Making Local LLMs Usable on 8GB GPUs

This story was originally published on HackerNoon at: https://hackernoon.com/making-local-llms-usable-on-8gb-gpus. Learn how to optimize local LLM inference on 8GB GPUs using KV-cache quantization, Flash Attention, batching, and practical latency benchmarks. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #local-llm, #llm-optimization, #circuit-based-noise-mitigation, #multilingual-llm, #model-quantization, #llama.cpp, #python-benchmarking, #hackernoon-top-story, and more. This story was written by: @nareshwaghela. Learn more about this writer by checking @nareshwaghela's about page, and for more stories, please visit hackernoon.com. This guide explores practical ways to run local LLMs efficiently on GPUs with only 8GB of VRAM. It compares baseline settings with Flash Attention, KV-cache quantization, batching, and context-size adjustments. You’ll learn how to measure VRAM usage, latency, throughput, and stability using reproducible llama.cpp commands and GPU monitoring tools.