# meta-pytorch/gpt-fast

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6,223 stars · 573 forks · Python · BSD-3-Clause

## Links

- GitHub: https://github.com/meta-pytorch/gpt-fast
- awesome-repositories: https://awesome-repositories.com/repository/meta-pytorch-gpt-fast.md

## Description

gpt-fast is a PyTorch transformer inference engine designed for text generation using a native tensor library implementation. It provides a runtime for executing large language models without the need for external C++ extensions.

The project implements speculative decoding to accelerate generation by using a small draft model for token prediction and a larger model for verification. It further optimizes performance through a compiled prefill stage and a multi-GPU tensor parallelism library that shards linear layers across multiple graphics processing units.

Memory efficiency is managed through a quantized runtime supporting int8 and int4 weight and grouped tensor quantization. The system also includes tools for architecture parameterization, text tokenization, and model accuracy evaluation using standardized harnesses.

## Tags

### Artificial Intelligence & ML

- [Model Inference Runtimes](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/hardware-and-acceleration/tensor-computing-libraries/pytorch-tensor-operations/model-inference-runtimes.md) — Provides a lightweight native PyTorch runtime for executing transformer-based text generation.
- [Generative Text Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/generative-ai/generative-text-inference.md) — Implements a native tensor library for producing text outputs from language models. ([source](https://github.com/meta-pytorch/gpt-fast/blob/main/setup.py))
- [Speculative Decoding Strategies](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/inference-optimization/inference-acceleration-techniques/speculative-decoding-strategies.md) — Implements speculative decoding using a small draft model to accelerate token generation. ([source](https://github.com/meta-pytorch/gpt-fast#readme))
- [PyTorch Tensor Operations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/hardware-and-acceleration/tensor-computing-libraries/pytorch-tensor-operations.md) — Performs transformer inference using native PyTorch tensor operations without requiring external C++ extensions.
- [Weight Quantization](https://awesome-repositories.com/f/artificial-intelligence-ml/quantized-inference-runtimes/weight-quantization.md) — Provides a runtime that supports int8 and int4 weight quantization to decrease GPU memory consumption. ([source](https://github.com/meta-pytorch/gpt-fast#readme))
- [Transformer Inference Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-inference-engines.md) — Implements a high-performance inference engine optimized specifically for transformer models.
- [Multi-GPU Parallelism Strategies](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-inference-services/multi-gpu-parallelism-strategies.md) — Distributes large model weights across multiple GPUs using tensor parallelism to increase throughput.
- [Compiled Prefill Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/memory-optimization/prefill-phase-optimizations/compiled-prefill-implementations.md) — Offers a high-performance implementation that optimizes the prefill stage through model compilation.
- [Compiled Prefill Stages](https://awesome-repositories.com/f/artificial-intelligence-ml/memory-optimization/prefill-phase-optimizations/compiled-prefill-stages.md) — Uses just-in-time compilation to optimize the initial prompt processing phase for faster generation.
- [Compiled Prefills](https://awesome-repositories.com/f/artificial-intelligence-ml/memory-optimization/prefill-phase-optimizations/compiled-prefills.md) — Implements a compiled prefill stage to accelerate the initial processing of prompt tokens. ([source](https://github.com/meta-pytorch/gpt-fast/tree/main/mixtral-moe))
- [Tensor-Parallel Inference Distributions](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/inference-deployment/model-deployment-toolkits/distributed-deployment-utilities/multi-gpu-distribution/tensor-parallel-inference-distributions.md) — Provides a toolkit for splitting model weights across multiple GPUs using tensor parallelism.
- [Model Quantization Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/quantization/model-quantization-frameworks.md) — Provides a framework for converting high-precision model weights into lower-precision formats like int4.
- [Quantized Inference Runtimes](https://awesome-repositories.com/f/artificial-intelligence-ml/quantized-inference-runtimes.md) — Provides an execution environment designed to run compressed int8 and int4 quantized models.
- [Grouped Quantization](https://awesome-repositories.com/f/artificial-intelligence-ml/quantized-inference-runtimes/weight-quantization/grouped-quantization.md) — Supports grouped tensor quantization to maintain precision while reducing memory usage. ([source](https://github.com/meta-pytorch/gpt-fast/blob/main/quantize.py))
- [Linear Layer Sharding](https://awesome-repositories.com/f/artificial-intelligence-ml/quantized-inference-runtimes/weight-quantization/quantized-training/weight-sharding/linear-layer-sharding.md) — Splits linear layer weights across multiple GPUs using row or column sharding to manage memory.
- [Tensor Parallelism](https://awesome-repositories.com/f/artificial-intelligence-ml/tensor-parallelism.md) — Implements strategies for partitioning model weights across multiple GPUs to handle larger models. ([source](https://github.com/meta-pytorch/gpt-fast/blob/main/README.md))

### Part of an Awesome List

- [Weight Quantization](https://awesome-repositories.com/f/awesome-lists/ai/memory-and-caching/model-memory-reclamation/weight-quantization.md) — Reduces GPU memory consumption by compressing model weights into int8 and int4 formats.
- [Speculative Decoding](https://awesome-repositories.com/f/awesome-lists/ai/speculative-decoding.md) — Ships a framework that uses smaller draft models to accelerate token generation via verification.
- [Language Model Development](https://awesome-repositories.com/f/awesome-lists/ai/language-model-development.md) — Efficient PyTorch-native transformer text generation.