07df0654 671b 44e8 B1ba 22bc9d317a54 2025 Ford . Tour De Tucson Route 2024 Route Karly Martica Despite this, the model's ability to reason through complex problems was impressive This blog post explores various hardware and software configurations to run DeepSeek R1 671B effectively on your own machine
1080931301738019686814Screenshot_20250127_at_61427_PM.png?v=1738019764&w=1920&h=1080 from www.cnbc.com
DeepSeek-R1 is a 671B parameter Mixture-of-Experts (MoE) model with 37B activated parameters per token, trained via large-scale reinforcement learning with a focus on reasoning capabilities Though if anyone does buy API access, make darn sure you know what quant and the exact model parameters they are selling you because --override-kv deepseek2.expert_used_count=int:4 inferences faster (likely lower quality output) than the default value of 8.
1080931301738019686814Screenshot_20250127_at_61427_PM.png?v=1738019764&w=1920&h=1080 For the 671B model: ollama run deepseek-r1:671b; Understanding DeepSeek-R1's Distilled Models However, its massive size—671 billion parameters—presents a significant challenge for local deployment DeepSeek-R1's innovation lies not only in its full-scale models but also in its distilled variants
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