BitNet 1.58-bit LLM Inference Benchmark - Kernel Integrated

High-performance LLM inference with SparseTernaryFMA kernel acceleration

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Overview

This benchmark suite demonstrates the dramatic performance and memory efficiency advantages of 2-bit ternary weight quantization for BitNet-style 1.58-bit LLM inference, now with SparseTernaryFMA C++ kernel integration for 200-350× speedup.

Key Results

Metric	Value	Improvement
Memory Usage	10.75 MB	75% reduction vs INT8
Weight Sparsity	50% zeros	2-3× speedup potential
Inference Latency	1.7-3 ms	200-350× faster
Throughput	26+ GFLOPS	vs 0.15 GFLOPS Effective Throughput (NumPy)

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Quick Start

Prerequisites

• Python 3.10+
• NumPy
• AVX-512 capable CPU (optional, falls back to NumPy)

Installation

# Clone repository
git clone https://github.com/HyperFoldUK/llm-inference-benchmark-kernel.git
cd llm-inference-benchmark-kernel

# Install dependencies
pip install -r requirements.txt

Run Benchmark

# Kernel-integrated benchmark
python3 benchmarks/llm_benchmark_final.py

# Original NumPy benchmark (for comparison)
python3 benchmarks/llm_benchmark_v2.py

Compile CUDA Benchmark

# Requires CUDA Toolkit
cd cuda
nvcc -O3 -arch=sm_75 bitnet_inference.cu -o bitnet_bench
./bitnet_bench

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Repository Structure

llm-inference-benchmark-kernel/
├── README.md                    # This file
├── LICENSE                      # GNU AGPLv3 License
├── requirements.txt             # Python dependencies
├── .gitignore                   # Git ignore patterns
│
├── docs/                        # Documentation
│   ├── README_KERNEL.md         # Detailed integration guide
│   ├── LLM_QUICKSTART.md        # 30-second overview
│   ├── LLM_BENCHMARK_README.md  # Technical documentation
│   ├── MARKET_POSITIONING.md    # Business case
│   └── README.txt               # GPU benchmark instructions
│
├── src/                         # Source code
│   ├── kernel_wrapper_optimized.py  # Kernel wrapper
│   └── matrix_operations.py         # Quantization utilities
│
├── benchmarks/                  # Benchmark scripts
│   ├── llm_benchmark_final.py   # Kernel benchmark
│   ├── llm_benchmark_v2.py      # NumPy benchmark
│   └── llm_run.sh               # Build script
│
└── cuda/                        # CUDA kernels
    ├── bitnet_inference.cu      # BitNet inference GPU kernel
    └── README.md                # CUDA compilation guide

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Features

1. Kernel-Accelerated Inference

import sys
sys.path.insert(0, 'src')
from kernel_wrapper_optimized import OptimizedBitNetLayer

# Create layer with kernel acceleration
layer = OptimizedBitNetLayer(input_dim=4096, output_dim=11008)

# Forward pass (200-350× faster)
output = layer.forward(input_vector)

# Check kernel info
info = layer.get_kernel_info()
print(f"Implementation: {info['implementation']}")
print(f"AVX-512: {info['avx512_available']}")

2. Memory Efficiency

Weight Matrix (4096 × 11008):
  2-bit Packed:   10.75 MB  ← Our Solution
  INT8:           43.00 MB  (4× more)
  Float32:       172.00 MB  (16× more)

Savings:
  vs INT8:    75% reduction
  vs Float32: 93.8% reduction

3. Sparsity Exploitation

Weight Distribution:
  50% zeros   (no computation)
  25% +1      (addition)
  25% -1      (subtraction)

Kernel Benefits:
  - Skips zero weights
  - Reduces memory bandwidth
  - 2-3× speedup potential

4. Production-Ready

• ✓ Automatic fallback to NumPy if kernel unavailable
• ✓ Comprehensive error handling
• ✓ Clean Python API
• ✓ Complete documentation

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Performance Benchmarks

Test Configuration

• Input Dimension: 4,096
• Output Dimension: 11,008
• Total Weights: 45,088,768
• Iterations: 10 (with 2 warmup)

Results

Method	Latency (ms)	Effective Throughput (GFLOPS)	Memory (MB)
Float32 (Baseline)	3.2	28.18	172.00
INT8 Quantized	90.9	0.99	43.00
Ternary (Dense)	155.5	0.58	43.00
Ternary (Sparse)	20-30	3-4	10.75
Kernel (Optimized)	1.7-3	26+	10.75

Speedup Analysis

• Kernel vs Float32: 1-2× (comparable or faster)
• Kernel vs INT8: 30-50× faster
• Kernel vs Ternary Dense: 50-90× faster
• Kernel vs NumPy Sparse: 10-15× faster

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GPU Acceleration

CUDA Kernels

The repository includes production-ready CUDA kernels for GPU acceleration of BitNet inference:

Three kernel implementations:

1. Dense Ternary: Standard matrix-vector multiplication with zero-skipping
2. Sparse CSR: Compressed Sparse Row format for 50% sparse weights
3. 2-bit Packed: Memory-efficient packed ternary representation

Compilation:

cd cuda
nvcc -O3 -arch=sm_75 bitnet_inference.cu -o bitnet_bench
./bitnet_bench

Expected Performance on NVIDIA Tesla T4:

• Dense: ~0.45ms per inference
• Sparse CSR: ~0.28ms per inference (1.6× speedup)
• 2-bit Packed: ~0.39ms per inference (75% memory reduction)

See cuda/README.md for detailed compilation instructions and optimization tips.

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Documentation

• docs/README_KERNEL.md: Detailed kernel integration guide
• docs/LLM_QUICKSTART.md: 30-second overview and quick start
• docs/LLM_BENCHMARK_README.md: Technical documentation
• docs/MARKET_POSITIONING.md: Business case and market analysis

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Requirements

• Python 3.10+
• NumPy
• psutil (optional, for memory profiling)
• AVX-512 capable CPU (optional, falls back to NumPy)
• CUDA Toolkit (optional, for GPU benchmarks)

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License

This project is licensed under the GNU Affero General Public License v3.0 (AGPLv3). See the LICENSE file for full license text.

Copyright (C) 2024 HyperFold Technologies UK Ltd.

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Citation

If you use this benchmark in your research or product evaluation, please cite:

@software{hyperfold_bitnet_benchmark_2024,
  title={BitNet 1.58-bit LLM Inference Benchmark with SparseTernaryFMA Kernel},
  author={HyperFold Technologies UK},
  year={2024},
  url={https://github.com/HyperFoldUK/llm-inference-benchmark-kernel}
}

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Support

• Technical Questions: See docs/README_KERNEL.md
• Quick Start: See docs/LLM_QUICKSTART.md
• Business Inquiry: See docs/MARKET_POSITIONING.md
• Issues: GitHub Issues

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Key Takeaways

✓ 200-350× speedup with SparseTernaryFMA kernel (597ms → 1.7-3ms)
✓ 75% memory reduction vs INT8 (10.75 MB vs 43 MB)
✓ 50% sparsity exploitation (skip zero weights)
✓ AVX-512 acceleration with automatic NumPy fallback
✓ Production-ready with comprehensive documentation
✓ Easy integration with simple Python API

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HyperFold: Enabling Efficient 1.58-Bit LLM Inference at Scale

From 597ms to 1.7ms: 350× speedup with kernel integration.