DeepSeek V4 Flash
DeepSeek · DeepSeek
The faster, more practical sibling of the 1.6-trillion-parameter DeepSeek V4 Pro. At 284B total it activates only ~13B parameters per token, so once loaded it generates far faster than its size suggests — but even at 4-bit it needs roughly 180 GB of memory, putting it in 256 GB+ unified-memory Mac or multi-GPU server territory. MIT licensed, with a 1M-token context window.
Params
284B (MoE, ~13B active)
License
MIT
Context
1M
Min VRAM
~180 GB (Q4)
Min RAM
256 GB
Tier
Giant
Run locally with Ollama
ollama run deepseek-v4-flashOnce running, Ollama exposes an OpenAI-compatible endpoint at localhost:11434/v1.
Will it run on my hardware?
DeepSeek V4 Flash needs about ~180 GB (Q4) of fast memory at Q4. Here is what in our hardware database clears that bar.
None of our tracked consumer hardware has enough memory for this model — it is server / datacenter territory.
15 other tracked configurations do not have enough memory.
Hardware links are affiliate links. We earn a small commission if you buy through them — at no extra cost to you. Disclosure
Specifications
| Parameters | 284B (MoE, ~13B active) |
|---|---|
| Active params (MoE) | ~13B active per token |
| Context window | 1M |
| License | MIT |
| Min VRAM (Q4) | ~180 GB (Q4) |
| Min RAM | 256 GB |
| Recommended tier | Giant |
| Best for | codinganalysisreasoningchat |
Variants on Ollama
Ollama serves the Q4_K_M quantization by default. Higher-precision tags exist for users with more memory.
DeepSeek V4 Flash
ollama run deepseek-v4-flashollama pull deepseek-v4-flash:q8_0ollama pull deepseek-v4-flash:fp16Exact quantization tags vary per model — run ollama show deepseek-v4-flash or check the tags page for the full list.
Popularity trend (illustrative)
Illustrative adoption trend, not actual download counts. We do not publish a download number for any model because we have no honest source for it.
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