Gpt-Fast

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gpt-fast is an AI Frameworks & Infra tool that provides simple and efficient PyTorch-native transformer text generation. It features very low latency and is implemented in under 1000 lines of Python code.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is gpt-fast?

gpt-fast is a highly efficient PyTorch-native transformer text generation tool, designed for minimal latency and a compact codebase of under 1000 lines of Python. It supports advanced features like int8/int4 quantization, speculative decoding, and tensor parallelism, making it suitable for high-performance applications. The tool is compatible with both Nvidia and AMD GPUs and is intended to showcase optimal performance achievable with native PyTorch, rather than serving as a comprehensive framework. Developers are encouraged to copy, paste, and fork the codebase for their specific needs, leveraging its efficiency for various LLM inference tasks.

Best used for

Ideal for developers and data scientists who need to achieve very low latency text generation, optimize LLM inference with quantization, and implement tensor parallelism. Especially valuable for those looking to leverage native PyTorch for high-performance transformer models on Nvidia and AMD GPUs.

Common actions

generate text

optimize LLM inference

quantize models

implement transformer models

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Capabilities

Key features

Low latency generation
PyTorch-native
Int8/int4 quantization
Speculative decoding
Tensor parallelism
Nvidia/AMD GPU support

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What models does gpt-fast support for text generation?

gpt-fast supports a range of models including the LLaMA family (Llama-2, Llama-3.1), Mixtral 8x7B, CodeLlama, Mistral-7B, Google Gemma, xAI Grok-1, and Databricks DBRX. It focuses on efficient generation for these transformer-based models.

Can gpt-fast be used for model training or fine-tuning?

No, gpt-fast is specifically designed for efficient text generation and inference. It is not intended as a framework for SFT/RM/PPO training or batched inference, though community projects like gpt-accelera extend it for such purposes.

What are the hardware requirements for running gpt-fast?

gpt-fast is optimized for performance on GPUs, supporting both Nvidia (e.g., A100) and AMD (e.g., MI-250x) hardware. Benchmarks are provided for various GPU configurations, indicating its capability to scale with multiple GPUs for tensor parallelism.

Does gpt-fast offer quantization for models?

Yes, gpt-fast supports int8 and int4 weight-only quantization, including a pure PyTorch implementation of GPTQ. This allows for reduced memory footprint and potentially faster inference, as demonstrated in its benchmarks.

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Coding & Development › Open Source & Models Research & Education › Scientific Computing

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