# Tencent/hpc-ops

High Performance LLM Inference Operator Library

Repository: https://github.com/Tencent/hpc-ops
Canonical: https://ross.abutalabs.com/products/hpc-ops
Language: C++
License: NOASSERTION
License Family: other
Last push: 2026-08-06T10:07:32+00:00

## Health v2 (maintenance only)
Score: 59/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 96, release rhythm 35, longevity 16
- inputs: {"age_days": 225, "days_push": 27, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1131, forks 140 (observed 2026-08-28T04:03:42.520777+00:00)

## What it is
HPC-Ops is a production-grade C++/CUDA operator library for high-performance LLM inference, developed by Tencent's Hunyuan AI Infra team. It provides optimized kernels for attention, MoE, GEMM, sampling, normalization, and fused communication-compute on modern NVIDIA GPUs, with a Python API for integration into frameworks like vLLM and SGLang.

## Use cases
- speed up LLM inference serving latency and throughput
- integrate optimized attention and MoE kernels into vLLM or SGLang
- run FP8 and BF16 mixed-precision inference on NVIDIA H20 GPUs
- benchmark custom CUDA kernels against FlashInfer, cuBLAS, and TensorRT-LLM
- learn to write production CUDA kernels with CuTe, CUTLASS, TMA, and PDL
- balance dynamic decode workloads with variable KV-cache lengths

## When to choose
- you serve LLMs on NVIDIA Hopper-class GPUs (especially H20) and need SOTA kernels
- you want drop-in operators for popular inference frameworks like vLLM or SGLang
- you need FP8/quantized or mixed-precision kernels for accuracy-sensitive inference
- you want compact, production-quality CUDA examples for learning GPU kernel engineering

## When to avoid
- you target non-NVIDIA GPUs or older CUDA versions below 12.8
- you need a full end-to-end inference engine rather than individual operators
- your workloads are small-scale or CPU-bound where kernel optimization matters little
- you require a permissive license for redistribution and cannot accept the custom license

## Facets
- artifact type: library
- maturity: active
- function: llm-inference, gpu-computing, machine-learning, benchmarking
- domain: deep-learning, large-language-models, gpu-computing, performance
- platform: python, cpp
- tags: cuda-kernels, attention, moe, gemm, fp8, inference-optimization, nvidia-h20, cutlass, gpu, linux

## Member repositories
- Tencent/hpc-ops (main) score 59

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:42.520777+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:38:00.752117+00:00, confidence not recorded.
  - readme: https://github.com/Tencent/hpc-ops (fetched 2026-08-28T04:03:42.520777+00:00, sha b904861024f9)
- Data as of 2026-08-30T08:39:29.467469+00:00.