Tencent Hunyuan Releases HPC-Ops: A High Performance LLM Inference Operator Library

Tencent Hunyuan Releases HPC-Ops: A High Performance LLM Inference Operator Library

Tencent Hunyuan has open-sourced HPC-Ops, a production-ready operator library designed to accelerate large language model (LLM) inference on NVIDIA GPUs. The library focuses on core operators like Attention, GEMM, and MoE, aiming to improve performance within existing inference stacks by up to 2.22x.

HPC-Ops addresses the gap between theoretical performance gains and real-world inference speeds, where optimized kernels can dramatically impact query throughput. Current LLM serving infrastructure often relies on general-purpose libraries, missing opportunities for tailored CUDA optimizations that directly address the computational bottlenecks in LLM workloads; a slow inference pipeline can degrade user experience and increase operational costs.

Key Insights

• 30% QPM Improvement: Internal deployments of HPC-Ops show a 30% increase in queries per minute (QPM) for Tencent-HY models.
• CUDA Focus: HPC-Ops leverages C++ and CUDA with CuTe and CUTLASS for low-level kernel optimization.
• Framework Integration: Designed to integrate seamlessly into popular frameworks like vLLM and SGLang, minimizing disruption to existing infrastructure.

Working Example

// Example snippet demonstrating a simplified GEMM operation from the HPC-Ops perspective
// This is illustrative and not a complete implementation.
#include <cuda_runtime.h>
#include <iostream>

__global__ void gemm_kernel(float *A, float *B, float *C, int M, int N, int K) {
  int row = blockIdx.y * blockDim.y + threadIdx.y;
  int col = blockIdx.x * blockDim.x + threadIdx.x;

  if (row < M && col < N) {
    float sum = 0.0f;
    for (int k = 0; k < K; ++k) {
      sum += A[row * K + k] * B[k * N + col];
    }
    C[row * N + col] = sum;
  }
}

// Host side function to launch the GEMM kernel
void launch_gemm(float *A, float *B, float *C, int M, int N, int K) {
  // Assuming block size of 16x16
  dim3 blockDim(16, 16);
  dim3 gridDim((N + blockDim.x - 1) / blockDim.x, (M + blockDim.y - 1) / blockDim.y);

  gemm_kernel<<<gridDim, blockDim>>>(A, B, C, M, N, K);
  cudaDeviceSynchronize();
}

Practical Applications

• Cloud Providers: Enhance LLM serving infrastructure within cloud platforms to deliver faster response times and higher throughput for customers.
• Model Developers: Integrate HPC-Ops into model serving pipelines to experiment with and benefit from low-level CUDA optimizations without extensive kernel development.

References:

• https://www.marktechpost.com/2026/01/27/tencent-hunyuan-releases-hpc-ops-a-high-performance-llm-inference-operator-library/