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GPT-FAST
PyTorch-native LLM inference with <1000 lines of code
BSD-3-Clause
ABOUT
LLM inference frameworks are often heavyweight, opaque, and hard to modify for experimentation. gpt-fast shows that high-performance transformer text generation — with int8/int4 quantization, speculative decoding, and tensor parallelism — can be achieved in under 1000 lines of native PyTorch. It serves as both a performant inference engine for LLaMA-family models and an educational reference for understanding the optimization techniques behind modern LLM serving.
INTEGRATION GUIDE
1. Run LLaMA-family models with efficient int8 or int4 quantization on consumer and datacenter GPUs
2. Benchmark speculative decoding performance improvements for latency-sensitive text generation
3. Study and extend a minimal, readable reference implementation of production-quality LLM inference
4. Deploy multi-GPU tensor parallelism for Mixtral 8x7B MoE models without a heavyweight serving framework
5. Experiment with PyTorch-native optimizations (torch.compile, CUDA graphs) for transformer inference
TAGS
llm-inferencepytorchquantizationtransformergpu-optimizationtext-generationspeculative-decoding