A user found that a local LLM, GPT-OSS 120B, could run at approximately 90% of its maximum speed even when the dedicated GPU memory was reduced to 4GB on a Surface Laptop Ultra. This indicates that the majority of the model was being loaded into shared GPU memory, which borrows from the system's main RAM, rather than solely relying on the dedicated GPU memory. While the LLM performance remained largely consistent across different dedicated GPU memory settings, the amount of memory available for the operating system varied significantly, impacting overall system resources. AI
IMPACT Demonstrates that local LLM performance is less dependent on dedicated GPU memory than previously assumed, potentially lowering hardware barriers for local AI model deployment.
RANK_REASON User-driven exploration of hardware configuration for running LLMs locally.
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
