Best Local LLMs for 64GB RAM (April 2026): gpt-oss 120B & Mistral Small 4
64GB is the first tier where 100B-class Mixture-of-Experts models run comfortably at Q4. Run gpt-oss 120B for OpenAI-quality tool calling, Mistral Small 4 (119B-A6B MoE) for premium reasoning, or Qwen 3.6 35B-A3B at full Q8 for top quality at fast speeds. Mac Studio M2 Max 64GB territory.
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Bottom Line (April 2026)
- Best overall pick: gpt-oss 120B at Q4_K_M
- Best for OpenClaw production: gpt-oss 120B (cleanest tool calls at scale)
- Best premium reasoning: Mistral Small 4 (119B-A6B MoE) at Q4_K_M
- Best fast inference: Qwen 3.6 35B-A3B at Q8_0
Top Picks for 64GB RAM
1. gpt-oss 120B (Q4_K_M) — best overall
OpenAI’s flagship open-weight model at 120B. About 60GB at Q4_K_M with 32K context. Cleanest tool-call JSON of any open model — keeps OpenClaw happy through long autonomous loops. Speed: 18-30 tok/sec on Mac Studio M2 Max 64GB.
ollama pull gpt-oss:120b openclaw config set agents.defaults.models.chat ollama/gpt-oss:120b openclaw run --agent --max-hours 12 "Implement the spec end-to-end"
2. Mistral Small 4 (119B-A6B MoE) at Q4_K_M — best reasoning
Mistral’s March 16, 2026 release. 119B total parameters with 6B active per token = fast inference (~25 tok/sec on Apple Silicon) with 119B-class reasoning depth. Replaces the older Mistral Large 123B. About 60GB at Q4_K_M.
ollama pull mistral-small-4:q4_K_M openclaw config set agents.defaults.models.chat ollama/mistral-small-4:q4_K_M openclaw chat "Analyze the trade-offs in this RFC"
3. Qwen 3.6 35B-A3B (Q8_0) — premium fast model
Qwen’s April 22 MoE at full Q8 uses about 38GB. Top quality with 8B-class inference speed. Pick this when you want the highest-quality MoE response and have RAM left over for parallel apps.
ollama pull qwen3.6:35b-q8_0
4. Triple-Model Setup at 64GB
Run three specialized models with keep_alive to avoid swap latency:
# Chat (Qwen 3.6 27B Q5) — 20GB # Agent loops (gpt-oss 20B Q8) — 22GB # Utility (Qwen 3.5 4B Q8) — 5GB openclaw config set agents.defaults.models.chat ollama/qwen3.6:27b-q5_K_M openclaw config set agents.defaults.models.agent ollama/gpt-oss:20b-q8_0 openclaw config set agents.defaults.models.utility ollama/qwen3.5:4b-q8_0 openclaw config set agents.defaults.keep_alive 1h openclaw models status
Total: ~47GB models + context + OS = comfortable on 64GB.
5. Llama 3.3 70B (Q4_K_M) — still works, no longer the headline
The old standard. 42GB at Q4_K_M, runs at 12-22 tok/sec on Apple Silicon. Solid model but Qwen 3.6 27B Q8 and gpt-oss 120B Q4 both match or exceed it on most tasks now.
What Fits in 64GB
| Model | Quant | RAM Used | Tool Calling |
|---|---|---|---|
| gpt-oss 120B | Q4_K_M | ~62 GB | Excellent (production) |
| Mistral Small 4 119B-A6B | Q4_K_M | ~62 GB | Good |
| Qwen 3.6 35B-A3B | Q8_0 | ~40 GB | Excellent |
| Llama 3.3 70B | Q4_K_M | ~46 GB | Excellent |
| Qwen 3.6 27B | Q8_0 | ~33 GB | Excellent |
| Triple model (chat + agent + utility) | mixed | ~47 GB | Excellent |
Common Mistakes at 64GB
- Running gpt-oss 120B with 128K context. KV cache pushes you past 64GB. Cap at 32K.
- Treating 64GB as “unlimited”. macOS + browser + IDE eat 12-16GB easily. Treat 64GB as 48-50GB available.
- Running 200B+ models at IQ2 because they fit. Tool calling collapses. Stick with gpt-oss 120B Q4 or Mistral Small 4 Q4.
- Skipping Qwen 3.6 35B-A3B because it is “smaller”. The MoE design makes it faster than dense 32B models with comparable quality. Keep it as your fast-response model in dual setups.
Hardware That Actually Hits 64GB
- Mac Studio M2 Max (64GB) — best dedicated host
- M3 Max MacBook Pro (64GB)
- M4 Max MacBook Pro (64GB)
- 2x RTX A6000 48GB (96GB total VRAM split)
- AMD Threadripper workstation with 64GB DDR5 + RTX 4090 (CPU+GPU offload)
See Also
- Best Local LLMs for 48GB RAM — Qwen 3.6 at Q8
- Best Local LLMs for 96GB RAM → — Qwen 3.5 122B-A10B
- OpenClaw Mac Mini Setup — host setup
- Best Local LLM by RAM (hub)
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