DeepSeek-V4 Latent Reasoning – moving "thinking" into latent space
DeepSeek-V4-Flash-0731-Latent-Reasoning. A self-contained model that does thinking in latent space, NVFP4-quantized, with a production vllm form for serving runtime. https://huggingface.co/nmitchko/De
In plain words
DeepSeek-V4-Flash-0731-Latent-Reasoning is an open-source language model that performs reasoning internally within latent space rather than exposing thinking tokens in outputs. Built on the DeepSeek-V4-Flash backbone and quantized in NVFP4 format, it is designed for developers and researchers who need efficient inference. The model comes production-ready with a vLLM serving implementation, enabling real-time deployment without separate thinking token processing.
written from the facts on this page · September 2026
From the sources
DeepSeek-V4-Flash-0731-Latent-Reasoning. A self-contained model that does thinking in latent space, NVFP4-quantized, with a production vllm form for serving runtime. https://huggingface.co/nmitchko/DeepSeek-V4-Flash-0731-Latent-Reasoning The goal of this model is to employ latent thinking in an open source model. The idea is to compress thinking tokens and keep them within latent forms not in the output emission. The DeepSeek-V4-Flash-0731 backbone . Roughly 79 GiB of weights per GPU at TP=2 (158–164 GiB total). The DSpark draft block (3 layers), preserved from the source. The trained latent reasoning head . 35.7M params, loaded from a single latent_reasoning_head.safetensors (~152 MB). BBH…from blog.n.ichol.ai
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