What is the best local LLM for 64GB RAM in July 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 agentic coding, Poolside Laguna XS 2.1 (33B/3B MoE, 256K context) uses ~36GB at Q8 and runs via Ollama. 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 mid-2026 model wave. 64GB is now the tier for Llama 4 Scout (10M context), Laguna XS 2.1 (agentic coding at 256K context), 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.

← All guides

Hardware July 21, 2026

Best Local LLMs for 64GB RAM (July 2026): Llama 4 Scout, gpt-oss 120B, DeepSeek V4 Flash & Laguna XS 2.1

Q: Can I run Laguna XS 2.1 on 64GB RAM?

Yes, easily. Laguna XS 2.1 is Poolside's July 2026 agentic coding model: 33B total parameters with 3B active per token (MoE), 256K context window, up to 32K output tokens. At Q8/FP8 the weights use about 33-36GB, which leaves headroom for long context on 64GB. It gains +5.4% on SWE-bench Multilingual over Laguna XS.2 and is available in Ollama.

64GB keeps getting better through July 2026. Llama 4 Scout (10M context) fits at ~58GB. DeepSeek V4 Flash reaches Ollama via cloud; local runs still need community forks. Poolside's new Laguna XS 2.1 adds a 256K-context agentic coder at just 36GB Q8. 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 July 21, 2026 — Laguna XS 2.1 joins the tier

Laguna XS 2.1 (Poolside, 33B/3B MoE) — ~36GB at Q8, 256K context, agentic coding, +5.4% SWE-bench Multilingual over XS.2, in Ollama now
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) — on Ollama cloud since April 25; local runs still need experimental forks
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 (July 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 agentic coding you can run today: Laguna XS 2.1 (33B/3B MoE) at Q8 — 256K context, ~36GB, in Ollama
Best raw coding benchmark: DeepSeek V4 Flash — top SWE-Bench, but local runs still need experimental forks (Ollama cloud otherwise)
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. Laguna XS 2.1 (33B/3B MoE) at Q8 — best agentic coding [New July 2026]

Poolside’s July 2026 agentic coding model. 33B total / 3B active per token (MoE), 256K context window, up to 32K output tokens. At Q8/FP8 the weights use about 33-36GB — the smallest footprint of any serious coding pick at this tier, with room left for long context. It gains +5.4% on SWE-bench Multilingual over Laguna XS.2 and is built for tool calling and long-horizon terminal work.

ollama pull laguna-xs-2.1
openclaw config set agents.defaults.models.agent ollama/laguna-xs-2.1
openclaw run --agent "Fix the failing tests and open a PR"

Weights ship in BF16, FP8, NVFP4, and INT4 on Hugging Face. The FP8 KV cache keeps memory flat on long agent runs — a real advantage over dense 32B coders when OpenClaw loops run for hours.

3. DeepSeek V4 Flash (284B/13B MoE) — top coding benchmark, cloud-first

DeepSeek’s efficiency-tier model. 284B total / 13B active per token (MoE), 1M token context. Tops SWE-Bench Verified among open models. Status check (July 2026): it runs on Ollama cloud (since April 25, on Blackwell hardware), but no stable release of llama.cpp, Ollama, or LM Studio loads the V4 architecture locally yet — local runs depend on experimental community forks.

# Via Ollama cloud (no 160GB download):
ollama run deepseek-v4-flash:cloud

# Local: wait for stable V4 architecture support,
# or use experimental forks (ds4 engine)

If your goal is fully-local coding today, use Laguna XS 2.1 above. If you accept a cloud hop for the top benchmark model, the :cloud tag works now.

4. 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"

5. 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"

6. 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

7. 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.

8. 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.

Not sure a model fits your exact 64GB machine?

Check it in seconds: the token speed & cost estimator (preloaded with the 64GB Apple Silicon preset) compares local vs cloud cost, and the local LLM calculator shows which quants fit with context headroom.

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
Laguna XS 2.1 33B/3B ✦ new (agentic coding, 256K ctx)	Q8/FP8	~33-36 GB	30-50	Excellent
DeepSeek V4 Flash 284B/13B (local = experimental forks)	Q4	~35-40 GB	8-15	Good (Ollama cloud: Excellent)
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 (July 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.

🛒 Recommended hardware for local AI

The two Macs that handle the workloads on this page.

MINIMUM · 24 GB

Apple MacBook Pro M-series 24GB — minimum for seamless local AI

Apple MacBook Pro M-series The 24 GB unified memory floor for seamless local AI. Runs 27B models at Q4 comfortably; perfect entry rig for OpenClaw + Ollama.

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MacBook Pro M-series — recommended Mac for serious local AI workloads

Premium Mac for 48 GB+ For 70B-class models, multi-model setups, and 8-hour autonomous OpenClaw loops without compromise.

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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)