FlashMLA
Faster LLM Inference on Hopper GPUs
Listed in categories:
Artificial IntelligenceGitHubOpen Source
Description
FlashMLA is an efficient MLA decoding kernel designed specifically for Hopper GPUs, optimized for handling variable-length sequences. It achieves remarkable performance metrics, including up to 3000 GB/s in memory-bound configurations and 580 TFLOPS in computation-bound configurations, making it a powerful tool for machine learning applications.
How to use FlashMLA?
To use FlashMLA, install the package using 'python setup.py install', then import it in your Python script. You can benchmark its performance with provided test scripts and utilize its features for efficient MLA decoding.
Core features of FlashMLA:
1️⃣
Efficient MLA decoding for Hopper GPUs
2️⃣
Optimized for variable-length sequences
3️⃣
High performance with up to 3000 GB/s memory bandwidth
4️⃣
Supports BF16 and FP16 formats
5️⃣
Integration with PyTorch for seamless usage
Why could be used FlashMLA?
| # | Use case | Status | |
|---|---|---|---|
| # 1 | Machine learning model inference on Hopper GPUs | ✅ | |
| # 2 | Real-time processing of variable-length sequences | ✅ | |
| # 3 | Benchmarking performance of decoding kernels | ✅ | |
Who developed FlashMLA?
FlashMLA is developed by Jiashi Li and is inspired by the FlashAttention and Cutlass projects. It is hosted on GitHub and is part of the open-source community, allowing users to contribute and enhance its capabilities.