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Hello folks! Yesterday, DeepSeek did a huge update to their R1 model, bringing its performance on par with OpenAI’s o3, o4-mini-high and Google’s Gemini 2.5 Pro. They called the model ‘DeepSeek-R1-0528’ (which was when the model finished training) aka R1 version 2.
Back in January, you could actually run the full 720GB sized R1 (non-distilled) model with just an RTX 4090 (24GB VRAM) and now we’re doing the same for this even better model and better tech.
Note: if you do not have a GPU, no worries, DeepSeek also released a smaller distilled version of R1-0528 by fine-tuning Qwen3-8B. The small 8B model performs on par with Qwen3-235B so you can try running it instead That model just needs 20GB RAM to run effectively. You can get 8 tokens/s on 48GB RAM (no GPU) with the Qwen3-8B R1 distilled model.
At Unsloth, we studied R1-0528’s architecture, then selectively quantized layers (like MOE layers) to 1.58-bit, 2-bit etc. which vastly outperforms basic versions with minimal compute. Our open-source GitHub repo: https://github.com/unslothai/unsloth
If you find the large one is too slow on your device, then would recommend you to try the smaller Qwen3-8B one: https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF
The big R1 GGUFs: https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF
We also made a complete step-by-step guide to run your own R1 locally: https://docs.unsloth.ai/basics/deepseek-r1-0528
Thanks so much once again for reading! I’ll be replying to every person btw so feel free to ask any questions!
submitted by /u/yoracale
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