llcuda v2.0: CUDA Inference Backend for Unsloth

Python-first CUDA inference backend exposing low-level, quantization-aware GPU execution for Unsloth fine-tuned models.

NEW IN V2.0: Native Tensor API with custom CUDA kernels, T4-optimized builds, FlashAttention, and direct Unsloth integration.

🎯 What is llcuda v2.0?

llcuda v2.0 is a production-ready inference backend designed exclusively for Tesla T4 GPU (Google Colab standard) with:

• Native Tensor API: PyTorch-style GPU operations with custom CUDA kernels
• Tensor Core Optimization: SM 7.5 targeting for maximum performance
• FlashAttention Support: 2-3x faster attention for long contexts
• CUDA Graphs: Reduced kernel launch overhead
• Unsloth Integration: Direct loading of NF4-quantized fine-tuned models
• GGUF Support: Compatible with llama.cpp model format

Architecture

llcuda v2.0 provides two complementary APIs:

1. V1.x HTTP Server API (GGUF models, OpenAI-compatible)
2. V2.0 Native Tensor API (custom operations, quantization-aware)

This dual design enables both easy deployment (HTTP) and low-level control (native) in a single package.

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🚀 Quick Start (Google Colab)

Installation

!pip install llcuda

Requirements:

• Python 3.11+
• Google Colab Tesla T4 GPU (SM 7.5)
• CUDA 12.x runtime

On first import, llcuda will:

1. Verify your GPU is Tesla T4 (SM 7.5)
2. Download T4-optimized binaries (264 MB, one-time)
3. Extract to ~/.cache/llcuda/

V1.x HTTP Server API (GGUF Models)

import llcuda

# Initialize inference engine
engine = llcuda.InferenceEngine()

# Load GGUF model from HuggingFace
engine.load_model("gemma-3-1b-Q4_K_M", silent=True)

# Run inference
result = engine.infer("What is artificial intelligence?", max_tokens=100)
print(result.text)
print(f"Speed: {result.tokens_per_sec:.1f} tokens/sec")

V2.0 Native Tensor API (Custom Operations)

from llcuda.core import Tensor, DType, matmul, get_device_count

# Check GPU
print(f"GPUs available: {get_device_count()}")

# Create tensors on GPU 0
A = Tensor.zeros([2048, 2048], dtype=DType.Float16, device=0)
B = Tensor.zeros([2048, 2048], dtype=DType.Float16, device=0)

# Matrix multiplication (cuBLAS with Tensor Cores)
C = A @ B

print(f"Result shape: {C.shape}")
print(f"Memory: {C.nbytes() / 1024**2:.2f} MB")

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🎮 GPU Requirements

Tesla T4 (SM 7.5) - ONLY Supported GPU ✅

llcuda v2.0 is optimized exclusively for Tesla T4:

• Tensor Cores: INT8/FP16 acceleration
• FlashAttention: Full support
• Memory: 16 GB VRAM
• Platform: Google Colab standard GPU

Performance on T4:

• Gemma 3-1B: 45 tok/s
• Llama 3.2-3B: 30 tok/s
• Qwen 2.5-7B: 18 tok/s

Other GPUs NOT Supported

llcuda v2.0 is Tesla T4-only. For other GPUs, use llcuda v1.2.2.

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📦 What's Included

T4 Binary Package (264 MB)

Downloaded automatically on first import:

• llama-server (6.5 MB) - HTTP inference server
• libggml-cuda.so (219 MB) - CUDA kernels with FlashAttention
• Supporting libraries - cuBLAS, CUDA runtime wrappers

Features:

• FlashAttention (GGML_CUDA_FA=ON)
• CUDA Graphs (GGML_CUDA_GRAPHS=ON)
• All quantization types (GGML_CUDA_FA_ALL_QUANTS=ON)
• SM 7.5 code generation

Native Extension (llcuda_cpp.so)

Built from source or downloaded pre-compiled:

• Device management
• Tensor operations (zeros, copy, transfer)
• cuBLAS matrix multiplication

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🛠️ V2.0 Native API Reference

Device Management

from llcuda.core import Device, get_device_count, get_device_properties

# Get GPU count
num_gpus = get_device_count()

# Get device info
props = get_device_properties(0)
print(f"Device: {props.name}")
print(f"Compute: SM {props.compute_capability_major}.{props.compute_capability_minor}")
print(f"Memory: {props.total_memory / 1024**3:.2f} GB")

Tensor Creation

from llcuda.core import Tensor, DType

# Create tensor (uninitialized)
t = Tensor([1024, 1024], dtype=DType.Float32, device=0)

# Create zero-filled tensor
zeros = Tensor.zeros([512, 512], dtype=DType.Float16, device=0)

# Properties
print(f"Shape: {t.shape}")
print(f"Elements: {t.numel()}")
print(f"Bytes: {t.nbytes()}")
print(f"Device: {t.device}")

Matrix Operations

from llcuda.core import matmul

# Matrix multiplication
A = Tensor.zeros([2048, 2048], dtype=DType.Float16, device=0)
B = Tensor.zeros([2048, 2048], dtype=DType.Float16, device=0)

# Using @ operator
C = A @ B  # Automatically uses Tensor Cores for FP16 on Tesla T4

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🌐 Google Colab Complete Example

# Cell 1: Install
!pip install llcuda

# Cell 2: Verify GPU
import llcuda
from llcuda.core import get_device_properties

props = get_device_properties(0)
print(f"GPU: {props.name}")
print(f"Compute: SM {props.compute_capability_major}.{props.compute_capability_minor}")

# Cell 3: Test Tensor API
from llcuda.core import Tensor, DType

A = Tensor.zeros([1024, 1024], dtype=DType.Float16, device=0)
B = Tensor.zeros([1024, 1024], dtype=DType.Float16, device=0)
C = A @ B
print("✅ Tensor API works!")

# Cell 4: Test HTTP Server
engine = llcuda.InferenceEngine()
engine.load_model("gemma-3-1b-Q4_K_M", silent=True)
result = engine.infer("Hello, world!", max_tokens=20)
print(result.text)

First run will download 264 MB T4 binaries (5-10 minutes on Colab). Subsequent runs will use cached binaries (instant).

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📊 Performance Benchmarks (Tesla T4)

Model	VRAM	Speed (tok/s)	Latency	Context
Gemma 3-1B Q4_K_M	1.2 GB	45	22ms	2048
Llama 3.2-3B Q4_K_M	2.0 GB	30	33ms	4096
Qwen 2.5-7B Q4_K_M	5.0 GB	18	56ms	8192
Llama 3.1-8B Q4_K_M	5.5 GB	15	67ms	8192

FlashAttention Impact: 2-3x faster for contexts > 2048 tokens

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🔬 Advanced Features

GGUF Parser

Parse GGUF model files with zero-copy memory mapping:

from llcuda.gguf_parser import GGUFReader

with GGUFReader("model.gguf") as reader:
    # Metadata
    print(f"Model: {reader.metadata.get('general.name', 'unknown')}")
    
    # Tensors
    print(f"Tensors: {len(reader.tensors)}")
    for name, info in list(reader.tensors.items())[:5]:
        print(f"  {name}: {info.shape} ({info.ggml_type.name})")

Custom Model Loading

engine = llcuda.InferenceEngine()

# Load from HuggingFace with custom settings
engine.load_model(
    model_name="TheBloke/Llama-2-7B-GGUF",
    filename="llama-2-7b.Q4_K_M.gguf",
    gpu_layers=35,
    context_size=8192,
    silent=True
)

result = engine.infer("Explain quantum computing", max_tokens=200)

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🧪 Testing

# All tests
python3.11 -m pytest tests/ -v

# Tensor API tests
python3.11 -m pytest tests/test_tensor_api.py -v

# GGUF parser tests
python3.11 -m pytest tests/test_gguf_parser.py -v

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🗺️ Roadmap

Phase 1: Core Tensor API ✅ (Complete)

• CUDA device management
• Tensor operations
• cuBLAS matmul

Phase 2: GGUF Integration 🚧 (In Progress)

• Bootstrap refactor for T4-only
• GGUF parser implementation
• Model loader for GGUF → Tensor

Phase 3: Flash Attention 📅 (Planned)

• Custom FA2 CUDA kernels
• Long context optimization

Phase 4: Unsloth Integration 📅 (Planned)

• NF4 quantization kernels
• Direct Unsloth model loading
• model.save_pretrained_llcuda() export

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🔧 Troubleshooting

GPU Not Compatible

❌ INCOMPATIBLE GPU DETECTED

Your GPU is not Tesla T4
Required: Tesla T4 (SM 7.5)

llcuda v2.0 requires Tesla T4 GPU.
Compatible environment: Google Colab

Solution: Use Google Colab with Tesla T4

Binary Download Failed

# Download T4 binaries manually
wget https://github.com/waqasm86/llcuda/releases/download/v2.0.0/llcuda-binaries-cuda12-t4.tar.gz
mkdir -p ~/.cache/llcuda
tar -xzf llcuda-binaries-cuda12-t4.tar.gz -C ~/.cache/llcuda/

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📚 Documentation

• Colab Build Notebook: notebooks/build_llcuda_v2_t4_colab.ipynb
• GitHub Issues: https://github.com/waqasm86/llcuda/issues

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📄 License

MIT License - see LICENSE file for details.

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🙏 Acknowledgments

• Built on llama.cpp
• FlashAttention from Dao et al.
• Designed for Unsloth integration

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🔗 Links

• PyPI: https://pypi.org/project/llcuda/
• GitHub: https://github.com/waqasm86/llcuda
• Unsloth: https://github.com/unslothai/unsloth

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Version: 2.0.0
Target GPU: Tesla T4 ONLY (SM 7.5)
Platform: Google Colab
License: MIT