Best Local LLMs for 96GB RAM (April 2026): Qwen 3.5 122B & gpt-oss 120B

Bottom Line (April 2026)

• Best overall pick: Qwen 3.5 122B-A10B (MoE) at Q4_K_M
• Best for OpenClaw production: gpt-oss 120B at Q5_K_M
• Best fast inference: Qwen 3.6 35B-A3B at Q8_0 (paired with bigger 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

Common Mistakes at 96GB

1. 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.
2. 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.
3. 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.
4. 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.

Hardware That Actually Hits 96GB

• Mac Studio M2 Max / M3 Max (96GB) — best dedicated host
• M3 Max / M4 Max MacBook Pro (96GB) — laptop option
• 2x RTX A6000 48GB = 96GB VRAM (Linux)
• 4x RTX 3090 24GB = 96GB VRAM (server build)

See Also

• Best Local LLMs for 64GB RAM — gpt-oss 120B Q4
• Best Local LLMs for 128GB RAM → — Qwen 3.5 397B + DeepSeek
• Best Local Models for OpenClaw
• Best Local LLM by RAM (hub)