Tag
DeepSeek-V4.1-Flash is a multimodal Mixture-of-Experts model with 552B backbone parameters and 1M-token context that achieves major KV cache compression gains via CSA2 and FP4 caching, cutting HBM footprint to ~890 bytes per token while boosting agentic performance.
Someone has optimized DeepSeek V4.1 to run faster than the official API on NVIDIA A100 GPUs, despite A100 not supporting FP4 precision, and shared the implementation on GitHub.
XiaomiMiMo releases MiMo-V2.5-Pro-FP4-DFlash, an FP4-quantized MoE model with block-diffusion speculative decoding to reduce memory and bandwidth for trillion-parameter inference.
DeepSeek released the full V4 paper detailing FP4 quantization-aware training, MoE training stability tricks (anticipatory routing and SwiGLU clamping), and a generative reward model for RLHF, achieving dramatic efficiency gains—V4-Flash uses only 10% of V3.2's FLOPs and 7% of its KV cache at 1M context length.