Flash MLA Offical Github Repo: FlashMLA - deepseek-ai - Github
DeepSeek Official Anouncement of Flash MLA on X:
Hacker News Discussion: DeepSeek Open Source FlashMLA – MLA Decoding Kernel for Hopper GPUs | Hacker News
Deepseek Open Source week series
Day 1: Flash MLA
🚀 Day 1 of #OpenSourceWeek: FlashMLA
Honored to share FlashMLA - our efficient MLA decoding kernel for Hopper GPUs, optimized for variable-length sequences and now in production.
✅ BF16 support
✅ Paged KV cache (block size 64)
⚡ 3000 GB/s memory-bound & 580 TFLOPS compute-bound on H800🔗 Explore on GitHub: https://github.com/deepseek-ai/FlashMLA
Day 2: DeepEP
🚀 Day 2 of #OpenSourceWeek: DeepEP
Excited to introduce DeepEP - the first open-source EP communication library for MoE model training and inference.
✅ Efficient and optimized all-to-all communication
✅ Both intranode and internode support with NVLink and RDMA
✅ High-throughput kernels for training and inference prefilling
✅ Low-latency kernels for inference decoding
✅ Native FP8 dispatch support
✅ Flexible GPU resource control for computation-communication overlapping🔗 GitHub: https://github.com/deepseek-ai/DeepEP
Day 3: DeepGEMM
🚀 Day 3 of #OpenSourceWeek: DeepGEMM
Introducing DeepGEMM - an FP8 GEMM library that supports both dense and MoE GEMMs, powering V3/R1 training and inference.
⚡ Up to 1350+ FP8 TFLOPS on Hopper GPUs
✅ No heavy dependency, as clean as a tutorial
✅ Fully Just-In-Time compiled
✅ Core logic at ~300 lines - yet outperforms expert-tuned kernels across most matrix sizes
✅ Supports dense layout and two MoE layouts🔗 GitHub: https://github.com/deepseek-ai/DeepGEMM
Day 4: Optimized Parallelism Strategies
🚀 Day 4 of #OpenSourceWeek: Optimized Parallelism Strategies
✅ DualPipe - a bidirectional pipeline parallelism algorithm for computation-communication overlap in V3/R1 training.
🔗 https://github.com/deepseek-ai/DualPipe✅ EPLB - an expert-parallel load balancer for V3/R1.
🔗 https://github.com/deepseek-ai/eplb📊 Analyze computation-communication overlap in V3/R1.
🔗 https://github.com/deepseek-ai/profile-data
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