Best Local LLMs for 128GB RAM (April 2026): gpt-oss 120B Q6 & Mistral Small 4 Q6

Bottom Line (April 2026)

• Best overall pick: gpt-oss 120B at Q6_K (premium quality, cleanest tool calls)
• Best for premium reasoning: Mistral Small 4 (119B-A6B MoE) at Q6_K
• Best for breadth: Qwen 3.5 122B-A10B at Q5_K_M (paired with gpt-oss for OpenClaw)
• Last resort for biggest: DeepSeek V3 at IQ2_XS (cloud API is the better answer for V4)

Top Picks for 128GB RAM

1. gpt-oss 120B (Q6_K) — best general-purpose

OpenAI’s flagship at Q6_K uses about 90GB. Essentially indistinguishable from FP16 on benchmarks. Cleanest tool-call JSON of any open-weight model — perfect for OpenClaw production at any horizon length.

ollama pull gpt-oss:120b-q6_K
openclaw config set agents.defaults.models.chat ollama/gpt-oss:120b-q6_K
openclaw run --agent --max-hours 24 "Continuous CI agent"

Speed: 18-28 tok/sec on M2 Ultra 128GB.

2. Mistral Small 4 (119B-A6B MoE) at Q6_K — best reasoning

Mistral’s March 16, 2026 release at Q6_K uses about 92GB. 6B active params per token gives faster inference than gpt-oss 120B Q6 with comparable reasoning depth. Replaces older Mistral Large 123B.

ollama pull mistral-small-4:q6_K
openclaw config set agents.defaults.models.chat ollama/mistral-small-4:q6_K

3. Qwen 3.5 122B-A10B (Q5_K_M) — premium MoE

The Qwen 3.5 medium series flagship MoE at Q5 uses about 88GB. Strong general capability and 14B-class inference speed. Note: tool calling is affected by the Ollama bug (issue #14493) so pair with gpt-oss 120B for the agent path.

ollama pull qwen3.5:122b-q5_K_M

# OpenClaw routing — chat with Qwen, agents with gpt-oss
openclaw config set agents.defaults.models.chat ollama/qwen3.5:122b-q5_K_M
openclaw config set agents.defaults.models.agent ollama/gpt-oss:120b

4. Quad-Model Setup at 128GB

Run four hot models simultaneously:

# Production routing:
# - gpt-oss 120B Q4 for general chat (~62GB)
# - Qwen 3.6 27B Q8 for fast premium responses (~33GB)
# - Qwen 3.6 35B-A3B Q5 for fast MoE inference (~26GB)
# (skip the third model at this combination — already at ~120GB)

openclaw config set agents.defaults.models.chat ollama/gpt-oss:120b
openclaw config set agents.defaults.models.fast ollama/qwen3.6:27b-q8_0
openclaw config set agents.defaults.models.moe ollama/qwen3.6:35b-q5_K_M
openclaw config set agents.defaults.keep_alive 4h

Total: ~120GB models + context + OS = tight but workable on 128GB. Cap context at 32K when loading all three.

5. DeepSeek V3 (IQ2_XS) — squeeze for the curious

DeepSeek V3 671B-A37B at IQ2_XS uses about 130GB which barely fits on 128GB hosts. Quality is degraded at IQ2 but still impressive on certain tasks. For real DeepSeek quality, V4 (released April 24, 2026) is cloud-only on consumer hardware.

ollama pull deepseek-v3:671b-iq2_xs
openclaw config set agents.defaults.context_limit 16000

What Fits in 128GB

Common Mistakes at 128GB

1. Trying to run DeepSeek V4 locally. It is 1.6T parameters with 49B active. No usable quant fits in 128GB. Use the cloud API instead.
2. Picking Qwen 3.5 122B-A10B as the OpenClaw chat model without gpt-oss fallback. The Ollama tool-calling bug (issue #14493) affects autonomous loops. Always pair with gpt-oss 120B for the agent path.
3. Loading three models without testing memory headroom. Triple-loaded setups can spike to 130GB+ during context expansion. Test each combo with realistic workloads.
4. Buying 128GB for “future-proofing” when you only run 70B Q4. A 64GB Mac Studio gives you the same Qwen 3.6 27B Q8 + gpt-oss 120B Q4 experience for half the price. Buy 128GB only if you actually need 119B+ models at premium quants or quad-model setups.

Hardware That Actually Hits 128GB

• Mac Studio M2 Ultra (128GB) — best dedicated AI host, 800GB/s bandwidth
• M3 Ultra Mac Studio (when available) — incremental upgrade
• M3 Max MacBook Pro (128GB) — laptop option, watch for thermal throttling
• 4x RTX A6000 48GB = 192GB VRAM (server build)
• 8x RTX 3090 24GB = 192GB VRAM (DIY budget rig)

See Also

• Best Local LLMs for 96GB RAM — Qwen 3.5 122B-A10B Q4
• Best Local LLM by RAM (hub) — full comparison
• Best Local Models for OpenClaw — model-first guide
• OpenClaw Mac Mini Setup — host setup playbook
• OpenClaw Costs Guide — when local pays back the hardware