DeepSeek R1 Distill Qwen 1.5B

• Cheapest

  NVIDIA GeForce RTX 3060 12GB

  79tok/sest.Q8_0

  309 €

• Best value

  NVIDIA GeForce RTX 3080 10GB

  166tok/sest.Q8_0

  500 €

• Fastest

  NVIDIA GeForce RTX 5090

  392tok/sest.Q8_0

  6.028 €

• Mac

  Mac mini M4 16GB

  28tok/sest.Q8_0

  800 €

• mlx-3bit
  • LM Studiolms get mlx-community/DeepSeek-R1-Distill-Qwen-1.5B-3bit
  • MLXmlx_lm.server --model mlx-community/DeepSeek-R1-Distill-Qwen-1.5B-3bit
• mlx-4bit
  • LM Studiolms get mlx-community/DeepSeek-R1-Distill-Qwen-1.5B-4bit
  • MLXmlx_lm.server --model mlx-community/DeepSeek-R1-Distill-Qwen-1.5B-4bit
• Q4_K_M
  • Ollamaollama run hf.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q4_K_M
  • LM Studiolms get unsloth/DeepSeek-R1-Distill-Qwen-1.5B-GGUF@q4_k_m
  • llama.cppllama-server -hf unsloth/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q4_K_M
• Q5_K_M
  • Ollamaollama run hf.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q5_K_M
  • LM Studiolms get unsloth/DeepSeek-R1-Distill-Qwen-1.5B-GGUF@q5_k_m
  • llama.cppllama-server -hf unsloth/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q5_K_M
• mlx-6bit
  • LM Studiolms get mlx-community/DeepSeek-R1-Distill-Qwen-1.5B-6bit
  • MLXmlx_lm.server --model mlx-community/DeepSeek-R1-Distill-Qwen-1.5B-6bit
• Q6_K
  • Ollamaollama run hf.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q6_K
  • LM Studiolms get unsloth/DeepSeek-R1-Distill-Qwen-1.5B-GGUF@q6_k
  • llama.cppllama-server -hf unsloth/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q6_K
• mlx-8bit
  • LM Studiolms get mlx-community/DeepSeek-R1-Distill-Qwen-1.5B-8bit
  • MLXmlx_lm.server --model mlx-community/DeepSeek-R1-Distill-Qwen-1.5B-8bit
• Q8_0
  • Ollamaollama run hf.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q8_0
  • LM Studiolms get unsloth/DeepSeek-R1-Distill-Qwen-1.5B-GGUF@q8_0
  • llama.cppllama-server -hf unsloth/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q8_0

Distilled from DeepSeek-R1 onto Qwen2.5-Math-1.5B (Qwen2.5 originally Apache 2.0); the card recommends temperature 0.6, no system prompt, and forcing responses to start with "<think>\n".

How much VRAM does DeepSeek R1 Distill Qwen 1.5B need?

At mlx-3bit, DeepSeek R1 Distill Qwen 1.5B needs about 2 GB of memory at 8k context and 2 GB at 32k, counting the weights, the KV cache and runtime buffers.

What is the best GPU for running DeepSeek R1 Distill Qwen 1.5B locally?

The cheapest hardware in Germany that runs DeepSeek R1 Distill Qwen 1.5B fully in memory at 32k context is the NVIDIA GeForce RTX 3060 12GB, 309 € at Amazon, at about 79 tok/s (estimated). The best value is the NVIDIA GeForce RTX 3080 10GB (about 166 tok/s for 500 €). The fastest we price is the NVIDIA GeForce RTX 5090, at about 392 tok/s.

Can I run DeepSeek R1 Distill Qwen 1.5B on a 16 GB GPU?

Yes: at Q8_0 it fits fully in 16 GB at 32k context. On the NVIDIA GeForce RTX 5080 it runs at about 210 tok/s (estimated).

Can I run DeepSeek R1 Distill Qwen 1.5B on a 24 GB GPU?

Yes: at Q8_0 it fits fully in 24 GB at 32k context. On the NVIDIA GeForce RTX 3090 Ti it runs at about 221 tok/s (estimated).

How do I run DeepSeek R1 Distill Qwen 1.5B locally?

With Ollama: ollama run hf.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B-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 1.5B released under?

MIT License, which allows commercial use.