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SqueezeAILab avatar

SqueezeAILab/KVQuant

0
View on GitHub↗
427 stars·46 forks·Python·7 viewsarxiv.org/abs/2401.18079↗

KVQuant

[NeurIPS 2024] KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

Features

  • Attention Optimization - Quantizes KV cache to support extremely long context lengths.

Star history

Star history chart for squeezeailab/kvquantStar history chart for squeezeailab/kvquant

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

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Open-source alternatives to KVQuant

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Frequently asked questions

What does squeezeailab/kvquant do?

[NeurIPS 2024] KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

What are the main features of squeezeailab/kvquant?

The main features of squeezeailab/kvquant are: Attention Optimization.

What are some open-source alternatives to squeezeailab/kvquant?

Open-source alternatives to squeezeailab/kvquant include: stability-ai/stablelm — StableLM is a pre-trained transformer-based large language model designed for natural language generation and… dao-ailab/flash-attention — FlashAttention is an attention mechanism optimization library and machine learning acceleration framework designed to… deepseek-ai/3fs — 3FS is a distributed file system and RDMA storage cluster designed for high-performance AI training and inference… deepseek-ai/deepep — DeepEP is a distributed model accelerator and expert-parallel communication library designed to optimize the training… deepseek-ai/deepgemm — DeepGEMM is a suite of specialized GPU kernels and a just-in-time compiler designed for low-precision matrix… bytedance/shadowkv — [ICML 2025 Spotlight] ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference.