DeepSeek-R1-Distill-Qwen-32B
DeepSeek-R1-Distill-Qwen-32B by DeepSeek (32.76B parameters). At Q4_K_M with an 8K context it needs about 21.9 GiB of memory. DeepSeek-R1's reasoning distilled into Qwen2.5-32B: a dense 32B that fits a 24 GB card at 4-bit.
Last updated: 2026-10-13
Model facts
Run it
ollama run deepseek-r1:32b
Memory needed
Weights + KV cache + runtime overhead, in GiB. Lower quantization = less memory, slightly lower quality.
| Quantization | 4K context | 8K context | 32K context |
|---|---|---|---|
| Q4_K_M | 20.9 GiB | 21.9 GiB | 27.9 GiB |
| Q5_K_M | 24.3 GiB | 25.3 GiB | 31.3 GiB |
| Q6_K | 27.9 GiB | 28.9 GiB | 34.9 GiB |
| Q8_0 | 35.5 GiB | 36.5 GiB | 42.5 GiB |
| FP16 | 65.6 GiB | 66.6 GiB | 72.6 GiB |
Which hardware runs it
Best quantization that fits each device with a 8K context. Estimates, not benchmarks. See the methodology.
Frequently asked questions
How much VRAM does DeepSeek-R1-Distill-Qwen-32B need?
With an 8K context, about 21.9 GiB at Q4_K_M and 36.5 GiB at Q8_0 (weights + KV cache + runtime overhead). Longer contexts need more memory for the KV cache.
Can DeepSeek-R1-Distill-Qwen-32B run on an GeForce RTX 4090?
Yes. DeepSeek-R1-Distill-Qwen-32B fits on the GeForce RTX 4090 (24 GB) at Q4_K_M with an 8K context, using about 21.9 GiB, at an estimated 36 tokens/s.
Can I run DeepSeek-R1-Distill-Qwen-32B on a Mac?
Yes. DeepSeek-R1-Distill-Qwen-32B fits on the M3 Ultra (96 GB) at FP16 with an 8K context, using about 66.6 GiB, at an estimated 8.1 tokens/s.
Can DeepSeek-R1-Distill-Qwen-32B be used commercially?
DeepSeek-R1-Distill-Qwen-32B is released under MIT. The licence allows commercial use (read its conditions).
Sources
- https://api-docs.deepseek.com/news/news250120
- https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
- https://huggingface.co/api/models/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B?expand[]=safetensors
- https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B/resolve/main/config.json
- https://ollama.com/library/deepseek-r1/tags