DeepSeek
DeepSeek LLM 67B (67B parameters) requires approximately 48.2 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 56 GB of VRAM.
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— copy & paste to run locallyCopy-paste commands to run DeepSeek LLM 67B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "deepseek-ai/deepseek-llm-67b-chat" \
--hf-file "deepseek-llm-67b-chat-Q4_K_M.gguf" \
-c 4096 -ngl 99Quick specs
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Quantization options
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 26.1 GB | Low | — |
Q3_K_S | 3 | 32.8 GB | Low | — |
NVFP4 | 4 | 37.5 GB | Medium | — |
Q4_K_M | 4 | 40.9 GB | Medium | — |
Q5_K_M | 5 | 48.2 GB | High | — |
Q6_K | 6 | 54.9 GB | High | — |
Q8_0 | 8 | 71.7 GB | Very High | — |
F16 | 16 | 137.4 GB | Maximum | — |
Quality benchmarks
Coding
Reasoning
General
Source: official · 2024-01-05
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
DeepSeek LLM 67B (67B parameters) requires approximately 48.2 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, MacBook Pro M4 Max 96GB can run DeepSeek LLM 67B with a compatibility score of 58/100. It provides 96 GB of memory and achieves approximately 16.0 tokens per second.
The recommended quantization for DeepSeek LLM 67B is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for DeepSeek LLM 67B: NVIDIA H100 80GB (score: 66/100), NVIDIA H800 80GB (score: 65/100), NVIDIA A100 80GB (score: 64/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, DeepSeek LLM 67B is well-suited for chat as well as reasoning. It was designed with these use cases in mind.
See also
