What is the best local LLM for 96GB RAM in June 2026?

gpt-oss 120B at Q5_K_M (~80GB) is the best production pick for OpenClaw. Llama 4 Scout (109B/17B MoE) at Q4 uses ~58GB and gives a 10 million token context window — best for long-document tasks. DeepSeek V4 Flash (284B/13B MoE) at Q4 uses ~80GB and tops SWE-Bench coding. Qwen 3.5 122B-A10B at Q4 uses ~75GB and runs at 14B-class inference speed.

Does Llama 4 Scout fit in 96GB RAM?

Yes, very comfortably. Llama 4 Scout (109B total / 17B active MoE) at Q4_K_M uses ~58-60GB, leaving 36GB headroom on a 96GB machine. The 10 million token context window is available — at 96GB you can practically use 200K-500K token contexts without memory pressure.

Does Llama 4 Maverick fit in 96GB RAM?

No. Llama 4 Maverick is 400B total parameters. At Q4 it uses approximately 95-100GB — it requires 128GB to fit. At any lower quant quality degrades significantly. Do not confuse Scout (109B, fits 64GB+) with Maverick (400B, needs 128GB).

Is 96GB enough for triple-model setups?

Yes. Keep Llama 4 Scout (58GB) for long context plus gpt-oss 20B Q8 (22GB) for fast tool calls — total 80GB, comfortable on 96GB. Or run gpt-oss 120B Q5 (80GB) as your primary plus smaller utility models hot-loaded as needed.

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Hardware June 23, 2026

Best Local LLMs for 96GB RAM (June 2026): Llama 4 Scout, DeepSeek V4 Flash & gpt-oss 120B Q5

96GB is a strong position in June 2026. Three new models arrived that fit comfortably: Llama 4 Scout (10M context, ~58GB at Q4), DeepSeek V4 Flash (~80GB at Q4, top coding), and more headroom for gpt-oss 120B at premium Q5 quality. The Mac Studio M3 Ultra 96GB (800 GB/s bandwidth) is ~40% faster than M4 Max on the same model.

96GB Mac Studio for serious local AI?

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

🎮 PREFER A DISCRETE GPU? 96 GB ON ONE CARD

Rather have a GPU than unified memory? The RTX PRO 6000 Blackwell packs 96 GB of VRAM — the same memory budget as a 96 GB Mac, for 100B-class MoE and 70B at long context without CPU offload.

96GBRTX PRO 6000 Blackwell 96 GB ↗

Updated June 2026 — new models at 96GB

Llama 4 Scout — 109B/17B MoE, ~58GB at Q4, 10M context window, fits with lots of headroom
DeepSeek V4 Flash — 284B/13B MoE, ~80GB at Q4, top SWE-Bench coding, via ds4 engine
Llama 4 Maverick (400B) does NOT fit 96GB — needs 128GB minimum at Q4

Bottom Line (June 2026)

Best overall pick: gpt-oss 120B at Q5_K_M (cleanest tool calls, production-proven)
Best long documents: Llama 4 Scout at Q4 — 10M context, ~58GB, fits with 38GB headroom
Best coding: DeepSeek V4 Flash at Q4 — ~80GB, top SWE-Bench, via ds4 engine
Best fast inference: Qwen 3.6 35B-A3B at Q8_0 (paired as fast second model)
Best premium reasoning: Mistral Small 4 (119B-A6B) at Q5_K_M

Top Picks for 96GB RAM

1. Qwen 3.5 122B-A10B (Q4_K_M) — best general-purpose

The Qwen 3.5 medium series flagship MoE released February 24, 2026. 122B total parameters, 10B active per token = 14B-class inference speed with 122B-class knowledge. About 75GB at Q4_K_M.

ollama pull qwen3.5:122b
openclaw config set agents.defaults.models.chat ollama/qwen3.5:122b

Speed: ~18-25 tok/sec on M3 Max 96GB. Note: Qwen 3.5 has the Ollama tool-calling bug (issue #14493) that can affect strict OpenClaw autonomous loops. Pair with gpt-oss 120B for the agent path.

2. gpt-oss 120B (Q5_K_M) — best for OpenClaw production

OpenAI’s flagship at Q5 uses about 80GB. The cleanest tool-call JSON of any open-weight model. The “ship it for OpenClaw” pick when reliability matters more than benchmark scores.

ollama pull gpt-oss:120b-q5_K_M
openclaw config set agents.defaults.models.chat ollama/gpt-oss:120b-q5_K_M
openclaw run --agent --max-hours 12 "Continuous CI agent"

3. Mistral Small 4 (119B-A6B MoE) at Q5_K_M — premium reasoning

Mistral’s March 16, 2026 release at Q5 uses about 80GB. 6B active parameters per token gives faster inference than gpt-oss 120B Q5 with comparable reasoning depth. Replaces the older Mistral Large 123B from 2024.

ollama pull mistral-small-4:q5_K_M

4. Quad-Model Setup at 96GB

Keep four specialized models loaded:

# Chat (Qwen 3.6 27B Q8) — 33GB
# Agent loops (gpt-oss 20B Q8) — 22GB
# Code (Nemotron Cascade 2 30B Q5) — 22GB
# Utility (Qwen 3.5 4B Q8) — 5GB

openclaw config set agents.defaults.models.chat ollama/qwen3.6:27b-q8_0
openclaw config set agents.defaults.models.agent ollama/gpt-oss:20b-q8_0
openclaw config set agents.defaults.models.code ollama/nemotron-cascade-2:30b-q5_K_M
openclaw config set agents.defaults.models.utility ollama/qwen3.5:4b-q8_0
openclaw config set agents.defaults.keep_alive 2h

openclaw models status

Total: ~82GB models + context + OS = comfortable on 96GB.

5. Llama 3.3 70B (Q6_K) — still works

The old standard at Q6_K uses about 60GB. Still solid but Qwen 3.5 122B-A10B and gpt-oss 120B both match or exceed it on most April 2026 benchmarks.

What Fits in 96GB

Model	Quant	RAM Used	Tool Calling
Qwen 3.5 122B-A10B	Q4_K_M	~78 GB	Fair (Ollama bug)
gpt-oss 120B	Q5_K_M	~82 GB	Excellent (production)
Mistral Small 4 119B-A6B	Q5_K_M	~82 GB	Good
Llama 3.3 70B	Q6_K	~62 GB	Excellent
Quad-model setup	mixed	~82 GB	Excellent
Qwen 3.6 27B + Qwen 3.6 35B-A3B (dual)	Q8 + Q6	~63 GB	Excellent

Common Mistakes at 96GB

Picking Qwen 3.5 122B-A10B for OpenClaw without gpt-oss fallback. The Ollama tool-calling bug (issue #14493) affects all Qwen 3.5 variants. Always pair with gpt-oss 120B for the agent path.
Loading three models without setting keep_alive. Ollama unloads idle models in 5 minutes by default. Set keep_alive 2h so model swaps don’t pause your workflow.
Running 235B+ models at IQ2 because “more parameters.” Quality at IQ2 is so degraded that a 122B-A10B at Q4 beats it. Skip the squeeze.
Skipping the new Qwen 3.6 35B-A3B because the 122B-A10B fits. The 35B-A3B is faster and excellent for parallel use cases. Keep both for routing.