DeepSeek-Flash

About

What DeepSeek-Flash is

Model Overview Description: The NVIDIA DeepSeek-V4-Flash-NVFP4 model is a quantized version of DeepSeek AI's DeepSeek-V4-Flash model, an autoregressive Mixture-of-Experts language model that uses an optimized Transformer architecture with hybrid attention (Compressed Sparse Attention and Heavily Compressed Attention) and Manifold-Constrained Hyper-Connections. For more information, refer to the DeepSeek-V4-Flash model card. The NVIDIA DeepSeek-V4-Flash-NVFP4 model is quantized with Model Optimizer. This model is ready for commercial/non-commercial use. <br Third-Party Community Consideration This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (DeepSeek-V4-Flash) Model Card. References Nvidia Model Optimizer: https://github.com/NVIDIA/Model-Optimizer License/Terms of Use: MIT Deployment Geography: Global <br Use Case: DeepSeek V4 is well-suited for advanced reasoning, agentic AI applications, tool use scenarios, and complex problem-solving in domains such as mathematics, software engineering, and enterprise AI assistants. <br Release Date: Hugging Face 05/28/2026 via https://huggingface.co/nvidia/DeepSeek-V4-Flash-NVFP4 <br Model Architecture: Architecture Type: Transformers <br Network Architecture: Mixture-of-Experts (MoE) with Hybrid Attention (Compressed Sparse Attention + Heavily Compressed Attention) <br Number of Model Parameters: 284B in total and 13B activated <br Input: Input Type(s): Text <br Input Format(s): String <br Input Parameters: 1D (One-Dimensional): Sequences <br Other Properties Related to Input: Supports multi-turn conversations with system prompts, user messages, and assistant responses. Maximum context length of 1 million tokens. Uses a custom encoding pipeline (encodingdsv4) with three reasoning modes: Non-think (fast), Think High (logical analysis), and Think Max (full reasoning extent). <br Output: Output Type(s): Text <br Output Format: String <br Output Parameters: One-Dimensional (1D): Sequences <br Other Properties Related to Output: Supports structured JSON output, function/tool calling, and reasoning conte

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

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Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys nvidia-deepseek-flash for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (nvidia-deepseek-flash below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"nvidia-deepseek-flash","messages":[{"role":"user","content":"Hello"}]}'

Details

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Licence

Open weights

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