leimao / Nsight-Compute-Docker-ImageLinks
Nsight Compute In Docker
☆12Updated last year
Alternatives and similar repositories for Nsight-Compute-Docker-Image
Users that are interested in Nsight-Compute-Docker-Image are comparing it to the libraries listed below
Sorting:
- A standalone GEMM kernel for fp16 activation and quantized weight, extracted from FasterTransformer☆92Updated this week
- Standalone Flash Attention v2 kernel without libtorch dependency☆110Updated 10 months ago
- ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.☆86Updated 2 months ago
- ☆11Updated 4 months ago
- Benchmark code for the "Online normalizer calculation for softmax" paper☆95Updated 6 years ago
- ☆31Updated 5 months ago
- Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.☆39Updated 4 months ago
- Several optimization methods of half-precision general matrix vector multiplication (HGEMV) using CUDA core.☆63Updated 10 months ago
- Decoding Attention is specially optimized for MHA, MQA, GQA and MLA using CUDA core for the decoding stage of LLM inference.☆38Updated last month
- Multiple GEMM operators are constructed with cutlass to support LLM inference.☆18Updated 9 months ago
- llama INT4 cuda inference with AWQ☆54Updated 5 months ago
- 使用 cutlass 实现 flash-attention 精简版,具有教学意义☆43Updated 10 months ago
- ☆19Updated 9 months ago
- Fast and memory-efficient exact attention☆79Updated last week
- 使用 cutlass 仓库在 ada 架构上实现 fp8 的 flash attention☆72Updated 10 months ago
- CUDA 8-bit Tensor Core Matrix Multiplication based on m16n16k16 WMMA API☆31Updated last year
- A practical way of learning Swizzle☆20Updated 5 months ago
- ☆96Updated 10 months ago
- Triton adapter for Ascend. Mirror of https://gitee.com/ascend/triton-ascend☆59Updated this week
- GPTQ inference TVM kernel☆40Updated last year
- 使用 CUDA C++ 实现的 llama 模型推理框架☆58Updated 8 months ago
- ☆60Updated 2 months ago
- DeeperGEMM: crazy optimized version☆69Updated 2 months ago
- ☆87Updated 3 months ago
- High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.☆112Updated 11 months ago
- Compare different hardware platforms via the Roofline Model for LLM inference tasks.☆107Updated last year
- Framework to reduce autotune overhead to zero for well known deployments.☆78Updated last week
- We invite you to visit and follow our new repository at https://github.com/microsoft/TileFusion. TiledCUDA is a highly efficient kernel …☆183Updated 5 months ago
- TensorRT LLM Benchmark Configuration☆13Updated 11 months ago
- This is a demo how to write a high performance convolution run on apple silicon☆54Updated 3 years ago