DeepLink-org / AIChipBenchmark
☆26Updated 2 weeks ago
Alternatives and similar repositories for AIChipBenchmark:
Users that are interested in AIChipBenchmark are comparing it to the libraries listed below
- ☆139Updated last year
- ☆58Updated 5 months ago
- ☆148Updated 3 months ago
- An unofficial cuda assembler, for all generations of SASS, hopefully :)☆82Updated 2 years ago
- ☆36Updated 6 months ago
- AI Accelerator Benchmark focuses on evaluating AI Accelerators from a practical production perspective, including the ease of use and ver…☆236Updated last week
- ☆90Updated 3 weeks ago
- ☆127Updated 4 months ago
- ☆92Updated 7 months ago
- A benchmark suited especially for deep learning operators☆42Updated 2 years ago
- play gemm with tvm☆90Updated last year
- 使用 CUDA C++ 实现的 llama 模型推理框架☆50Updated 5 months ago
- A standalone GEMM kernel for fp16 activation and quantized weight, extracted from FasterTransformer☆91Updated 3 weeks ago
- ☆122Updated last year
- 📚FFPA(Split-D): Yet another Faster Flash Attention with O(1) GPU SRAM complexity large headdim, 1.8x~3x↑🎉 faster than SDPA EA.☆169Updated 3 weeks ago
- Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.☆35Updated 2 months ago
- The DeepSpark open platform selects hundreds of open source application algorithms and models that are deeply coupled with industrial app…☆42Updated 2 weeks ago
- llm theoretical performance analysis tools and support params, flops, memory and latency analysis.☆86Updated 3 months ago
- 使用 cutlass 仓库在 ada 架构上实现 fp8 的 flash attention☆63Updated 8 months ago
- DeepSparkHub selects hundreds of application algorithms and models, covering various fields of AI and general-purpose computing, to suppo…☆63Updated this week
- Tutorials for writing high-performance GPU operators in AI frameworks.☆130Updated last year
- A tutorial for CUDA&PyTorch☆137Updated 3 months ago
- ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.☆73Updated 3 weeks ago
- Compare different hardware platforms via the Roofline Model for LLM inference tasks.☆97Updated last year
- ☆20Updated 4 years ago
- ☆7Updated last year
- code reading for tvm☆76Updated 3 years ago
- ☆49Updated this week
- NART = NART is not A RunTime, a deep learning inference framework.☆38Updated 2 years ago
- ☆30Updated last year