DeepSeek R1 Distill Qwen 14B

• Cheapest

  NVIDIA RTX PRO 6000 Blackwell

  27tok/sest.Q8_0

  19.076 €

• Best value

  NVIDIA RTX PRO 6000 Blackwell

  27tok/sest.Q8_0

  19.076 €

• Fastest

  NVIDIA RTX PRO 6000 Blackwell

  27tok/sest.Q8_0

  19.076 €

2 other cards and machines we price cannot hold it at this context.

• mlx-3bit
  • LM Studiolms get mlx-community/DeepSeek-R1-Distill-Qwen-14B-3bit
  • MLXmlx_lm.server --model mlx-community/DeepSeek-R1-Distill-Qwen-14B-3bit
• mlx-4bit
  • LM Studiolms get mlx-community/DeepSeek-R1-Distill-Qwen-14B-4bit
  • MLXmlx_lm.server --model mlx-community/DeepSeek-R1-Distill-Qwen-14B-4bit
• Q4_K_M
  • Ollamaollama run hf.co/unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q4_K_M
  • LM Studiolms get unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF@q4_k_m
  • llama.cppllama-server -hf unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q4_K_M
• Q5_K_M
  • Ollamaollama run hf.co/unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q5_K_M
  • LM Studiolms get unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF@q5_k_m
  • llama.cppllama-server -hf unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q5_K_M
• mlx-6bit
  • LM Studiolms get mlx-community/DeepSeek-R1-Distill-Qwen-14B-6bit
  • MLXmlx_lm.server --model mlx-community/DeepSeek-R1-Distill-Qwen-14B-6bit
• Q6_K
  • Ollamaollama run hf.co/unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q6_K
  • LM Studiolms get unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF@q6_k
  • llama.cppllama-server -hf unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q6_K
• mlx-8bit
  • LM Studiolms get mlx-community/DeepSeek-R1-Distill-Qwen-14B-8bit
  • MLXmlx_lm.server --model mlx-community/DeepSeek-R1-Distill-Qwen-14B-8bit
• Q8_0
  • Ollamaollama run hf.co/unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q8_0
  • LM Studiolms get unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF@q8_0
  • llama.cppllama-server -hf unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q8_0

Distilled from DeepSeek-R1 onto Qwen2.5-14B (originally Apache 2.0); card says to avoid system prompts and use temperature 0.6. Card recommends forcing output to start with "<think>\n".

How much VRAM does DeepSeek R1 Distill Qwen 14B need?

At mlx-3bit, DeepSeek R1 Distill Qwen 14B needs about 8 GB of memory at 8k context and 31 GB at 128k, counting the weights, the KV cache and runtime buffers.

What is the best GPU for running DeepSeek R1 Distill Qwen 14B locally?

The cheapest hardware in Germany that runs DeepSeek R1 Distill Qwen 14B fully in memory at 128k context is the NVIDIA RTX PRO 6000 Blackwell, 19.076 € at eBay, at about 27 tok/s (estimated).

Can I run DeepSeek R1 Distill Qwen 14B on a 16 GB GPU?

Only partly: at 128k context it does not fit in 16 GB, so part of it runs from system RAM and it slows to about 2 tok/s on the AMD Radeon RX 9060 XT 16GB (estimated).

Can I run DeepSeek R1 Distill Qwen 14B on a 24 GB GPU?

Only partly: at 128k context it does not fit in 24 GB, so part of it runs from system RAM and it slows to about 3 tok/s on the NVIDIA Tesla P40 (estimated).

How do I run DeepSeek R1 Distill Qwen 14B locally?

With Ollama: ollama run hf.co/unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q4_K_M (the Q4_K_M build). LM Studio and llama.cpp commands are on this page.

What licence is DeepSeek R1 Distill Qwen 14B released under?

MIT License, which allows commercial use.