DeepSeek V4 Flash 0731 304B

Overview

DeepSeek's official 0731 build of V4 Flash — a dense 304B model under MIT with a 1M-token context window, tuned for code and reasoning. ~176GB VRAM at Q4. Released July 31, 2026.

When to pick this model

• Long-context code and reasoning workloads that need the full 1M-token window
• Teams that need MIT licensing for unrestricted commercial redistribution
• Multi-GPU inference setups with ~176GB+ VRAM headroom
• Agentic coding pipelines where dense (not MoE) predictability is preferred

VRAM requirements by quantization

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

In practice, DeepSeek V4 Flash 0731 304B is server-class even at Q4_K_M (176 GB). Stepping up to Q8_0 nearly doubles the footprint to 325 GB, and unquantized FP16 weights take 608 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, DeepSeek V4 Flash 0731 304B needs roughly 395 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 1.5 tokens/sec on entry-level GPUs, on the order of 2.5 tokens/sec on a mid-range card, and up to 5 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches DeepSeek V4 Flash 0731 304B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

Which GPU should you buy to run DeepSeek V4 Flash 0731 304B?

To run DeepSeek V4 Flash 0731 304B locally at Q4, you need ~176 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).

Strengths

• 1M-token context window, among the largest available
• Strong code and agentic-reasoning tuning
• Permissive MIT license
• Official DeepSeek release, more polished than the earlier preview

Limitations

• ~176GB VRAM at Q4 puts it well outside consumer hardware
• Local throughput is slow (~5 tok/s at Q4)
• No official Ollama tag — requires manual HuggingFace setup

Typical workloads

In our catalog grid, DeepSeek V4 Flash 0731 304B is filed under Code, Agentic Reasoning, Long Context 1M — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline); multi-step reasoning and math-flavoured tasks.

The 1024k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The MIT license is permissive, so shipping it inside a commercial product raises no special legal questions.

Architecture & training

Architecture: Dense transformer, 304B parameters · 1,048,576-token context window (1M)

Training: Official DeepSeek V4 Flash release (build 0731), succeeding the preview. Training details not published.

Quick start

# HuggingFace : deepseek-ai/DeepSeek-V4-Flash-0731

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

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Frequently asked questions

How much VRAM does DeepSeek V4 Flash 0731 304B need?

At the recommended Q4_K_M quantization, DeepSeek V4 Flash 0731 304B needs about 176 GB of VRAM. Q8_0 takes 325 GB, and unquantized FP16 weights take 608 GB.

Can DeepSeek V4 Flash 0731 304B run without a GPU?

Yes — with roughly 395 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does DeepSeek V4 Flash 0731 304B support?

DeepSeek V4 Flash 0731 304B supports a 1024k-token context window (1,048,576 tokens).

Can I use DeepSeek V4 Flash 0731 304B commercially?

Yes. DeepSeek V4 Flash 0731 304B is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.

How fast is DeepSeek V4 Flash 0731 304B on consumer hardware?

Our compatibility engine estimates on the order of 2.5 tokens/sec on a mid-range GPU and up to 5 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of DeepSeek V4 Flash 0731 304B should I download first?

Start with Q4_K_M (176 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.