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🚀 Run DeepSeek V4 on more AI GPUs with FlagOS
DeepSeek V4 just dropped with huge specs: 1.6T params, 1M context, MIT license.
But there’s a catch: the official weights use FP4+FP8 mixed precision, which mainly targets NVIDIA Blackwell / B200-class GPUs.
So we built DeepSeek-V4-FlagOS.
On Day 0, the FlagOS community completed multi-chip adaptation across 8 AI hardware platforms:
✅ NVIDIA H100/H20 — FP8/BF16
✅ Huawei Ascend — BF16
✅ Hygon DCU — BF16
✅ MetaX GPU — BF16
✅ Moore Threads MTT S5000 — FP8
✅ Kunlunxin XPU — BF16
✅ T-Head/Alibaba Zhenwu — BF16
✅ Iluvatar GPU — BF16
🔧 What makes it work?
1️⃣ FlagGems operator replacement
DeepSeek V4 operators — MoE routing, Attention, RMSNorm and more — are reimplemented with Triton, reducing dependency on CUDA-specific libraries.
New V4 operators include:
Act Quant, hc_split_sinkhorn, FP8 MatMul, Sparse Attention, Hadamard Transform.
2️⃣ Flexible tensor parallelism
DeepSeek V4 uses o_groups=8, which can limit TP.
We added an independent communication group for o-groups, while allowing the rest of the model to scale to higher TP, enabling deployment on 32GB/64GB cards.
3️⃣ FP4 → BF16 conversion
For hardware without native FP4, we provide ready-to-use BF16 conversion and pre-converted model releases.
📦 Pre-converted models are available on Hugging Face:
V4-Pro:
FlagRelease/DeepSeek-V4-Pro-nvidia-FlagOS
FlagRelease/DeepSeek-V4-Pro-metax-FlagOS
FlagRelease/DeepSeek-V4-Pro-mthreads-FlagOS
FlagRelease/DeepSeek-V4-Pro-hygon-FlagOS
FlagRelease/DeepSeek-V4-Pro-ascend-FlagOS
V4-Flash:
FlagRelease/DeepSeek-V4-Flash-nvidia-FlagOS
FlagRelease/DeepSeek-V4-Flash-zhenwu-FlagOS
FlagRelease/DeepSeek-V4-Flash-kunlunxin-FlagOS
FlagRelease/DeepSeek-V4-Flash-iluvatar-FlagOS
⚡ Performance on NVIDIA H20, V4-Flash FP8:
FlagGems C++ Wrapper + Triton: 70.7 tok/s
DeepSeek TileLang: 62.99 tok/s
That’s 12.24% faster.
👉 Try it here:
https://github.com/flagos-ai/DeepSeek-V4-FlagOS
Open models should run on open infrastructure
DeepSeek V4 just dropped with huge specs: 1.6T params, 1M context, MIT license.
But there’s a catch: the official weights use FP4+FP8 mixed precision, which mainly targets NVIDIA Blackwell / B200-class GPUs.
So we built DeepSeek-V4-FlagOS.
On Day 0, the FlagOS community completed multi-chip adaptation across 8 AI hardware platforms:
✅ NVIDIA H100/H20 — FP8/BF16
✅ Huawei Ascend — BF16
✅ Hygon DCU — BF16
✅ MetaX GPU — BF16
✅ Moore Threads MTT S5000 — FP8
✅ Kunlunxin XPU — BF16
✅ T-Head/Alibaba Zhenwu — BF16
✅ Iluvatar GPU — BF16
🔧 What makes it work?
1️⃣ FlagGems operator replacement
DeepSeek V4 operators — MoE routing, Attention, RMSNorm and more — are reimplemented with Triton, reducing dependency on CUDA-specific libraries.
New V4 operators include:
Act Quant, hc_split_sinkhorn, FP8 MatMul, Sparse Attention, Hadamard Transform.
2️⃣ Flexible tensor parallelism
DeepSeek V4 uses o_groups=8, which can limit TP.
We added an independent communication group for o-groups, while allowing the rest of the model to scale to higher TP, enabling deployment on 32GB/64GB cards.
3️⃣ FP4 → BF16 conversion
For hardware without native FP4, we provide ready-to-use BF16 conversion and pre-converted model releases.
📦 Pre-converted models are available on Hugging Face:
V4-Pro:
FlagRelease/DeepSeek-V4-Pro-nvidia-FlagOS
FlagRelease/DeepSeek-V4-Pro-metax-FlagOS
FlagRelease/DeepSeek-V4-Pro-mthreads-FlagOS
FlagRelease/DeepSeek-V4-Pro-hygon-FlagOS
FlagRelease/DeepSeek-V4-Pro-ascend-FlagOS
V4-Flash:
FlagRelease/DeepSeek-V4-Flash-nvidia-FlagOS
FlagRelease/DeepSeek-V4-Flash-zhenwu-FlagOS
FlagRelease/DeepSeek-V4-Flash-kunlunxin-FlagOS
FlagRelease/DeepSeek-V4-Flash-iluvatar-FlagOS
⚡ Performance on NVIDIA H20, V4-Flash FP8:
FlagGems C++ Wrapper + Triton: 70.7 tok/s
DeepSeek TileLang: 62.99 tok/s
That’s 12.24% faster.
👉 Try it here:
https://github.com/flagos-ai/DeepSeek-V4-FlagOS
Open models should run on open infrastructure
