Best Local LLM for 64GB RAM (2026): gpt-oss 120B Wins
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.
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- 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.
Shopping, not just reading? Our full gear ladder lists every verified pick by tier — Macs, single GPUs, and multi-GPU builds — with what to buy and what to skip.
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 (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
- Best dense model at near-lossless quant: Gemma 4 31B at Q8_0 (~32.6GB, 256K context, multimodal)
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. Gemma 4 31B (Q8_0) — dense quality without quantization loss
Google’s dense Gemma 4, 30.7B parameters and a 256K context window with multimodal text and image input. The Q8_0 build is 32.6GB, so 64GB is the first tier where you can run it at a near-lossless quant and still have room for long context and a second model. Q6_K (25.2GB) buys back another 7GB if you want the headroom. It supports structured tool use natively.
ollama pull gemma4:31b-q8_0 openclaw config set agents.defaults.models.chat ollama/gemma4:31b-q8_0
8. 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.
9. 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 |
| Gemma 4 31B (dense, 256K ctx) | Q8_0 | ~32.6 GB | 15-25 | 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 MacBook Pro M4 Max, 48GB and up, on Amazon is the Mac we link for this tier — 546 GB/s bandwidth, though a laptop shows more thermal variability than a desktop on long runs. If you want a quiet, always-on desktop host instead, the Mac Studio M2 Max at 64GB delivers 400 GB/s, and we do not currently stock an affiliate link for that configuration.
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.
Amazon affiliate links — we earn a small commission at no cost to you.
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)
FAQ
What is the best local LLM for 64GB RAM in 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.
Can I run Laguna XS 2.1 on 64GB RAM?
Yes, easily. Laguna XS 2.1 is Poolside's 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.
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.
What is the best LLM for 64GB VRAM?
64GB of VRAM is a GPU setup, usually two RTX 5090s or two 48GB workstation cards, and it is faster than 64GB of unified memory. The picks overlap: gpt-oss 120B at Q4 (~60GB) and Laguna XS 2.1 at Q8 (~36GB), plus Laguna S 2.1 at UD-IQ4_XS (57.6GB) on dual 5090s. See the 64GB VRAM guide for the GPU-specific answer.
What is the best local AI model for 64GB RAM?
gpt-oss 120B at Q4_K_M. It uses about 60GB, has the cleanest tool-call JSON of any open-weight model, and runs at 18-30 tok/sec on a Mac Studio M2 Max 64GB. For long documents, run Llama 4 Scout at Q4 (~58-60GB) with its 10 million token context window. Cap gpt-oss 120B context at 32K on 64GB.
What local LLM runs on an M1 Max 64GB?
The same picks as any 64GB Apple Silicon Mac: gpt-oss 120B at Q4_K_M (~60GB), Llama 4 Scout at Q4 (~58GB), or Qwen 3.6 35B-A3B at Q8_0 (~38GB) for speed. The M1 Max has the same 400 GB/s memory bandwidth as the M2 Max, so expect a similar 18-31 tok/sec on 100B-class MoE models. Treat 64GB as 48-50GB available after macOS, a browser and an IDE.
Before you order parts, check the tested hardware list for current prices by tier.
See Also
- Best local LLM for 32GB RAM — exact 32GB tier answer before you upgrade
- Best local LLM Reddit users recommend for 64GB RAM — community-search shortlist for Qwen, gpt-oss, Scout, and what to avoid
- Best Local LLMs for 48GB RAM — Qwen 3.6 at Q8
- Best Local LLMs for 96GB RAM → — Qwen 3.5 122B-A10B
- Best local LLM for 128GB RAM — 120B-class models and multi-model routing
- OpenClaw Mac Mini Setup — host setup
- Best Local LLM by RAM (hub)
Hardware check
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Models that fit
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