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DeepGEMM Update Confirms mHC and Blackwell for V4

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๐Ÿฆ™Read original on Reddit r/LocalLLaMA
#matrix-mult#gpu-optimization#low-precisiondeepgemmdeepseekdeepgemmmhcblackwell

๐Ÿ’กDeepGEMM update unlocks mHC + Blackwell/FP4 for V4โ€”infra boost for open models

โšก 30-Second TL;DR

What Changed

Integrates Manifold-constrained Hyper-connection (mHC)

Why It Matters

Enables faster inference on next-gen GPUs, boosting open-source efficiency. Positions DeepSeek ahead in hardware-software co-design for V4.

What To Do Next

Pull latest DeepGEMM from GitHub and benchmark mHC on Blackwell sims.

Who should care:Researchers & Academics

Key Points

  • โ€ขIntegrates Manifold-constrained Hyper-connection (mHC)
  • โ€ขAdds NVIDIA Blackwell SM100 support
  • โ€ขImplements FP4 ultra-low precision computing
  • โ€ขSignals DeepSeek V4 hardware optimizations

๐Ÿง  Deep Insight

Background and context from public sources โ€” not the original article. 4 sources cited.

๐Ÿ”‘ Enhanced Key Takeaways

  • โ€ขDeepGEMM requires NVIDIA SM90 or SM100 GPUs, Python 3.8+, C++20 compilers, and CUDA 12.3+ for SM90 support[4].
  • โ€ขRecent GitHub activity includes multiple CI builds for wheel deployment and fixes for pre-built wheels as of early 2026[4].
  • โ€ขmHC integration leverages CUTLASS/CUTE implementations for persistent batched GEMM operations on Blackwell[1][2][3].

๐Ÿ› ๏ธ Technical Deep Dive

  • โ€ขBlock-scaled GEMM + amax on SM100 supports FP4/FP8 inputs with per-block scale factors SFA/SFB, dequantizing along K dimension, outputting full C and global amax[1].
  • โ€ขGEMM + SwiGLU fusion on SM100 offers quantized block-scaled mode for FP4/FP8 with tile shapes like mma_tiler_mn=(128,128) and cluster_shape_mn=(1,1)[2].
  • โ€ขBlackwell SM100 introduces tcgen05.mma instructions, 2x-4x faster than Hopper WGMMA, supporting block-scaled MMA for mxf4nvf4 with tile shapes like 128x128x128 or 256x256x128[3].

๐Ÿ”ฎ Future ImplicationsAI analysis grounded in cited sources

DeepSeek V4 will achieve 2x-4x GEMM speedup on Blackwell vs Hopper
tcgen05.mma instructions in SM100 provide this performance uplift over Hopper WGMMA for low-precision block-scaled operations[3].
DeepGEMM enables FP4 training/inference for V4 on SM100
Library adds early FP4 support integrated with cuDNN Frontend's block-scaled GEMM fusions for Blackwell[1][4].

โณ Timeline

2025-12
DeepGEMM initial release for SM90 FP8 GEMM kernels
2026-01
CI fixes and wheel builds added to DeepGEMM repo
2026-02
Update integrates mHC, Blackwell SM100, and FP4 support

๐Ÿ“Ž Sources (4)

Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.

  1. docs.nvidia.com โ€” Gemm Amax
  2. docs.nvidia.com โ€” Gemm Swiglu
  3. docs.nvidia.com โ€” Blackwell Functionality
  4. GitHub โ€” Deepgemm
๐Ÿฆ™Read original article on Reddit r/LocalLLaMA
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