A Reddit user reported that DeepSeek V4 relies on custom GPU kernels designed for datacenter Blackwell (SM100), causing...
A Reddit user reported that DeepSeek V4 relies on custom GPU kernels designed for datacenter Blackwell (SM100), causing it to fall back to dense attention or CPU execution on consumer RTX PRO 6000 GPUs, resulting in very low token throughput (14 t/s) at large context (source: Reddit). The user notes that no GGUF for DeepSeek V4 flash has been shipped by Unsloth, likely due to this kernel issue. This suggests a software ecosystem gap that hampers DeepSeek model adoption on high-end consumer NVIDIA hardware, potentially reducing competitive positioning versus models like Mimo 2.5 that use more broadly compatible attention mechanisms.
signal brief
A Reddit user reported that DeepSeek V4 relies on custom GPU kernels designed for datacenter Blackwell (SM100), causing it to fall back to dense attention or CPU execution on consumer RTX PRO 6000 GPUs, resulting in very low token throughput (14 t/s) at large context (source: Reddit). The user notes that no GGUF for DeepSeek V4 flash has been shipped by Unsloth, likely due to this kernel issue. This suggests a software ecosystem gap that hampers DeepSeek model adoption on high-end consumer NVIDIA hardware, potentially reducing competitive positioning versus models like Mimo 2.5 that use more broadly compatible attention mechanisms.
evidence
- https://www.reddit.com/r/LocalLLaMA/comments/1udwabh/mimo_25_is_fast_at_large_context_dual_rtx_pro_6000/reddit
- https://developer.nvidia.com/blog/maximize-ai-factory-energy-efficiency-through-full-stack-inference-and-training-optimizations/web
- https://manifold.markets/AndrewPeterson960c/did-deepseek-lie-about-the-number-oweb
Decision support, not stock advice. This signal is research with cited evidence — not a recommendation to buy, sell, or hold any security.