litespark-inference 0.1.0

Efficient CPU inference for BitNet 1.58-bit models

Litespark-Inference

Fast CPU inference for ternary neural networks

Litespark-Inference is a pip-installable Python library that enables efficient inference of ternary models on consumer CPUs. By exploiting the ternary weight structure ({-1, 0, +1}) with custom SIMD kernels, we eliminate floating-point multiplication entirely and achieve dramatic speedups over standard PyTorch inference.

Key Results

Apple Silicon (M1–M4)

Performance comparison on Apple Silicon M4. Litespark-Inference achieves ~14× memory reduction, 9.2× faster TTFT, and 52× higher throughput compared to PyTorch.

Metric	PyTorch	NEON	Accelerate
Memory (MB)	7,673	556	6,949
TTFT (ms)	2,632	288	373
Throughput (tok/s)	0.39	20.4	5.52

Intel Ice Lake / AMD Zen4 (AVX-512 VNNI)

Performance comparison on Intel Ice Lake / AMD Zen4 using AVX-512 VNNI kernels.

Metric	PyTorch	AVX-512 VNNI	Speedup
Memory (MB)	7,800	556	14.0×
TTFT (ms)	2,450	195	12.6×
Throughput (tok/s)	0.42	11.2	26.7×

Intel Core Ultra (AVX-VNNI)

Performance comparison on Intel Core Ultra using AVX-VNNI kernels.

Metric	PyTorch	AVX-VNNI	Speedup
Memory (MB)	7,750	556	13.9×
TTFT (ms)	2,580	310	8.3×
Throughput (tok/s)	0.40	8.5	21.3×

Cross-Platform Comparison

Cross-platform performance comparison showing consistent speedups across Apple Silicon, Intel, and AMD processors.

Comparison with BitNet.cpp v2

We benchmarked Litespark-Inference against Microsoft's BitNet.cpp v2 using their pp128+tg128 methodology (128-token prompt processing + 128-token generation).

AMD EPYC 9R14 (AWS c7a.2xlarge)

Scaling behavior on AMD EPYC 9R14. BitNet.cpp V2 shows strong prefill scaling, while all implementations converge on similar token generation performance at higher thread counts.

Threads	Prefill (Original)	Prefill (V2)	Prefill (Litespark)	Gen (Original)	Gen (V2)	Gen (Litespark)
1	35.0	43.4	38.2	10.0	15.6	15.9
2	70.0	81.2	74.7	18.0	28.7	28.1
4	140.0	156.8	140.7	30.0	49.2	48.2
8	210.0	291.8	230.7	42.0	66.2	67.5

Intel Xeon Platinum 8488C (AWS c7i.2xlarge)

Scaling behavior on Intel Xeon Platinum 8488C. Litespark-Inference maintains a consistent lead in prefill throughput across all thread configurations.

Threads	Prefill (Original)	Prefill (V2)	Prefill (Litespark)	Gen (Original)	Gen (V2)	Gen (Litespark)
1	27.0	43.4	59.7	10.0	13.3	13.6
2	40.0	65.8	85.9	13.0	19.1	19.5
4	55.0	77.9	110.2	16.0	24.3	25.0
6	79.0	101.3	120.7	20.0	29.5	28.0

Apple M4 (MacBook Pro)

Litespark-Inference scaling on Apple M4. Prefill throughput scales nearly linearly up to 4 threads, while token generation benefits from using all 10 CPU cores.

Threads	Prefill pp128 (tok/s)	Generation tg128 (tok/s)
1	26.1	6.5
2	43.1	11.0
4	81.9	15.4
8	101.2	14.0
10	108.8	19.6

Supported Platforms

• Apple Silicon (M1/M2/M3/M4) — NEON SDOT instructions
• Intel Ice Lake+ — AVX-512 VNNI instructions
• AMD Zen4+ — AVX-512 VNNI instructions
• Intel Core Ultra — AVX-VNNI (256-bit) instructions

Installation

git clone https://github.com/Mindbeam-AI/Litespark-Inference.git
cd Litespark-Inference
pip install -e .

Requirements:

• Python 3.9+
• PyTorch 2.0+
• macOS: brew install libomp (for OpenMP support)

Usage

Command Line

# Generate text
litespark-inference generate "The meaning of life is"

# Interactive chat
litespark-inference chat

# Run benchmark on your hardware
litespark-inference benchmark

# Show system info and detected SIMD capabilities
litespark-inference info

Python API

from litespark_inference import load_model

# Load the BitNet 2B model (auto-downloads from HuggingFace)
model, tokenizer = load_model("bitnet-2b")

# Generate text
input_ids = tokenizer.encode("Hello, world!", return_tensors="pt")
output = model.generate(input_ids, max_new_tokens=100)
print(tokenizer.decode(output[0]))

Kernel Modes (Apple Silicon)

Two inference modes are available on Apple Silicon:

# NEON mode (default) — fast int8 quantized inference, ~556 MB
litespark-inference generate "Hello" --mode neon

# Accelerate mode — float32 with Apple AMX, bit-exact accuracy, ~2.5 GB
litespark-inference generate "Hello" --mode accelerate

# In Python
model, tokenizer = load_model("bitnet-2b", mode="neon")       # default, fast
model, tokenizer = load_model("bitnet-2b", mode="accelerate") # accurate

How It Works

Ternary models use weights constrained to {-1, 0, +1}. This means matrix multiplication reduces to simple addition and subtraction:

y = Σ x_j · w_j  →  y = Σ(w=+1) x_j - Σ(w=-1) x_j

Litespark-Inference exploits this structure with custom SIMD kernels that:

1. Store weights as int8 — enabling direct use of hardware dot product instructions
2. Quantize activations per-row — converting float32 inputs to int8 with scale factors
3. Use hardware SIMD instructions — NEON SDOT (ARM) or AVX-512 VPDPBUSD (x86)
4. Apply zero-point correction — maintaining numerical accuracy

The library automatically detects your CPU's SIMD capabilities and dispatches to the optimal kernel.

Benchmarking

Run the built-in benchmark to measure performance on your hardware:

litespark-inference benchmark

Or use the benchmark scripts for detailed profiling:

python benchmark_kernel.py      # Kernel-level benchmarks
python benchmark_synthetic.py   # Synthetic workload benchmarks

Citation

If you use Litespark-Inference in your research, please cite:

@article{litespark2024,
  title={Litespark Inference on Consumer CPUs: Custom SIMD Kernels for Ternary Neural Networks},
  author={Dade, Nii Osae Osae and Morri, Maurizio and Rahat, Moinul Hossain},
  year={2024}
}

License

Apache License 2.0. See LICENSE for details.