Air Gapped and Offline local GPU usage of LLM models Meta Llama 3.3, claude-sonnet 3.5, Gemma 2-27b, Qwen2, Mistral-large-instruct, Deepseek R1 / V3 - compare to online OpenAI o1 pro

[obriensystems]

Air Gapped and Offline local GPU usage of LLM models Meta Llama 3.2, claude-sonnet 3.5, Gemma 2-27b, Qwen2, Mistral-large-instruct, Deepseek R1 / V3

see https://obrienlabs.medium.com/running-reasoning-llms-like-the-deepseek-r1-70b-43g-locally-for-private-offline-air-gapped-259fa437da8f

Compare to OpenAI o1 pro

• deepseek R1 - DeepSeek R1 14b on NVIDIA 48G RTX-A6000 or Apple M4 Max 40 core 48G compared to OpenAI o1 pro machine-learning#37 - see deepseek-r1 on ollama on dual RTX-4090 dual RTX-A4500 RTX-A6000 RTX-A4000 RTX-A3500 M4 Max 40 core #95

• Qwen2 - Qwen2 72b-instruct on NVIDIA 48G RTX-A6000 or Apple M4 Max 40 core 48G compared to OpenAI o1 pro #96

• Llama 3.3 - Meta llama 3.3 70b on NVIDIA 48G RTX-A6000 or Apple M4 Max 40 core 48G compared to OpenAI o1 pro #97

• Mistral-large-instruct Mistral-large123b-instruct-2411-q2_K (45G) on NVIDIA 48G RTX-A6000 or Apple M4 Max 40 core 48G compared to OpenAI o1 pro #98

• claude-sonnet 3.5 Claude-Sonnet 3.5 #99

• gemma 2 - Google Gemma 2 27b on CUDA and Metal #31

• see also https://github.com/ObrienlabsDev/blog/wiki/CUDA-based-%E2%80%90-High-Performance-Computing-%E2%80%90-LLM-Training-%E2%80%90-Ground-to-GCP-Cloud-Hybrid

• GPU/CPU performance from the bottom up - https://github.com/ObrienlabsDev/performance