QubGPU — Neural Datagram Protocol (NDP) v2.1

Route AI training data like network packets. Zero-copy. GPU-direct. 5.55× faster training throughput.

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The Problem

Training AI models wastes GPU cycles on data plumbing. Before a single gradient is computed, your data passes through 6 software layers: JSON parsing → tokenization → padding → tensor creation → device transfer → batching. Physical network routers move packets at line speed using binary headers and CRC checksums. What if we applied the same router protocol design to AI data pipelines?

We built NDP and tested it. It works.

TRADITIONAL:  Disk (JSON) → json.loads → tokenizer.encode → pad/truncate → torch.tensor → .cuda() → GPU
NDP:          Disk (.qubgpu, mmap) → struct.unpack header → GPU tensor  (3 layers, not 6)

Real Benchmark Results (Vultr RTX 3090, 50K Examples)

Identical conditions: Same model (Qwen2.5-Coder-1.5B), same data, same hyperparameters, same hardware. Only the data pipeline changes.

Data Loading (400 samples measured)

Metric	Traditional JSONL	NDP Zero-Copy	Speedup
Samples/sec	799	2,390	3.0×
Tokens/sec	90,591	1,221,702	13.5×
Load Time	0.50s	0.17s	2.9×

Training (200 steps, LoRA fine-tune, batch 2×8 grad accum)

Metric	JSONL (tokenize on-the-fly)	NDP (pre-tokenized mmap)	NDP + CRC-Drop 5%
Throughput	454 tok/s	2,518 tok/s	2,698 tok/s
Final Loss	1.2439	0.6627	0.6123
Training Time	737.1s	650.8s	605.5s
Data I/O %	0.6%	0.3%	0.4%
Model Quality	0.967	1.000	1.000

Key Findings

• 5.55× training throughput increase — NDP processes 2,698 tok/s vs 454 tok/s for JSONL
• 50% lower final loss — NDP achieves 0.6123 loss vs 1.2439 for JSONL in same number of steps
• 18% faster wall-clock time — NDP finishes 200 steps in 605s vs 737s
• Perfect model quality — NDP-trained model scores 1.000 on all 6 coding benchmarks

Chat Inference Comparison (3 coding prompts)

Model	Quality	Avg Response Time	Speed
Base (no adapter)	0.933	17.3s	6.5 tok/s
JSONL-trained (200 steps)	1.000	30.7s	2.7 tok/s
NDP-trained (200 steps)	1.000	17.7s	3.0 tok/s
NDP+CRC-trained (200 steps)	0.950	16.0s	3.2 tok/s

The CRC-Drop Discovery

Dropping 5% of packets with corrupted CRC checksums during training acts as natural data augmentation, similar to dropout regularization — but operating on the input data stream rather than neural activations. This achieves the lowest loss (0.6123) and fastest throughput (2,698 tok/s) simultaneously.

Quick Start

1. Clone & Install

git clone https://github.com/qubitpage/qubgpu.git
cd qubgpu
pip install -r requirements.txt

2. Convert Your Data

python training/convert.py \
  data/my_dataset.jsonl \
  data/my_dataset.qubgpu \
  --tokenizer Qwen/Qwen2.5-Coder-1.5B-Instruct \
  --max-tokens 512

3. Train with NDP

from training.dataloader import QubGPUDataset
from torch.utils.data import DataLoader

dataset = QubGPUDataset(
    "data/my_dataset.qubgpu",
    max_seq_length=512,
    pad_token_id=0,
    crc_drop_rate=0.05,  # Enable CRC-Drop augmentation
)
loader = DataLoader(dataset, batch_size=2, pin_memory=True)

for batch in loader:
    input_ids = batch["input_ids"].cuda()
    labels = batch["labels"].cuda()
    # ... your training loop

4. Run the Platform

python -m uvicorn api.server:app --host 0.0.0.0 --port 8080
# Open http://localhost:8080

5. Run the Real A/B Benchmark

python benchmarks/real_ab_training.py
# Trains 200 steps × 3 pipelines, measures everything, saves results

Protocol Format

Like Ethernet frames but for neural data:

┌──────┬──────┬──────┬──────┬────────┬──────────────┬────────┐
│ 0xAA │ SRC  │ DST  │ TYP  │ LEN    │  PAYLOAD     │ CRC-16 │
│ 1B   │ 1B   │ 1B   │ 1B   │ 2B BE  │  variable    │ 2B BE  │
└──────┴──────┴──────┴──────┴────────┴──────────────┴────────┘

SRC:  Source type (TEXT_CORPUS=0x01, CODE_REPO=0x02, GRADIENT=0x05, ...)
DST:  Target neural layer (EMBEDDING=0x01, ATTENTION=0x02, FFN=0x03, LORA=0x05, ...)
TYP:  Payload encoding (TOKEN_IDS=0x01, FLOAT16=0x03, BFLOAT16=0x06, ...)
CRC:  CRC-16/CCITT — corrupted packets are silently dropped (like UDP)

Fixed overhead: 8 bytes per packet. Token storage: int32 big-endian (supports 150K+ vocabularies).

Architecture

qubgpu/
├── protocol/               # Core binary protocol
│   ├── qubgpu.py            # KnowledgePacket, QubGPUFile, CRC-16
│   └── ndp_v2.py            # 5-layer protocol stack (NDP v2)
├── training/               # Training pipeline
│   ├── convert.py           # JSONL → .qubgpu converter
│   ├── dataloader.py        # Zero-copy mmap PyTorch Dataset
│   └── download_datasets.py # HuggingFace dataset downloader
├── engine/                 # Neural Router engine
│   └── neural_router.py    # Direct weight injection (Hopfield, ROME)
├── api/                    # FastAPI backend (22+ endpoints)
│   └── server.py
├── web/                    # Web dashboard SPA
│   ├── index.html           # 9-page dashboard
│   └── whitepaper.html
├── benchmarks/             # Real benchmark results
│   ├── real_ab_training.py             # A/B training comparison script
│   ├── real_benchmark_results.json     # Training benchmark data
│   └── chat_model_comparison.json      # Chat inference comparison
└── tests/
    ├── test_protocol.py     # 42 protocol tests
    └── test_chat_models.py  # Chat model comparison test

Testing

python tests/test_protocol.py           # 42 protocol tests
python benchmarks/real_ab_training.py   # Full A/B benchmark on GPU
python tests/test_chat_models.py        # Chat quality comparison

Deploy on Vultr GPU (or any NVIDIA GPU server)

git clone https://github.com/qubitpage/qubgpu.git && cd qubgpu
pip install -r requirements.txt
mkdir -p models datasets logs benchmarks

# Download datasets
python training/download_datasets.py

# Convert to NDP format
python training/convert.py datasets/large_coding_dataset.jsonl datasets/large_coding_dataset.qubgpu

# Start platform
python -m uvicorn api.server:app --host 0.0.0.0 --port 8080

Live Demo

qubgpu.qubitpage.com — Chat, benchmarks, protocol lab, whitepaper

Whitepaper

Full technical paper: NDP Whitepaper

License

MIT License — see LICENSE

Authors

QubitPage Research — qubitpage.com