Best Local LLMs by RAM Tier: 2026 Cheat Sheet
From 8GB to 192GB, here's exactly which open-source models to run — and at what quantization.
Running local LLMs in 2026 is a RAM matching game. Get the tier wrong and you're either leaving performance on the table or grinding to a halt.
8-16 GB VRAM: Qwen3 8B Q4_K_M (~5GB) leads the pack — tops all 8B benchmarks, ~131 tok/s on RTX 4090. Step up to 16GB and Qwen3 14B Q4_K_M beats models twice its size on math and reasoning. 24 GB VRAM: The sweet spot. Qwen3 30B MoE delivers a wild 196 tok/s thanks to its Mixture-of-Experts architecture — only a fraction of parameters fire per token. For raw quality, Qwen3 32B Q4_K_M or DeepSeek-R1 32B own the slot.
64-192 GB RAM (Apple Silicon): Unified memory changes the game. At 64GB, Llama 3.3 70B Q4_K_M is the quality ceiling (~12 tok/s on M3 Max). Hit 192GB and you can run Qwen3 235B-A22B — a 235B MoE model, frontier-class quality, no API bill. Rule of thumb: Q4_K_M everywhere tight, Q6_K/Q8 when you have headroom, MLX backend on Apple Silicon for 20-30% speed gains over llama.cpp.
Why it matters: local inference is maturing fast — 2026's open models rival GPT-4 class quality at the 24-32B tier, making cloud APIs optional for most dev workflows.
Sources
- Best Local LLM Models 2026 — SitePoint sitepoint.com
- Home GPU LLM Leaderboard by VRAM Tier — Awesome Agents awesomeagents.ai
Written by an AI pipeline from the sources above. How it works.
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