Best DeepSeek Models to Run Locally
DeepSeek's open-weight models bring chain-of-thought reasoning to local hardware, from a compact 8B R1 distill to the large V4 Flash mixture-of-experts, all MIT licensed.
DeepSeek R1 Distill 8B
DeepSeek · MIT
A distilled version of DeepSeek's R1 reasoning model that inherits chain-of-thought problem-solving capabilities in a compact 8B package. One of the few small models that can reliably work through multi-step math and logic problems without external tooling.
Min VRAM
5 GB
Context
128K
Run with Ollama
ollama run deepseek-r1:8bDeepSeek V4 Flash
DeepSeek · MIT
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.
Min VRAM
~180 GB (Q4)
Context
1M
Run with Ollama
ollama run deepseek-v4-flashWant to mix in more filters?
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