What is the best local LLM for 128GB RAM in June 2026?

gpt-oss 120B at Q6_K is the best general-purpose production pick — about 93GB, essentially FP16 quality, cleanest tool-call JSON of any open-weight model. For strongest reasoning, Llama 4 Maverick (400B/17B MoE) at Q4 uses ~95-100GB and barely fits 128GB. For coding, DeepSeek V4 Flash (284B/13B MoE) at Q4 uses ~80GB and tops SWE-Bench. For long documents, Llama 4 Scout at Q4 (~58GB) gives a 10 million token context window.

Does Llama 4 Maverick fit in 128GB RAM?

Barely. Llama 4 Maverick is 400B total parameters with 17B active per token (MoE). At Q4_K_M the weights are approximately 95-100GB — which fits in 128GB with limited KV cache headroom. Cap context at 16K-32K when running Maverick to stay under 128GB total. At Q2 it would fit more comfortably but quality degrades significantly. It does NOT fit 96GB or below.

Can I run DeepSeek V4 Flash locally on 128GB?

Yes. DeepSeek V4 Flash is 284B total parameters with 13B active per token (MoE). At Q4 the weights use approximately 80GB, leaving headroom for KV cache on a 128GB machine. Note: DeepSeek V4 Flash is not yet supported natively in Ollama — use the ds4 engine (github.com/antirez/ds4) or the V4-aware llama.cpp fork. The full DeepSeek V4 Pro (1.6T parameters) does NOT fit on any single consumer machine.

Is 128GB Mac Studio M4 Max worth it for local LLMs in 2026?

Yes for serious local AI work. The Mac Studio M4 Max 128GB has 546 GB/s memory bandwidth and handles gpt-oss 120B Q6 at 18-28 tok/sec and Llama 4 Scout at 25-35 tok/sec. The M3 Ultra variant (800 GB/s bandwidth) is ~40% faster on the same model. Compared to multi-GPU NVIDIA setups it is quieter, lower-power, and single-machine simple.

← All guides

Hardware June 29, 2026

Best Local LLMs for 128GB RAM (June 2026): Llama 4 Maverick, gpt-oss 120B & DeepSeek V4 Flash

128GB is where the June 2026 model wave lands hardest. Llama 4 Maverick (Meta's 400B/17B MoE) barely fits at ~95GB Q4 and is the strongest reasoning model you can run locally. gpt-oss 120B at Q6 remains the production-reliable pick. DeepSeek V4 Flash (284B/13B MoE) fits at Q4 and tops coding benchmarks. The hardware for this tier is the Mac Studio M4 Max 128GB.

Building a 128GB self-hosted LLM rig?

See our AI training options. We'll plan a quad-model OpenClaw setup that turns your Mac Studio Ultra into a private AI server for your team.

Updated June 2026 — 3 new models fit that didn't exist in April

Llama 4 Maverick (Meta, 400B/17B MoE) — ~95-100GB at Q4, strongest reasoning locally, barely fits
Llama 4 Scout (Meta, 109B/17B MoE) — ~58GB at Q4, 10 million token context window, fits easily
DeepSeek V4 Flash (284B/13B MoE) — ~80GB at Q4, #1 coding benchmark, requires ds4 engine

Bottom Line (June 2026)

Best overall pick: gpt-oss 120B at Q6_K (premium quality, cleanest tool calls, production-proven)
Best reasoning: Llama 4 Maverick (400B/17B MoE) at Q4 — frontier quality, tight 128GB fit
Best coding: DeepSeek V4 Flash (284B/13B MoE) at Q4 — top SWE-Bench, via ds4 engine
Best for long documents: Llama 4 Scout at Q4 — 10M context window, no other local model comes close
Best premium reasoning MoE: Mistral Small 4 (119B-A6B) at Q6_K

If you are not sure you need this tier, compare down first: best local LLM for 32GB RAM covers the practical 20B-32B tier, and best local LLMs for 64GB RAM covers the mainstream serious local AI host. Buy 128GB only when you need 120B-class models, long context headroom, or multi-model OpenClaw routing.

If your search was closer to “best local LLM reddit 128GB RAM” or “Reddit’s favorite model for OpenClaw”, use the compressed community-search page too: Best local LLM Reddit users recommend for 128GB RAM. It gives the fast shortlist before the deeper hardware notes here.

If your question is specifically “I have 128GB RAM but no GPU”, read the exact answer first: Can I run a local LLM with 128GB RAM and no GPU?. CPU-only 128GB and Apple unified-memory 128GB are very different setups.

If your 128GB machine has an RTX 3090, RTX 4090, or another 24GB GPU, use the hybrid answer instead: Can I run a local LLM with 128GB RAM and 24GB VRAM?. That setup is fast for 20B-35B GPU models, but the 24GB VRAM ceiling still matters.

If your 128GB machine has a 48GB workstation GPU, use the larger-GPU answer: Can I run a local LLM with 128GB RAM and 48GB VRAM?. That setup moves you into practical 70B-class GPU inference while keeping enough system RAM for OpenClaw, tools, logs, and offload.

Top Picks for 128GB RAM

1. Llama 4 Maverick (400B/17B MoE) at Q4 — best reasoning locally [New June 2026]

Meta’s flagship 400B Mixture-of-Experts model. 400B total parameters, 17B active per token. At Q4_K_M it uses ~95-100GB — it barely fits in 128GB with limited context headroom. This is the strongest reasoning model you can run on a single machine right now.

ollama run llama4:maverick
# Verify Ollama library availability first: ollama list | grep maverick

openclaw config set agents.defaults.models.chat ollama/llama4:maverick
openclaw config set agents.defaults.context_limit 16384  # required: limit context on 128GB

Speed: 10-15 tok/sec on M4 Max 128GB. Slow but frontier quality. Cap context at 16K-32K or you’ll page into swap. Does NOT fit 96GB or below.

2. Llama 4 Scout (109B/17B MoE) at Q4 — 10 million token context [New June 2026]

Scout is the long-context sibling of Maverick. 109B total / 17B active per token, 10 million token context window — nothing else comes close locally. At Q4 it uses ~58GB, leaving comfortable headroom on 128GB. Tok/sec: 25-35 on M4 Max.

ollama run llama4:scout
openclaw config set agents.defaults.models.chat ollama/llama4:scout
openclaw config set agents.defaults.context_limit 512000  # Scout handles it

Use Scout when you need to feed a whole codebase, a large PDF, or a long conversation history to the model in one shot. The 10M context is unprecedented for local inference.

3. DeepSeek V4 Flash (284B/13B MoE) at Q4 — best coding model [New June 2026]

DeepSeek’s mid-tier June 2026 release. 284B total / 13B active per token. At Q4 the weights use ~80GB — fits on 128GB with headroom for KV cache. Top SWE-Bench Verified score among locally runnable models.

Watch: running DeepSeek V4 Flash as a fully local coding agent on a 128GB Mac with the ds4 engine.

# Not yet in Ollama — use ds4 engine:
# https://github.com/antirez/ds4
# Or the V4-aware llama.cpp fork

# Once available in Ollama:
# ollama pull deepseek-v4-flash
openclaw config set agents.defaults.models.chat ollama/deepseek-v4-flash

Note: DeepSeek V4 Pro (1.6T parameters, 49B active) does NOT fit on any single consumer machine. Cloud API only.

4. gpt-oss 120B (Q6_K) — best production pick

OpenAI’s flagship open-weight model at Q6_K uses about 93GB. Essentially FP16 quality. Cleanest tool-call JSON of any open-weight model — the production OpenClaw pick for long autonomous runs.

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; 14-20 tok/sec on M4 Max 128GB.

If you’re shopping for a Mac that runs this well, the Mac Studio M4 Max on Amazon is the current machine — 128GB unified memory, 546 GB/s bandwidth. The M3 Ultra (800 GB/s) is ~40% faster on the same model if you can find the configuration you need.

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

Mistral’s March 16, 2026 release at Q6_K uses about 95GB. 6B active params per token gives faster inference than dense 120B models with comparable reasoning depth.

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

6. Qwen 3.5 122B-A10B (Q5_K_M) — strong MoE breadth

The Qwen 3.5 medium series flagship at Q5 uses about 88GB. 14B-class inference speed with 122B-class knowledge. Note: tool calling is affected by the Ollama bug (issue #14493) — pair with gpt-oss 120B for the OpenClaw agent path.

ollama pull qwen3.5:122b-q5_K_M
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

7. Quad-Model Setup at 128GB

# Production routing (total ~120GB):
# - gpt-oss 120B Q4 for agent loops (~62GB)
# - Qwen 3.6 27B Q8 for fast chat (~33GB)
# - Qwen 3.6 35B-A3B Q5 for MoE inference (~26GB)

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

Cap context at 32K when all three are loaded. Monitor with openclaw models status.

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

DeepSeek V3 671B-A37B at IQ2_XS uses about 130GB. Quality is degraded at IQ2 but still useful for experimentation. V4 Flash (above) is a better pick if you want DeepSeek-class coding.

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

What Fits in 128GB

Model	Quant	RAM Used	Tok/s (M4 Max)	Tool Calling
Llama 4 Maverick 400B/17B MoE ✦ new	Q4_K_M	~95-100 GB	10-15 tok/s	Good
DeepSeek V4 Flash 284B/13B MoE ✦ new	Q4	~80 GB	8-15 tok/s	Excellent (coding)
Llama 4 Scout 109B/17B MoE ✦ new (10M ctx)	Q4_K_M	~58 GB	25-35 tok/s	Good
gpt-oss 120B	Q6_K	~93 GB	14-20 tok/s	Excellent (production)
Mistral Small 4 119B-A6B MoE	Q6_K	~95 GB	14-18 tok/s	Good
Qwen 3.5 122B-A10B MoE	Q5_K_M	~92 GB	18-25 tok/s	Fair (Ollama bug)
Llama 3.3 70B	Q8_0	~80 GB	12-18 tok/s	Excellent
Quad-model (gpt-oss Q4 + 27B Q8 + 35B-A3B Q5)	mixed	~120 GB	varies	Excellent
DeepSeek V3 671B-A37B	IQ2_XS	~125 GB	5-8 tok/s	Fair (degraded)

Common Mistakes at 128GB

Confusing DeepSeek V4 Pro with V4 Flash. V4 Flash (284B/13B MoE, ~80GB Q4) DOES fit in 128GB. V4 Pro (1.6T parameters, 49B active) does NOT — cloud API only. Don’t conflate them.
Running Llama 4 Maverick with large context. At Q4, Maverick uses ~95-100GB for weights alone. Adding 64K context blows past 128GB. Cap at 16K-32K context when Maverick is loaded.
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.
Loading three 100B-class models simultaneously. Triple-loaded setups can spike to 130GB+ during context expansion. Test each combo with realistic workloads before committing to a routing config.
Buying 128GB for future-proofing when you only run 70B Q4. A 64GB Mac Studio runs Qwen 3.6 27B Q8 + gpt-oss 120B Q4 — same quality for half the cost. Buy 128GB only if you need Maverick, Scout, V4 Flash, or quad-model 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.

Check current price on Amazon → RECOMMENDED · 48 GB+

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.

Check current price on Amazon →

Amazon affiliate links — we earn a small commission at no cost to you.

Hardware That Actually Hits 128GB

The current Mac lineup for 128GB (June 2026):

Mac Studio M4 Max 128GB — the current dedicated AI host; 546 GB/s bandwidth; released March 2025. This is the machine for gpt-oss 120B Q6 + Llama 4 Scout daily use.
Mac Studio M3 Ultra 192GB — 800 GB/s bandwidth (~40% faster than M4 Max on same model); configured at 96GB or 192GB; no 128GB config available
MacBook Pro M4 Max 128GB — laptop option; expect some thermal throttling on sustained 24hr loads
Mac Studio M2 Ultra 128GB — prior generation; 800 GB/s bandwidth; still excellent if you have one
4× RTX A6000 48GB = 192GB VRAM (server build)
8× RTX 3090 24GB = 192GB VRAM (DIY budget rig, complex setup)

Note: There is no Mac Studio M4 Ultra. Apple did not release an M4 Ultra as of June 2026. The current Mac Studio top chip is M3 Ultra. Any benchmark you see citing “M4 Ultra” is speculative or erroneous.