DeepSeek kicked off OpenSourceWeek by releasing FlashMLA, an efficient MLA decoding kernel specifically designed for Hopper GPUs. It optimizes variable-length sequence services and reduces the KV Cache during inference. The open-source release of FlashMLA quickly gained massive attention, reaching over 400 stars in just 45 minutes!
This technology offers extremely fast speeds (3000 GB/s memory bandwidth and 580 TFLOPS computational performance on H800 SXM5 GPUs) and significantly reduces inference costs. Whether you’re an AI developer or a machine learning enthusiast, FlashMLA promises to deliver unprecedented performance improvements.
Project Link: FlashMLA on GitHub
🔧 Key Features:
Optimized for Hopper GPUs
BF16 paging KV Cache
3000 GB/s in memory-bound configurations, 580 TFLOPS in compute-bound configurations
💻 Quick Start:
Install with python setup.py install
Run benchmarks with python tests/test_flash_mla.py
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#DeepSeek #FlashMLA #InferenceAcceleration #OpenSource #MachineLearning #HopperGPU #AITechnology #GitHub #AIDevelopment #InferenceOptimization #OpenSourceTech #CUDA #PyTorch
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