What is the best local LLM for 64GB RAM in April 2026?

gpt-oss 120B at Q4_K_M is the best general-purpose pick for 64GB. It uses about 65GB total with context — exactly the headroom 64GB Mac Studios give you when other apps are quit. Tool calling is excellent and the JSON output is the cleanest of any open-weight model, making it ideal for OpenClaw production.

Can I run Mistral Small 4 on 64GB RAM?

Yes at Q4_K_M (about 60GB). Mistral Small 4 (released March 16, 2026) is a 119B-A6B Mixture-of-Experts model that replaces the older Mistral Large 123B. The 6B active parameters per token give it fast inference (~25 tok/sec on Apple Silicon) with 119B-class knowledge.

Is 64GB Mac Studio worth it for local LLMs in April 2026?

Yes if you run 100B-class MoE models or want to keep two large models loaded simultaneously. The 64GB Mac Studio M2 Max delivers 18-30 tokens per second on gpt-oss 120B Q4 with 400GB/s memory bandwidth. For pure inference you can find faster setups, but for quiet always-on hosting with the latest April 2026 models, it is unmatched.

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Hardware April 26, 2026

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.

Running production OpenClaw on 64GB?

Book a Call at calendly.com/cloudyeti/meet. We'll architect a triple-model setup that turns your Mac Studio into a private LLM server.

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.