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type: "post"
id: "announcing-humming-fast-flexible-kernels-for-quantized-inference-67a0bf"
slug: "announcing-humming-fast-flexible-kernels-for-quantized-inference-67a0bf"
title: "Announcing Humming: Fast, Flexible Kernels for Quantized Inference (19 minute read)"
description: "On October 21, 2026, Ant Group and the vLLM community announced Humming, an open-source kernel library for quantized inference."
retrieval_nugget: "On October 21, 2026, Ant Group and the vLLM community announced Humming, an open-source kernel library for quantized inference."
published_at: "2026-10-22"
updated_at: "2026-10-22"
record_date: "2026-10-22"
date_kind: "published_at"
topics: ["inference"]
entities: ["vLLM","Humming","Ant Group","NVIDIA","T-Head"]
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novelty_label: "new_fact"
what_changed: "vLLM and Ant Group announce Humming, an open-source quantized-inference kernel library with broad device and format support and claimed performance gains; the prior vLLM items cover TPU engines, Kimi K3 optimization and KV transfer."
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source_url: "https://vllm.ai/blog/2026-10-21-humming"
source_urls: ["https://vllm.ai/blog/2026-10-21-humming"]
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status: "published"
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# Announcing Humming: Fast, Flexible Kernels for Quantized Inference (19 minute read)

## Retrieval answer

On October 21, 2026, Ant Group and the vLLM community announced Humming, an open-source kernel library for quantized inference.

On October 21, 2026, Ant Group and the vLLM community announced Humming, an open-source kernel library for quantized inference. The project is intended for dense and mixture-of-experts workloads and is positioned as a general-purpose alternative to maintaining separate kernels for many quantization formats.

Humming converts supported model exports into a common representation for weights, scales, zero points, and related tensors. It then combines quantization settings, tensor shapes, GEMM type, target GPU, and tuning options to compile kernels with NVRTC or NVCC. The project lists WNA16, W8A8, W4A8, and W4A4 support, and says it supports NVIDIA GPU architectures from Turing onward as well as the CUDA-compatible T-Head Zhenwu 810E. Compiled kernels are cached for reuse.

The authors report comparisons with vLLM Marlin, FlashInfer, and DeepGEMM across representative workloads. The announcement does not establish a single performance result for every model, GPU, batch size, or deployment configuration, nor does it provide a universal cost or latency claim.

Source: https://vllm.ai/blog/2026-10-21-humming

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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