DeepSeek R1 Distill Qwen 7B
7.6B parameters · 128k context · MIT License · released 2025-01-20
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In short
DeepSeek R1 Distill Qwen 7B is an open model from DeepSeek with 7.6B parameters. At mlx-3bit, DeepSeek R1 Distill Qwen 7B needs about 4 GB of memory at 8k context and 12 GB at 128k, counting the weights, the KV cache and runtime buffers. The cheapest hardware in Canada that runs DeepSeek R1 Distill Qwen 7B fully in memory at 128k context is the Intel Arc A770 16GB, $655 at Amazon, at about 20 tok/s (estimated).
Updated . Speeds are estimates; how they are worked out.
What to run it on in Canada
Fully in memory at 128k context, priced at retailers today.
Cheapest
Intel Arc A770 16GB20tok/sest.Q6_K
$655
Best value
Intel Arc A770 16GB20tok/sest.Q6_K
$655
Fastest
NVIDIA RTX PRO 6000 Blackwell71tok/sest.Q8_0
$3,133
All hardware that runs it
Everything we price, best value first. Two cards split the model by layers.
1 other card or machine we price cannot hold it at this context.
Memory needed
Weights, KV cache and runtime buffers at each context length (KV cache at fp16).
| Quant | File | @8k | @32k | @64k | @128k |
|---|---|---|---|---|---|
| mlx-3bitmlx | 3.1 GB | 4.3 GB | 5.7 GB | 7.7 GB | 11.6 GB |
| mlx-4bitmlx | 4.0 GB | 5.2 GB | 6.6 GB | 8.6 GB | 12.4 GB |
| Q4_K_Mgguf | 4.4 GB | 5.5 GB | 7.0 GB | 8.9 GB | 12.8 GB |
| Q5_K_Mgguf | 5.1 GB | 6.3 GB | 7.7 GB | 9.6 GB | 13.5 GB |
| mlx-6bitmlx | 5.8 GB | 6.9 GB | 8.4 GB | 10.3 GB | 14.2 GB |
| Q6_Kgguf | 5.8 GB | 7.0 GB | 8.5 GB | 10.4 GB | 14.3 GB |
| mlx-8bitmlx | 7.5 GB | 8.7 GB | 10.2 GB | 12.1 GB | 16.0 GB |
| Q8_0gguf | 7.5 GB | 8.7 GB | 10.2 GB | 12.1 GB | 16.0 GB |
Run it
Copy a command for the runtime you use.
- mlx-3bit
- LM Studio
lms get mlx-community/DeepSeek-R1-Distill-Qwen-7B-3bit - MLX
mlx_lm.server --model mlx-community/DeepSeek-R1-Distill-Qwen-7B-3bit
- LM Studio
- mlx-4bit
- LM Studio
lms get mlx-community/DeepSeek-R1-Distill-Qwen-7B-4bit - MLX
mlx_lm.server --model mlx-community/DeepSeek-R1-Distill-Qwen-7B-4bit
- LM Studio
- Q4_K_M
- Ollama
ollama run hf.co/unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M - LM Studio
lms get unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF@q4_k_m - llama.cpp
llama-server -hf unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M
- Ollama
- Q5_K_M
- Ollama
ollama run hf.co/unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q5_K_M - LM Studio
lms get unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF@q5_k_m - llama.cpp
llama-server -hf unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q5_K_M
- Ollama
- mlx-6bit
- LM Studio
lms get mlx-community/DeepSeek-R1-Distill-Qwen-7B-6bit - MLX
mlx_lm.server --model mlx-community/DeepSeek-R1-Distill-Qwen-7B-6bit
- LM Studio
- Q6_K
- Ollama
ollama run hf.co/unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q6_K - LM Studio
lms get unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF@q6_k - llama.cpp
llama-server -hf unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q6_K
- Ollama
- mlx-8bit
- LM Studio
lms get mlx-community/DeepSeek-R1-Distill-Qwen-7B-8bit - MLX
mlx_lm.server --model mlx-community/DeepSeek-R1-Distill-Qwen-7B-8bit
- LM Studio
- Q8_0
- Ollama
ollama run hf.co/unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q8_0 - LM Studio
lms get unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF@q8_0 - llama.cpp
llama-server -hf unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q8_0
- Ollama
Notes
Distilled from DeepSeek-R1 onto Qwen2.5-Math-7B (Qwen2.5 series is Apache 2.0); the card licenses the weights under MIT. Use temperature 0.5-0.7, no system prompt, and start output with "<think>\n".
Questions
- How much VRAM does DeepSeek R1 Distill Qwen 7B need?
- At mlx-3bit, DeepSeek R1 Distill Qwen 7B needs about 4 GB of memory at 8k context and 12 GB at 128k, counting the weights, the KV cache and runtime buffers.
- What is the best GPU for running DeepSeek R1 Distill Qwen 7B locally?
- The cheapest hardware in Canada that runs DeepSeek R1 Distill Qwen 7B fully in memory at 128k context is the Intel Arc A770 16GB, $655 at Amazon, at about 20 tok/s (estimated). The fastest we price is the NVIDIA RTX PRO 6000 Blackwell, at about 71 tok/s.
- Can I run DeepSeek R1 Distill Qwen 7B on a 16 GB GPU?
- Yes: at Q6_K it fits fully in 16 GB at 128k context. On the NVIDIA GeForce RTX 5080 it runs at about 43 tok/s (estimated).
- Can I run DeepSeek R1 Distill Qwen 7B on a 24 GB GPU?
- Yes: at Q8_0 it fits fully in 24 GB at 128k context. On the NVIDIA GeForce RTX 3090 Ti it runs at about 40 tok/s (estimated).
- How do I run DeepSeek R1 Distill Qwen 7B locally?
- With Ollama: ollama run hf.co/unsloth/DeepSeek-R1-Distill-Qwen-7B-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 7B released under?
- MIT License, which allows commercial use.