#VRAM — Kunal Ganglani

selective focus photography of GEFORCE RTX graphics card

LLM Quantization Levels Compared: Q4_K_M vs Q8_0 vs FP16

The practitioner's guide to choosing between Q4_K_M, Q5_K_S, Q8_0, and FP16 quantization for local LLMs — with real perplexity numbers, throughput benchmarks, and per-use-case recommendations.

local-llm, ollama, lm-studio, mlx, gpu, vram, apple-silicon, nvidia, ai-hardware, self-hosted-ai

Technology

Local LLM Hardware Guide 2026: VRAM, GPUs and Setup [Tested]

Every VRAM tier, GPU option, and runtime tool mapped out for running local LLMs in 2026 — from budget CPU-only rigs to RTX 5090 and Apple Silicon M5 workstations.

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GGUF vs GPTQ vs EXL2: LLM Quantization Formats Compared A head-to-head comparison of GGUF, GPTQ, and EXL2 quantization formats with real quality, speed, and VRAM trade-offs — updated for the… 11 min read
RTX 4060 Ti vs RTX 4070 for Local LLM Inference I'd pick the RTX 4060 Ti if you're running sub-13B models solo on a tight budget, and the RTX 4070 if VRAM headroom and generation speed… 12 min read
Portable LLM on a USB Stick: Run Ollama or LM Studio Offline Run a full LLM from a USB drive with zero internet. 16 min read