Best local LLMs for 64GB RAM
The best local LLM for 64GB RAM is Qwen3.6 27B — 27B parameters, roughly 16GB at Q4 quantization, scoring 37.1 on the Intelligence Index. Qwen3.5-27B (27B) is the runner-up. Sizes are Q4 estimates with headroom left for the OS and context.
Open-weight models that actually fit in 64GB RAM (high-end workstations, Mac Studios, multi-GPU rigs), 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
qwen3.6-27bReasoningToolsJSON+127B params · ~16GB @ Q4 · 262K ctx37.1Intelligence - 2qwen3.5-27bReasoningToolsJSON+127B params · ~16GB @ Q4 · 262K ctx33.8Intelligence
- 3qwen3.6-35b-a3bReasoningToolsJSON+135B params · ~21GB @ Q4 · 262K ctx31.6Intelligence
- 4qwen3.5-35b-a3bReasoningToolsJSON+135B params · ~21GB @ Q4 · 262K ctx29.3Intelligence
- 5qwen3.5-9bReasoningToolsJSON+19B params · ~6GB @ Q4 · 262K ctx21.4Intelligence
- 6
nemotron-3-nano-omni-30b-a3b-reasoning:freeReasoningToolsVision+130B params · ~18GB @ Q4 · 256K ctx14.9Intelligence - 7
gpt-oss-20bReasoningToolsJSON20B params · ~13GB @ Q4 · 131K ctx14.9Intelligence - 8qwen3-coder-30b-a3b-instructToolsJSON30B params · ~18GB @ Q4 · 262K ctx13.6Intelligence
- 9qwen3-vl-32b-instructToolsJSONVision32B params · ~19GB @ Q4 · 131K ctx11.1Intelligence
- 10qwen3-vl-30b-a3b-instructToolsJSONVision30B params · ~18GB @ Q4 · 262K ctx10.0Intelligence
- 11
deepseek-r1-distill-llama-70bReasoning70B params · ~40GB @ Q4 · 8K ctx9.9Intelligence - 12qwen-2.5-72b-instructToolsJSON72B params · ~41GB @ Q4 · 33K ctx9.6Intelligence
- 13qwen3-vl-8b-instructToolsJSONVision8B params · ~6GB @ Q4 · 262K ctx8.4Intelligence
- 14nemotron-3-nano-30b-a3bReasoningToolsJSON30B params · ~18GB @ Q4 · 262K ctx7.4Intelligence
- 15qwen-2.5-coder-32b-instruct32B params · ~19GB @ Q4 · 33K ctx7.1Intelligence
Frequently asked
What is the best local LLM for 64GB RAM?
Qwen3.6 27B (27B parameters, ~16GB at Q4) is the most capable open-weight model that comfortably fits 64GB RAM — typical for high-end workstations, Mac Studios, multi-GPU rigs.
How big a model can 64GB 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 64GB RAM around 78B 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.