependent dependency.
+except:
+    lms = None
+try:
+    from mlx_lm import load, generate           # Platform dependent dependency.
+except:
+    load = None
+    generate = None
+
 from google import genai
 from tokencost import (
     calculate_completion_cost,
@@ -681,6 +691,146 @@ def __deepcopy__(self, memo):
         return new
 
 
+class LMStudio_LLM(LLM):
+    """A manager for running MLX-Optimised LLM locally."""
+
+    def __init__(self, model, config=None, **kwargs):
+        """
+        Initialises the LMStudio LLM inteface.
+
+        :param model: Name of the model, to be initialised for interaction.
+        :param config: Configuration to be set for LLM chat.
+        :param kwargs: Keyed arguements for setting up the LLM chat.
+        """
+        super().__init__(api_key="", model=model, **kwargs)
+        self.llm = lms.llm(model)
+        self.config = config
+
+    def _query(self, session: list[dict[str, str]], max_tries: int = 5) -> str:
+        """
+        Query stub for LMStudio class.
+
+        ## Parameters
+        `session: list[dict[str, str]]`: A session message is a list of {'role' : 'user'|'system', 'content': 'content'} data, use to make LLM request.
+        `max_tries: int`: A max count for the number of tries, to get a response.
+        """
+        request = session[-1]["content"]
+        for _ in range(max_tries):
+            try:
+                if self.config is not None:
+                    response = self.llm.respond(request, config=self.config)
+                else:
+                    response = self.llm.respond(request)
+                response = re.sub(  # Remove thinking section, if avaiable.
+                    r"<think>.*?</think>", "", str(response), flags=re.DOTALL
+                )
+                return response
+            except:
+                pass
+        return ""
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state.pop("llm", None)
+        return state
+
+    def __setstate__(self, state):
+        self.__dict__.update(state)
+        self.llm = lms.llm(self.model)
+
+    def __deepcopy__(self, memo):
+        cls = self.__class__
+        new = cls.__new__(cls)
+        memo[id(self)] = new
+        for k, v in self.__dict__.items():
+            if k == "llm":
+                continue
+            setattr(new, k, copy.deepcopy(v, memo))
+        new.llm = self.llm
+        return new
+
+
+class MLX_LM_LLM(LLM):
+    """An mlx_lm implementation for running large LLMs locally."""
+
+    def __init__(self, model, config=None, max_tokens: int = 12000, **kwargs):
+        """
+        Initialises the LMStudio LLM inteface.
+
+        :param model: Name of the model, to be initialised for interaction.
+        :param config: Configuration to be set for LLM chat.
+        :param max_tokens: Maximun number of tokens to be generated for a request.
+        :param kwargs: Keyed arguements for setting up the LLM chat.
+        """
+        super().__init__(api_key="", model=model, **kwargs)
+        if config is not None:
+            llm, tokenizer = load(model, model_config=config)
+        else:
+            llm, tokenizer = load(model)
+        self.llm = llm
+        self.tokenizer = tokenizer
+
+        self.config = config
+        self.max_tokens = max_tokens
+
+    def __getstate__(self) -> object:
+        state = self.__dict__
+        state.pop("tokenizer", None)
+        state.pop("llm", None)
+        return state
+
+    def __setstate__(self, state):
+        self.__dict__.update(state)
+        if self.config is None:
+            llm, tokenizer = load(self.model)
+        else:
+            llm, tokenizer = load(self.model, model_config=self.config)
+        self.llm = llm
+        self.tokenizer = tokenizer
+
+    def __deepcopy__(self, memo):
+        cls = self.__class__
+        new = cls.__new__(cls)
+        memo[id(self)] = new
+        for k, v in self.__dict__.items():
+            if k in ["llm", "tokenizer"]:
+                continue
+            setattr(new, k, copy.deepcopy(v, memo))
+        new.llm = self.llm  # <- reference symantics copy for massive object `llm`.
+        new.tokenizer = self.tokenizer
+        return new
+
+    def _query(
+        self, session: list, max_tries: int = 5, add_generation_prompt: bool = False
+    ):
+        """
+        Query stub for LMStudio class.
+
+        ## Parameters
+        `session: list[dict[str, str]]`: A session message is a list of {'role' : 'user'|'system', 'content': 'content'} data, use to make LLM request.
+        `max_tries: int`: A max count for the number of tries, to get a response.
+        `add_generation_prompt: bool`: MLX_LM come with an option to add_generation_prompt to optimise prompts.
+        """
+        prompt = self.tokenizer.apply_chat_template(
+            session, add_generation_prompt=add_generation_prompt
+        )
+        for _ in range(max_tries):
+            try:
+                response = generate(
+                    self.llm,
+                    self.tokenizer,
+                    prompt,
+                    max_tokens=self.max_tokens,  # Disable limit on token count.
+                )
+                response = re.sub(  # Remove thinking section, if avaiable.
+                    r"<think>.*?</think>", "", str(response), flags=re.DOTALL
+                )
+                return response
+            except:
+                pass
+        return ""
+
+
 class Dummy_LLM(LLM):
     def __init__(self, model="DUMMY", **kwargs):
         """
diff --git a/pyproject.toml b/pyproject.toml
index 529ee4e..9098efc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -63,6 +63,10 @@ methods = [
     "eoh",
     "reevo",
 ]
+apple-silicon = [
+    "lmstudio>=1.5.0,<2; platform_system == 'Darwin' and platform_machine == 'arm64'",
+    "mlx_lm>=0.30.2,<1; platform_system == 'Darwin' and platform_machine == 'arm64'",
+]
 kerneltuner = [
     "kernel-tuner",
     "autotuning_methodology",
diff --git a/run_benchmarks/heilbronn_triangle.py b/run_benchmarks/heilbronn_triangle.py
index a903761..ab0508f 100644
--- a/run_benchmarks/heilbronn_triangle.py
+++ b/run_benchmarks/heilbronn_triangle.py
@@ -1,7 +1,7 @@
 from os import environ
 
 from iohblade.experiment import Experiment
-from iohblade.llm import Gemini_LLM, Ollama_LLM
+from iohblade.llm import Gemini_LLM
 from iohblade.methods import LLaMEA
 from iohblade.loggers import ExperimentLogger
 
@@ -13,11 +13,10 @@
 
     api_key = environ.get("GOOGLE_API_KEY")
 
-    ollama_llm = Ollama_LLM()
     gemini_llm = Gemini_LLM(api_key=api_key)
 
     # Helibronn n11 benchmark.
-    heilbronn_triangle = get_heilbronn_triangle_problems(True)[0]
+    heilbronn_triangle = get_heilbronn_triangle_problems(False)[0]
 
     methods = []
     for llm in [gemini_llm]:
diff --git a/tests/unit/test_llm.py b/tests/unit/test_llm.py
index 2db1544..55b8c8c 100644
--- a/tests/unit/test_llm.py
+++ b/tests/unit/test_llm.py
@@ -16,6 +16,8 @@
     OpenAI_LLM,
     DeepSeek_LLM,
     Dummy_LLM,
+    LMStudio_LLM,
+    MLX_LM_LLM,
 )
 
 
@@ -519,3 +521,206 @@ def test_ollama_deep_copies(monkeypatch):
     )
 
     mocked_chat2.assert_not_called()
+
+@pytest.fixture
+def fake_mlx_load(monkeypatch):
+    llm = object()
+    tokenizer = object()
+    calls = []
+
+    def fake_load(model, model_config=None):
+        calls.append((model, model_config))
+        print(calls)
+        return llm, tokenizer
+
+    monkeypatch.setattr(
+        "iohblade.llm.load",
+        fake_load
+    )
+
+    return {
+        "llm": llm,
+        "tokenizer": tokenizer,
+        "calls": calls,
+    }
+
+class AlwaysFailLLM:
+    def __init__(self):
+        self.calls = 0
+
+    def generate(self, *args, **kwargs):
+        self.calls += 1
+        raise RuntimeError("LLM failure")
+
+class FakeTokenier:
+    def apply_chat_template(self, session, add_generation_prompt:bool):
+        return session
+
+
+def test_query_fails_only_after_max_tries(fake_mlx_load, monkeypatch):
+
+    mock_tokenizer = FakeTokenier()
+    failing_llm = AlwaysFailLLM()
+    # Replace the llm returned by load()
+    fake_mlx_load["llm"] = failing_llm
+
+    llm = MLX_LM_LLM(
+        model="dummy-model",
+        max_tokens=10,
+    )
+
+    # monkeypatch the internal llm and dependencies
+    monkeypatch.setattr('iohblade.llm.generate', failing_llm.generate)
+    llm.tokenizer = mock_tokenizer
+
+    result = llm._query([{"client": "hello"}], max_tries=3, add_generation_prompt=True)
+
+    assert result == ""
+    assert failing_llm.calls == 3
+
+def test_deepcopy_shares_llm_and_tokenizer(fake_mlx_load):
+    llm = MLX_LM_LLM(
+        model="dummy-model",
+        config={"foo": "bar"},
+        max_tokens=123,
+    )
+
+    llm_copy = copy.deepcopy(llm)
+
+    # Shared heavy objects
+    assert llm.llm is llm_copy.llm
+    assert llm.tokenizer is llm_copy.tokenizer
+
+    # But normal attributes are copied
+    assert llm is not llm_copy
+    assert llm.config == llm_copy.config
+    assert llm.max_tokens == llm_copy.max_tokens
+
+def test_getstate_setstate_restores_llm_and_tokenizer(fake_mlx_load):
+    llm = MLX_LM_LLM(
+        model="dummy-model",
+        config={"alpha": 0.5},
+        max_tokens=42,
+    )
+
+    # Serialize + deserialize
+    blob = pickle.dumps(llm)
+    restored = pickle.loads(blob)
+
+    # load() must be called again
+    assert len(fake_mlx_load["calls"]) == 2
+
+    # Config and parameters restored
+    assert restored.model == llm.model
+    assert restored.config == llm.config
+    assert restored.max_tokens == llm.max_tokens
+
+    # Heavy objects exist
+    assert hasattr(restored, "llm")
+    assert hasattr(restored, "tokenizer")
+
+    assert restored.llm is fake_mlx_load["llm"]
+    assert restored.tokenizer is fake_mlx_load["tokenizer"]
+
+class AlwaysFailLMStudio:
+    def __init__(self):
+        self.calls = 0
+
+    def respond(self, *args, **kwargs):
+        self.calls += 1
+        raise RuntimeError("LMStudio failure")
+
+
+class AlwaysSucceedLMStudio:
+    def __init__(self, response="OK"):
+        self.calls = 0
+        self.response = response
+
+    def respond(self, *args, **kwargs):
+        self.calls += 1
+        return self.response
+
+@pytest.fixture
+def fake_lms_llm(monkeypatch):
+    calls = []
+
+    def factory(model):
+        client = AlwaysFailLMStudio()
+        calls.append((model, client))
+        return client
+
+    monkeypatch.setattr(
+        "iohblade.llm.lms.llm",
+        factory,
+    )
+
+    return {
+        "calls": calls,
+    }
+
+def test_lms_query_fails_only_after_max_tries(fake_lms_llm):
+    llm = LMStudio_LLM(
+        model="dummy-model",
+        config=None,
+    )
+
+    # Replace llm with failing instance explicitly
+    failing = AlwaysFailLMStudio()
+    llm.llm = failing
+
+    result = llm._query(
+        [{"role": "user", "content": "hello"}],
+        max_tries=3,
+    )
+
+    assert result == ""
+    assert failing.calls == 3
+
+def test_lms_query_success_and_think_stripped(fake_lms_llm):
+    llm = LMStudio_LLM(model="dummy", config=None)
+
+    llm.llm = AlwaysSucceedLMStudio(
+        response="<think>This client dare only greet me??? Fine, I'll be kind for now, *hmph*.</think>Hello!"
+    )
+
+    result = llm._query(
+        [{"role": "user", "content": "hi"}],
+        max_tries=2,
+    )
+
+    assert result == "Hello!"
+
+def test_lms_deepcopy_shares_llm_instance(fake_lms_llm):
+    llm = LMStudio_LLM(
+        model="dummy-model",
+        config={"temperature": 0.2},
+    )
+
+    llm_copy = copy.deepcopy(llm)
+
+    # Shared heavy object
+    assert llm.llm is llm_copy.llm
+
+    # Independent wrapper
+    assert llm is not llm_copy
+    assert llm.model == llm_copy.model
+    assert llm.config == llm_copy.config
+
+def test_lms_getstate_setstate_reloads_llm(fake_lms_llm):
+    llm = LMStudio_LLM(
+        model="dummy-model",
+        config={"top_p": 0.9},
+    )
+
+    # One load from __init__
+    assert len(fake_lms_llm["calls"]) == 1
+
+    blob = pickle.dumps(llm)
+    restored = pickle.loads(blob)
+
+    # Second load from __setstate__
+    assert len(fake_lms_llm["calls"]) == 2
+
+    assert restored.model == llm.model
+    assert restored.config == llm.config
+    assert hasattr(restored, "llm")
\ No newline at end of file
diff --git a/uv.lock b/uv.lock
index 49c1e25..ac1fde5 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.11, <4"
 resolution-markers = [
     "python_full_version >= '3.14'",
@@ -8,6 +8,9 @@ resolution-markers = [
     "python_full_version < '3.12'",
 ]
 
+[options]
+prerelease-mode = "allow"
+
 [[package]]
 name = "aiofiles"
 version = "24.1.0"
@@ -371,6 +374,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f6/22/91616fe707a5c5510de2cac9b046a30defe7007ba8a0c04f9c08f27df312/audioop_lts-0.2.2-cp314-cp314t-win_arm64.whl", hash = "sha256:b492c3b040153e68b9fdaff5913305aaaba5bb433d8a7f73d5cf6a64ed3cc1dd", size = 25206, upload-time = "2025-08-05T16:43:16.444Z" },
 ]
 
+[[package]]
+name = "authlib"
+version = "1.6.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cryptography" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/bb/9b/b1661026ff24bc641b76b78c5222d614776b0c085bcfdac9bd15a1cb4b35/authlib-1.6.6.tar.gz", hash = "sha256:45770e8e056d0f283451d9996fbb59b70d45722b45d854d58f32878d0a40c38e", size = 164894, upload-time = "2025-12-12T08:01:41.464Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/54/51/321e821856452f7386c4e9df866f196720b1ad0c5ea1623ea7399969ae3b/authlib-1.6.6-py2.py3-none-any.whl", hash = "sha256:7d9e9bc535c13974313a87f53e8430eb6ea3d1cf6ae4f6efcd793f2e949143fd", size = 244005, upload-time = "2025-12-12T08:01:40.209Z" },
+]
+
 [[package]]
 name = "autotuning-methodology"
 version = "1.2.0"
@@ -714,6 +729,9 @@ dependencies = [
     { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/0e/bc/2a53b259ec14101a42690992ea8fc2456edcacc73ede62f803dd3c4b0bdc/configspace-1.2.1.tar.gz", hash = "sha256:2fc1c4477f7839d38b53d8813c2581967df57ccd7d8b4b506e879a8835cb964c", size = 130983, upload-time = "2024-11-21T10:18:39.79Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f1/d0/e899691a9cdec2b866fbf7c39747195b9402e64260632f415c851ae8e503/configspace-1.2.1-py3-none-any.whl", hash = "sha256:78d3cef1bdb447bf25c2433891580bf6c2b468d47b729356a8646d5b7cd9eb5a", size = 117557, upload-time = "2025-12-16T13:10:39.616Z" },
+]
 
 [[package]]
 name = "contourpy"
@@ -1486,7 +1504,7 @@ wheels = [
 
 [[package]]
 name = "gradio"
-version = "5.44.1"
+version = "6.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiofiles" },
@@ -1510,24 +1528,28 @@ dependencies = [
     { name = "pydub" },
     { name = "python-multipart" },
     { name = "pyyaml" },
-    { name = "ruff", marker = "sys_platform != 'emscripten'" },
     { name = "safehttpx" },
     { name = "semantic-version" },
-    { name = "starlette", marker = "sys_platform != 'emscripten'" },
+    { name = "starlette" },
     { name = "tomlkit" },
-    { name = "typer", marker = "sys_platform != 'emscripten'" },
+    { name = "typer" },
     { name = "typing-extensions" },
-    { name = "urllib3", marker = "sys_platform == 'emscripten'" },
-    { name = "uvicorn", marker = "sys_platform != 'emscripten'" },
+    { name = "uvicorn" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/18/ea/a9b3ded7913ca22eda86a56ddae8f7523963eb58ae60606015c9466f1950/gradio-5.44.1.tar.gz", hash = "sha256:8527837aa6de4b0d2398dab11baac8e3eac9da69140ed0da6efc6ac497fa818d", size = 72141502, upload-time = "2025-08-29T00:09:45.678Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/75/53/b11072617ee422b1fa1c10d72fef2bae862e4b217fe95761bc6f41d85e50/gradio-6.3.0.tar.gz", hash = "sha256:fef669ff0515d6cb85503a11f78740d82d7f10e3c243777a8603831ea9633217", size = 38507982, upload-time = "2026-01-10T00:03:44.007Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e1/2f/8c2f8822217061888b68a8610c556d053983fe8759273c9a6fcf3f2fabca/gradio-5.44.1-py3-none-any.whl", hash = "sha256:cb22dd519c3bb2f8c7960cdcc23ca3b869511c85e320f486d7aef6e3627f97b9", size = 60195018, upload-time = "2025-08-29T00:09:40.879Z" },
+    { url = "https://files.pythonhosted.org/packages/05/b8/dde879605be8e9e10fd3355fdbba0c3f5d2c062c0a558abadd58cf0acc80/gradio-6.3.0-py3-none-any.whl", hash = "sha256:f33d8a2b6ca240a85c502ac83f82d30a6a8c5fe6e59bfe6a2ad740ce2c677a3d", size = 22985655, upload-time = "2026-01-10T00:03:39.944Z" },
+]
+
+[package.optional-dependencies]
+oauth = [
+    { name = "authlib" },
+    { name = "itsdangerous" },
 ]
 
 [[package]]
 name = "gradio-client"
-version = "1.12.1"
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