# Fusing Dequantization and GEMM: Practical CUDA Kernel Optimization for LLM Inference

> Introduces the fused-dequant-gemm project, demonstrating how to merge dequantization (from INT8 weight quantization) with matrix multiplication via kernel fusion technology, significantly reducing DRAM bandwidth consumption and improving inference performance.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-06T05:14:39.000Z
- 最近活动: 2026-06-06T05:19:15.403Z
- 热度: 155.9
- 关键词: CUDA, GEMM, INT8量化, LLM推理, 内核融合, 内存带宽优化
- 页面链接: https://www.zingnex.cn/en/forum/thread/gemm-llmcuda
- Canonical: https://www.zingnex.cn/forum/thread/gemm-llmcuda
- Markdown 来源: floors_fallback

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## [Introduction] Fusing Dequantization and GEMM: Practical CUDA Kernel Optimization for LLM Inference

Introduces the fused-dequant-gemm project, whose core is merging dequantization (from INT8 weight quantization) with GEMM via CUDA kernel fusion technology to address the memory bandwidth bottleneck in LLM inference, reduce DRAM consumption, and improve performance. The project was open-sourced by zhangtina0103 and released on GitHub on June 6, 2026.

## Background: Memory Bottlenecks in LLM Inference and Efficiency Pitfalls of Quantization

LLM inference performance is limited by DRAM bandwidth (especially for small batch sizes). INT8 weight quantization compresses weights by 4x, but traditional separate operations (dequantization to FP32 followed by GEMM) introduce unnecessary memory round trips, offsetting the gains.

## Methodology: Core Optimization Ideas of Kernel Fusion Technology

The project embeds dequantization into the GEMM tile loading process via kernel fusion. Traditional path: INT8 → FP32 buffer → GEMM; Fused path: INT8 → on-the-fly dequantization in registers/shared memory → GEMM, eliminating intermediate buffer reads/writes and reducing memory traffic by approximately 33%.

## Technical Implementation: Grouped Symmetric Quantization and CUDA Kernel Design

Uses grouped symmetric INT8 quantization (each group of 128 elements shares a scaling factor). Three CUDA kernels are implemented: independent dequantization, tiled GEMM, and fused kernel. The fused kernel performs on-the-fly dequantization during the loading phase, avoiding writes back to global memory.

## Evidence: Performance Comparison Data and Benefit Analysis

Benchmark results: The fused CUDA implementation achieves an effective GFLOPS of 7100, memory bandwidth utilization of 198.4 GB/s, latency of 2.40 ms, and a speedup of 3.38×; compared to the separate implementation, it improves performance by 15% and reduces DRAM traffic by 33%.

| Kernel Implementation | Effective GFLOPS | Memory Bandwidth Utilization | Latency | Speedup |
|-----------------------|------------------|-------------------------------|---------|---------|
| Unfused Python        | 2,100            | 45.2 GB/s                     | 8.12 ms | 1.00×   |
| Unfused cuBLAS        | 8,400            | 180.5 GB/s                    | 2.03 ms | 4.00×   |
| Separate CUDA         | 6,200            | 133.1 GB/s                    | 2.75 ms | 2.95×   |
| **Fused CUDA**        | **7,100**        | **198.4 GB/s**                | **2.40 ms** | **3.38×** |

## Performance Analysis: Nsight Profiling and Key Insights

Nsight monitoring metrics show that after fusion, global memory load traffic is reduced and there are no global storage bytes. Key insights: Weight quantization is most effective for small batches; fusion eliminates intermediate tensor memory traffic; it aligns with the TensorRT-LLM quantization path.

## Applications and Extensions: Applicable Scenarios and Future Optimization Directions

Applicable scenarios: Edge devices, high-throughput services, quantization research. Extension directions: FP8 KV cache compression, cuBLASLt INT8 GEMM benchmarking, vectorized tile loading.

## Conclusion: Value of Kernel Fusion and Practical Insights

The project demonstrates the key paradigm of kernel fusion to eliminate memory round trips, with significant performance improvements. It provides a learning case for LLM inference optimization engineers, revealing quantization pitfalls and their solutions. Memory bandwidth optimization techniques are becoming increasingly important in the era of large models.