DeepSeek-R1-0528-Qwen3-8B
DeepSeek-R1-0528-Qwen3-8B by DeepSeek (8.19B parameters). At Q4_K_M with an 8K context it needs about 6.5 GiB of memory. DeepSeek-R1-0528's chain of thought distilled into Qwen3-8B: step-by-step reasoning that fits a 12 GB card at 8-bit.
Last updated: 2026-10-13
Model facts
Run it
ollama run deepseek-r1:8b
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 | 5.9 GiB | 6.5 GiB | 9.9 GiB |
| Q5_K_M | 6.8 GiB | 7.3 GiB | 10.7 GiB |
| Q6_K | 7.7 GiB | 8.2 GiB | 11.6 GiB |
| Q8_0 | 9.6 GiB | 10.1 GiB | 13.5 GiB |
| FP16 | 17.1 GiB | 17.6 GiB | 21.0 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-R1-0528-Qwen3-8B need?
With an 8K context, about 6.5 GiB at Q4_K_M and 10.1 GiB at Q8_0 (weights + KV cache + runtime overhead). Longer contexts need more memory for the KV cache.
Can DeepSeek-R1-0528-Qwen3-8B run on an GeForce RTX 4090?
Yes. DeepSeek-R1-0528-Qwen3-8B fits on the GeForce RTX 4090 (24 GB) at FP16 with an 8K context, using about 17.6 GiB, at an estimated 43 tokens/s.
Can I run DeepSeek-R1-0528-Qwen3-8B on a Mac?
Yes. DeepSeek-R1-0528-Qwen3-8B fits on the M3 Ultra (96 GB) at FP16 with an 8K context, using about 17.6 GiB, at an estimated 33 tokens/s.
Can DeepSeek-R1-0528-Qwen3-8B be used commercially?
DeepSeek-R1-0528-Qwen3-8B is released under MIT. The licence allows commercial use (read its conditions).
Sources
- https://api-docs.deepseek.com/news/news250528
- https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
- https://huggingface.co/api/models/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B?expand[]=safetensors
- https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B/resolve/main/config.json
- https://ollama.com/library/deepseek-r1/tags