A user shared their experience running large language models (LLMs) with limited hardware, utilizing techniques like model quantization (Q3, Q2) and memory mapping (mmap) to offload parameters to NVMe storage. They found success with Mixture-of-Experts (MoE) models such as Deepseek-V4-Flash and Nemotron-3-Super-120B-A12B, achieving token generation speeds between 1.0-2.5 tokens/sec. This approach was employed for tasks like reverse engineering and code auditing, especially in regions where cloud-based LLM access is restricted. AI
IMPACT Provides insights into optimizing LLM performance on consumer-grade hardware, potentially enabling wider access for individuals with restricted cloud options.
RANK_REASON User experience post detailing methods for running LLMs on limited hardware.
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