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

gpt-oss 120B at Q4_K_M is the best general-purpose production pick — about 62GB, cleanest tool-call JSON of any open-weight model. For long documents, Llama 4 Scout (109B/17B MoE) at Q4 uses ~58GB and gives a 10 million token context window. For coding, DeepSeek V4 Flash (284B/13B MoE) at Q4 uses ~35-40GB and tops SWE-Bench. For premium reasoning, Mistral Small 4 (119B-A6B MoE) at Q4 is the pick.

Does Llama 4 Scout fit in 64GB RAM?

Yes. Llama 4 Scout is 109B total parameters with 17B active per token (MoE). At Q4_K_M it uses approximately 58-60GB — fits 64GB with headroom for context. The 10 million token context window means you can feed entire codebases in a single run. Scout does NOT require 128GB — that is Llama 4 Maverick (400B), which is a different model.

Can I run Mistral Small 4 on 64GB RAM?

Yes at Q4_K_M (about 62GB). Mistral Small 4 (released March 16, 2026) is a 119B-A6B MoE model. 6B active parameters per token give fast inference (~25 tok/sec on Apple Silicon) with 119B-class knowledge. It fits in 64GB when you quit memory-hungry other apps.

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

Yes for the June 2026 model wave. 64GB is now the tier for Llama 4 Scout (10M context), DeepSeek V4 Flash (top coding), gpt-oss 120B Q4 (production agents), and triple-model setups. The Mac Studio M2/M3 Max at 64GB delivers 400 GB/s bandwidth and 18-31 tok/sec on 100B-class models.

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Hardware June 29, 2026

Best Local LLMs for 64GB RAM (June 2026): Llama 4 Scout, gpt-oss 120B & DeepSeek V4 Flash

64GB is where the June 2026 model wave adds the most new options. Llama 4 Scout (10M context) fits at ~58GB and is the most practically useful new arrival. DeepSeek V4 Flash (~35-40GB at Q4) gives you top coding benchmarks with RAM to spare. gpt-oss 120B at Q4 remains the production-reliable pick for OpenClaw agent loops. Mac Studio M2/M3 Max territory.

Running production OpenClaw on 64GB?

See our AI training options. We'll architect a triple-model setup that turns your Mac Studio into a private LLM server.

Updated June 2026 — 2 new models at 64GB

Llama 4 Scout (Meta, 109B/17B MoE) — ~58GB at Q4, 10 million token context window, 31 tok/sec, best long-document model locally
DeepSeek V4 Flash (284B/13B MoE) — ~35-40GB at Q4, top SWE-Bench coding score, via ds4 engine
Llama 4 Maverick (400B) does NOT fit 64GB — needs 128GB. Don't confuse with Scout.

Watch: Can DeepSeek Actually Code Like Claude?

DeepSeek V4 Flash is one of the standout 64GB picks below for coding. We put it up against Claude in a live, unedited test to see whether a local model on this tier can really replace a cloud coding agent.

Bottom Line (June 2026)

Best overall pick: gpt-oss 120B at Q4_K_M (production-proven, cleanest tool calls)
Best long documents: Llama 4 Scout at Q4 — 10M context window, nothing else comes close
Best coding: DeepSeek V4 Flash at Q4 — top SWE-Bench, via ds4 engine (not yet in Ollama)
Best premium reasoning: Mistral Small 4 (119B-A6B MoE) at Q4_K_M
Best fast inference: Qwen 3.6 35B-A3B at Q8_0

If you are still deciding whether 64GB is worth it, start with the exact 32GB answer: best local LLM for 32GB RAM. For many OpenClaw users, 32GB is enough for Qwen 3.6 27B Q6 and gpt-oss 20B Q8; 64GB is the upgrade when you want bigger context, 70B-class experiments, or multiple serious models loaded at once.

If you came in through a community-style search like “best local LLM reddit 64GB RAM”, use the shorter Reddit-intent answer too: Best local LLM Reddit users recommend for 64GB RAM. It compresses this guide into the practical shortlist: Qwen for speed, gpt-oss for OpenClaw tool calls, and Scout when long context is the actual bottleneck.

Top Picks for 64GB RAM

1. Llama 4 Scout (109B/17B MoE) at Q4 — 10M context window [New June 2026]

Meta’s long-context specialist. 109B total / 17B active per token. At Q4_K_M it uses ~58-60GB — fits comfortably in 64GB with context headroom. The 10 million token context window is the most practically significant new feature in the June 2026 model wave.

ollama run llama4:scout
openclaw config set agents.defaults.models.chat ollama/llama4:scout

# Feed a whole codebase in one shot (Scout handles it at 64GB)
openclaw run --agent "Analyze the entire codebase and produce a security audit"

Speed: 31 tok/sec on Mac Studio M2 Max 64GB. Task success rate: 87% in our 30-day benchmark (slightly behind gpt-oss 120B). Quality on long-context tasks: best at this tier.

Use Scout when you need to process large inputs: full repo audits, long PDFs, extended conversation history. Use gpt-oss 120B for production agentic loops.

2. DeepSeek V4 Flash (284B/13B MoE) at Q4 — best coding [New June 2026]

DeepSeek’s efficiency-tier June 2026 model. 284B total / 13B active per token (MoE). At Q4 the weights use approximately 35-40GB — fits in 64GB with comfortable headroom. Tops SWE-Bench Verified among locally runnable models.

# Not yet in Ollama — use ds4 engine:
# https://github.com/antirez/ds4
# Once in Ollama:
# ollama pull deepseek-v4-flash

Note: DeepSeek V4 Flash requires the ds4 engine (not yet in Ollama as of June 2026). Tool-calling compatibility with OpenClaw is in progress. Watch for native Ollama support.

3. gpt-oss 120B (Q4_K_M) — best production pick

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"

4. 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"

5. 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

6. 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.

7. 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	Tok/s	Tool Calling
Llama 4 Scout 109B/17B ✦ new (10M ctx)	Q4_K_M	~58-60 GB	25-35	Good
DeepSeek V4 Flash 284B/13B ✦ new (coding)	Q4	~35-40 GB	8-15	Excellent (ds4 engine)
gpt-oss 120B	Q4_K_M	~62 GB	18-30	Excellent (production)
Mistral Small 4 119B-A6B MoE	Q4_K_M	~62 GB	20-28	Good
Qwen 3.6 35B-A3B MoE	Q8_0	~38-40 GB	25-45	Excellent
Llama 3.3 70B	Q4_K_M	~46 GB	12-22	Excellent
Triple-model (chat + agent + utility)	mixed	~47 GB	varies	Excellent

Does NOT fit 64GB (June 2026):

Llama 4 Maverick (400B total at Q4 = ~95GB) — needs 128GB
DeepSeek V4 Pro (1.6T total) — cloud only, no consumer hardware
Kimi K2.6 (1T total at Q2 = ~340GB) — requires 4× Mac Ultra cluster
GLM-5.2 (~750B total) — cloud only

The Mac Studio M2 Max 64GB on Amazon is the current dedicated host for this tier — quiet, always-on, 400 GB/s bandwidth. If you’re on a MacBook Pro M4 Max with 64GB you get similar results with slightly faster M4 bandwidth (546 GB/s) but more thermal variability on long runs.

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.