Best local LLMs for 16GB RAM
The best local LLM for 16GB RAM is Qwen3.5-9B — 9B parameters, roughly 6GB at Q4 quantization, scoring 21.4 on the Intelligence Index. Qwen3 VL 8B Instruct (8B) is the runner-up. Sizes are Q4 estimates with headroom left for the OS and context.
Open-weight models that actually fit in 16GB RAM (mainstream laptops, M-series MacBook Airs, mid gaming PCs), ranked by intelligence. Memory figures are Q4-quantization estimates with ~30% headroom reserved for the OS and context — every model here runs with Ollama, llama.cpp or LM Studio.
- 1
qwen3.5-9bReasoningToolsJSON+19B params · ~6GB @ Q4 · 262K ctx21.4Intelligence - 2qwen3-vl-8b-instructToolsJSONVision8B params · ~6GB @ Q4 · 262K ctx8.4Intelligence
- 3
granite-4.1-8bToolsJSON8B params · ~6GB @ Q4 · 131K ctx6.7Intelligence
Frequently asked
What is the best local LLM for 16GB RAM?
Qwen3.5-9B (9B parameters, ~6GB at Q4) is the most capable open-weight model that comfortably fits 16GB RAM — typical for mainstream laptops, M-series MacBook Airs, mid gaming PCs.
How big a model can 16GB RAM run?
At Q4 quantization a model needs roughly 0.55GB per billion parameters plus overhead, and you should keep ~30% of memory free for the OS and context. That puts the practical ceiling for 16GB RAM around 17B parameters.
How are these memory estimates calculated?
Total parameters × ~0.55 bytes (Q4_K quantization, the local-inference default) + ~1.5GB runtime overhead. Lower quantizations (Q2/Q3) shrink further at a quality cost; Q8 or FP16 roughly doubles or quadruples the footprint. Long contexts add KV-cache memory on top.
Do mixture-of-experts models need less RAM?
No — MoE models activate only some experts per token (faster inference), but all weights must stay in memory. A 122B-A10B model needs RAM for all 122B parameters, so these pages size by total parameters.