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 the UK that runs DeepSeek R1 Distill Qwen 7B fully in memory at 128k context is the NVIDIA GeForce RTX 5060 Ti 16GB, £550 at eBay, at about 20 tok/s (estimated).
Updated . Speeds are estimates; how they are worked out.
What to run it on in the UK
Fully in memory at 128k context, priced at retailers today.
Cheapest
NVIDIA GeForce RTX 5060 Ti 16GB20tok/sest.Q6_K
£550
Best value
AMD Radeon RX 7800 XT25tok/sest.Q6_K
£568
Fastest
NVIDIA GeForce RTX 509071tok/sest.Q8_0
£5,000
Mac
Mac Studio M4 Max 64GB23tok/sest.Q8_0
£3,399
All hardware that runs it
Everything we price, best value first. Two cards split the model by layers.
| Hardware | Quant | Fits | Decode | Price | tok/s per 1,000 |
|---|---|---|---|---|---|
| NVIDIA Tesla P40 | Q8_0 | fits | 14 tok/s | £261 | 52.6 |
| AMD Radeon RX 7600 XT | Q6_K | fits | 12 tok/s | £348 | 33.0 |
| AMD Radeon RX 9060 XT 16GB | Q6_K | fits | 13 tok/s | £499 | 25.7 |
| NVIDIA GeForce RTX 5060 Ti 16GB | Q6_K | fits | 20 tok/s | £550 | 36.7 |
| AMD Radeon RX 7800 XT | Q6_K | fits | 25 tok/s | £568 | 43.8 |
| Intel Arc A770 16GB | Q6_K | fits | 20 tok/s | £588 | 34.5 |
| AMD Radeon RX 9070 XT | Q6_K | fits | 26 tok/s | £670 | 38.2 |
| Intel Arc Pro B60 | Q8_0 | fits | 15 tok/s | £742 | 19.7 |
| AMD Radeon RX 7900 XT | Q8_0 | fits | 28 tok/s | £789 | 35.7 |
| NVIDIA GeForce RTX 4070 Ti Super | Q6_K | fits | 30 tok/s | £950 | 31.9 |
| NVIDIA GeForce RTX 5070 Ti | Q6_K | fits | 40 tok/s | £975 | 41.3 |
| NVIDIA GeForce RTX 4060 Ti 16GB | Q6_K | fits | 13 tok/s | £977 | 13.3 |
| AMD Radeon RX 7900 XTX | Q8_0 | fits | 34 tok/s | £999 | 33.8 |
| NVIDIA GeForce RTX 5080 | Q6_K | fits | 43 tok/s | £1,199 | 36.0 |
| Mac mini M4 32GB | Q8_0 | fits | 5 tok/s | £1,200 | 4.2 |
| NVIDIA GeForce RTX 4080 Super | Q6_K | fits | 33 tok/s | £1,429 | 23.2 |
| AMD Radeon AI PRO R9700 | Q8_0 | fits | 23 tok/s | £1,674 | 13.4 |
| NVIDIA GeForce RTX 3090 | Q8_0 | fits | 37 tok/s | £1,707 | 21.8 |
| NVIDIA GeForce RTX 3090 Ti | Q8_0 | fits | 40 tok/s | £2,199 | 18.2 |
| Mac mini M4 Pro 48GB | Q8_0 | fits | 11 tok/s | £3,000 | 3.8 |
| GMKtec EVO-X2 Ryzen AI Max+ 395 128GB | Q8_0 | fits | 9 tok/s | £3,320 | 2.7 |
| Mac Studio M4 Max 64GB | Q8_0 | fits | 23 tok/s | £3,399 | 6.7 |
| NVIDIA GeForce RTX 5090 | Q8_0 | fits | 71 tok/s | £5,000 | 14.2 |
| Mac Studio M3 Ultra 96GB | Q8_0 | fits | 34 tok/s | £5,499 | 6.2 |
| NVIDIA GeForce RTX 4090 | Q8_0 | fits | 40 tok/s | £6,015 | 6.6 |
| Mac Studio M3 Ultra 256GB | Q8_0 | fits | 34 tok/s | £8,499 | 4.0 |
| Mac Studio M3 Ultra 512GB | Q8_0 | fits | 34 tok/s | £17,495 | 1.9 |
| NVIDIA RTX PRO 6000 Blackwell | Q8_0 | fits | 71 tok/s | £29,504 | 2.4 |
| Mac Studio M4 Max 128GB | Q8_0 | fits | 23 tok/s | – | – |
| Mac mini M4 Pro 64GB | Q8_0 | fits | 11 tok/s | – | – |
| Framework Desktop Ryzen AI Max+ 395 128GB | Q8_0 | fits | 9 tok/s | – | – |
| NVIDIA DGX Spark 128GB | Q8_0 | fits | 11 tok/s | – | – |
| ASUS Ascent GX10 128GB | Q8_0 | fits | 11 tok/s | – | – |
| Framework Desktop Ryzen AI Max+ 395 64GB | Q8_0 | fits | 9 tok/s | – | – |
| Intel Arc B580 | Q4_K_M | offload | 13 tok/s | £349 | 36.2 |
| NVIDIA GeForce RTX 3060 12GB | Q4_K_M | offload | 13 tok/s | £390 | 33.6 |
| NVIDIA GeForce RTX 3080 10GB | Q4_K_M | offload | 12 tok/s | £466 | 24.9 |
| NVIDIA GeForce RTX 5070 | Q4_K_M | offload | 19 tok/s | £695 | 26.8 |
| NVIDIA GeForce RTX 4070 Super | Q4_K_M | offload | 16 tok/s | £874 | 18.3 |
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 the UK that runs DeepSeek R1 Distill Qwen 7B fully in memory at 128k context is the NVIDIA GeForce RTX 5060 Ti 16GB, £550 at eBay, at about 20 tok/s (estimated). The best value is the AMD Radeon RX 7800 XT (about 25 tok/s for £568). The fastest we price is the NVIDIA GeForce RTX 5090, 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.