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Kvquant

KVQuant is a Python research project that reduces the memory footprint of long-context language-model inference by quantizing the model’s key-value cache to low precision. Its methods include per-channel, pre-RoPE key quantization, non-uniform quantization and dense-and-sparse quantization.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Kvquant — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use KVQuant when investigating how low-precision KV-cache representations can reduce the memory demands of long-context LLM inference, or when working through its separate quantization, deployment, evaluation and benchmarking code paths.

Who it is for

  • Researchers studying low-precision KV-cache quantization and inference accuracy.
  • Engineers exploring memory-efficient, long-context LLM inference and GPU kernel performance.

Documented uses

  • Quantize a new model after computing its Fisher information.
  • Run simulated quantization and evaluation experiments.
  • Run inference with quantized models or compressed vectors.
  • Evaluate quantized Large World Models and benchmark kernels.
Before you choose

Requirements & limits

The repository is public on GitHub. Installation instructions are organized by subfolder, each with its own README.

  • Quantizing a new model requires the gradients stage to compute Fisher information before the quant stage.
  • Deployment and kernel benchmarking require quantizers produced by the quant stage.
  • The repository documents separate inference and evaluation scripts for quantized Large World Models.
  • Reported context-length examples use specified GPU configurations and should be treated as project examples, not universal performance guarantees.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare KVQuant’s perplexity-reproduction workflow

Set up the documented sequence for reproducing the paper’s perplexity numbers.

Before you start

  • Use the KVQuant repository and its subfolder READMEs for environment setup instructions.

Steps

  1. Follow the README in the gradients subfolder to install its required environment.

  2. Use gradients to compute Fisher information.

  3. Follow the README in the quant subfolder to install its required environment.

  4. Use quant for simulated quantization and evaluation after computing Fisher information.

Expected outcome

The documented gradients-then-quant workflow for reproducing the paper’s perplexity numbers is prepared and run.

Guide limitations

  • The top-level README does not provide the exact installation commands or detailed execution parameters; those instructions are in the individual subfolder READMEs.
  • The documented quant workflow is for simulated quantization and evaluation, not deployment.
Documentation 1 ↗
Source-based practical guide

Run and evaluate quantized Large World Models using KVQuant

Use KVQuant’s `lwm` component for inference and evaluation with quantized Large World Models.

Before you start

  • Have access to quantized Large World Models; the lwm component is described for those models.
  • Follow the lwm subfolder README for environment setup and instructions.

Steps

  1. Open the lwm subfolder and follow its README to install the required environment.

  2. Use the scripts in lwm/ to run inference with quantized Large World Models.

  3. Use the scripts in lwm/ to evaluate those models.

Expected outcome

The documented `lwm` workflow provides scripts for inference and evaluation with quantized Large World Models.

Guide limitations

  • The top-level README does not give script names, commands, or detailed execution parameters; refer to the lwm README.
  • This workflow concerns quantized LWM models, rather than quantizing a new model from scratch.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
SqueezeAILab/KVQuant · Official homepage title · retained in its source language
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