← All guides
Hardware July 21, 2026

Best Local LLM for 64GB RAM (2026): Qwen3.8 27B at Q8

On 64GB, run Qwen3.8 27B at Q8_0. The file is 27.05 GiB, and a 64GB Mac gives the GPU 51.84 GiB by default. That leaves about 24.8 GiB for context or a second model. gpt-oss 120B (59.03 GiB) and Llama 4 Scout at Q4 (57.74-60.87 GiB) are over that limit. For speed, run Qwen3.6 35B-A3B at Q8_0 (34.37 GiB).

Apple Mac for 64GB RAM local AI on Amazon
🛒 BEST MAC FOR 64GB RAM Apple MacBook Pro · 48GB+ unified memory Runs Qwen3.8 27B at Q8_0 (27.05 GiB) with room for long context on 48GB and up. Check current price on Amazon →
Updated October 29, 2026 — new top pick, file sizes checked
  • Qwen3.8 27B at Q8_0 is the new top pick: 27.05 GiB, about 24.8 GiB left for context
  • gpt-oss 120B is no longer the pick. Its 59.03 GiB file is over the 51.84 GiB a 64GB Mac gives the GPU
  • Llama 4 Scout and Mistral Small 4 do not fit at Q4 either (57.74-68.70 GiB)
  • Every file size on this page is read from the Hugging Face API on October 29, 2026

Bottom Line (October 2026)

  • Best overall pick: Qwen3.8 27B at Q8_0 (27.05 GiB). It leaves about 24.8 GiB for context.
  • Best fast pick: Qwen3.6 35B-A3B at Q8_0 (34.37 GiB). Only 3B parameters are active per token.
  • Best dense alternative with image input: Gemma 4 31B at Q8_0 (30.39 GiB).
  • Best small agentic coder: Laguna XS 2.1 at Q4_K_M (18.88 GiB).
  • Two models at once: Qwen3.8 27B Q8_0 plus Qwen3.6 35B-A3B UD-Q4_K_M (20.61 GiB) = 47.66 GiB.
  • Does not fit by default: gpt-oss 120B (59.03 GiB), Llama 4 Scout Q4 (57.74-60.87 GiB), Mistral Small 4 Q4 (68.70 GiB).

Why 64GB is really about 52 GiB. macOS lets the GPU use only part of the RAM. On a 64GB M4 Pro Mac mini, llama.cpp printed recommendedMaxWorkingSetSize = 55662.79 MB at startup (llama.cpp #20011). That is 51.84 GiB, or 81% of the RAM. A model file must fit in that space with its context. Our MacBook guide lists the limit for each RAM size.

If you are still deciding whether 64GB is worth it, start with the exact 32GB answer: best local LLM for 32GB RAM. A 48GB Mac gives the GPU about 37.4 GiB and holds the same top picks. 64GB adds context and room for a second model.

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.

Top Picks for 64GB RAM

1. Qwen3.8 27B at Q8_0 — best overall [New pick, October 2026]

Qwen3.8 27B is a dense 27B model. Unsloth published its GGUF builds in August 2026. The Q8_0 file is 27.05 GiB and keeps almost all of the model’s quality. On a 64GB Mac it leaves about 24.8 GiB of the GPU limit for the KV cache and a second model.

ollama run hf.co/unsloth/Qwen3.8-27B-GGUF:Q8_0
openclaw config set agents.defaults.models.chat ollama/hf.co/unsloth/Qwen3.8-27B-GGUF:Q8_0

Speed: Hardware Corner measured 31.4 tok/s at 4K context and 18.2 tok/s at 64K context on an M5 Max with 128GB. That test used the Q4_K_S build (source, updated 2026-08-17). A dense model reads all of its weights for each token. The Q8_0 file is larger than a Q4 file, so it runs slower on the same chip. A chip with half the memory bandwidth runs about half as fast. Use Q6_K (20.47 GiB) if you want more speed.

2. Qwen3.6 35B-A3B at Q8_0 — best fast pick

Qwen3.6 35B-A3B is a mixture-of-experts (MoE) model with 3B active parameters per token. The Q8_0 file is 34.37 GiB. It reads far fewer bytes per token than the dense 27B, so it generates text faster. Pick it when response speed matters more than the last bit of quality.

ollama run hf.co/unsloth/Qwen3.6-35B-A3B-GGUF:Q8_0

3. Gemma 4 31B at Q8_0 — dense alternative with image input

Google’s dense Gemma 4 31B takes text and image input. The Q8_0 file is 30.39 GiB, so 64GB is the first tier where it runs at a near-lossless quant with room for context. Q6_K (23.47 GiB) gives back another 6.9 GiB if you want more headroom.

ollama run hf.co/unsloth/gemma-4-31B-it-GGUF:Q8_0

4. Laguna XS 2.1 at Q4_K_M — small agentic coder

Poolside’s Laguna XS 2.1 is a 33B MoE model with 3B active parameters per token and a 256K context window. It is made for tool calls and long terminal tasks. Poolside’s own Q4_K_M GGUF is 18.88 GiB, so it leaves more than 30 GiB for long agent runs.

ollama run hf.co/poolside/Laguna-XS-2.1-GGUF:Q4_K_M
openclaw config set agents.defaults.models.agent ollama/hf.co/poolside/Laguna-XS-2.1-GGUF:Q4_K_M

5. Two models at once

Load a quality model and a fast model, and keep both in memory:

# Chat: Qwen3.8 27B Q8_0 — 27.05 GiB
# Agent loops: Qwen3.6 35B-A3B UD-Q4_K_M — 20.61 GiB

openclaw config set agents.defaults.models.chat ollama/hf.co/unsloth/Qwen3.8-27B-GGUF:Q8_0
openclaw config set agents.defaults.models.agent ollama/hf.co/unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M
openclaw config set agents.defaults.keep_alive 1h

Total: 47.66 GiB of weights. That leaves about 4 GiB of the GPU limit for context, so keep the context windows short. For long context, run one model at a time.

6. gpt-oss 120B — only with more memory or a PC GPU

gpt-oss 120B was the old pick on this page. The MXFP4 GGUF is 59.03 GiB. That is 7.19 GiB over the 51.84 GiB a 64GB Mac gives the GPU. You can raise the limit by hand, but then macOS has under 4 GiB left. We do not recommend that.

On a PC, the math is different. A PC with 64GB of system RAM and a 16-24GB GPU can hold the 59.03 GiB file split across both. On a Mac, buy 96GB or more for this model. See best local LLM for 96GB RAM.

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

All sizes are from the Hugging Face API, read October 29, 2026. The limit is the 51.84 GiB a 64GB Mac gives the GPU by default.

ModelQuantFile sizeFits 51.84 GiB?
Qwen3.8 27B (dense) ✦ top pickQ8_027.05 GiBYes (Excellent)
Qwen3.6 35B-A3B (MoE, 3B active)Q8_034.37 GiBYes (Excellent)
Gemma 4 31B (dense, image input)Q8_030.39 GiBYes (Good)
Laguna XS 2.1 (MoE, agentic coding)Q4_K_M18.88 GiBYes (Excellent)
Qwen3.8 27B + Qwen3.6 35B-A3B (two models)Q8_0 + UD-Q4_K_M47.66 GiBYes (Excellent)
Mistral Small 4 119B (MoE)UD-Q3_K_M50.64 GiBBarely (Not advised: no room for context)
Llama 4 Scout 109B (MoE)UD-Q4_K_XL57.74 GiBNo (Over the Mac limit)
gpt-oss 120B (MoE)MXFP459.03 GiBNo (Over the Mac limit)

Does NOT fit 64GB (October 2026):

  • gpt-oss 120B (MXFP4, 59.03 GiB) — over the Mac GPU limit; needs 96GB or more
  • Llama 4 Scout (Q4_K_M, 60.87 GiB) — over the limit
  • Mistral Small 4 119B (UD-Q4_K_M, 68.70 GiB) — over the full 64GB
  • DeepSeek V4.1 Flash 1017 (763B MoE) — the smallest GGUF (IQ1_M) is about 112 GiB plus a shared 103 GiB Engram shard
  • Qwen3.8 Flash Next — the 2-bit Q2_0 build is 61.86 GiB; 26.82 GiB of it can stay on disk (details)

The MacBook Pro M4 Max, 48GB and up, on Amazon is the Mac we link for this tier. A laptop shows more thermal change than a desktop on long runs. For a quiet, always-on desktop host, look at a Mac Studio or Mac mini with 64GB. We do not stock an affiliate link for those configurations.

Watch: Can DeepSeek Actually Code Like Claude?

DeepSeek’s current Flash model does not fit 64GB. This live test shows how a large open model compares with a cloud coding agent.

Common Mistakes at 64GB

  1. Picking a model by RAM size, not by the GPU limit. A 64GB Mac gives the GPU 51.84 GiB. A 59 GiB file does not load at default settings.
  2. Filling the limit with weights. The KV cache grows with context. Leave at least 8-10 GiB free for long context.
  3. Running 100B+ models at 2-3 bits because they fit. Quality and tool calls get worse. Qwen3.8 27B at Q8_0 is the safer choice.
  4. Skipping Qwen3.6 35B-A3B because it is “smaller”. Only 3B parameters are active per token, so it is much faster than a dense 27B. Keep it as your fast model.

🛒 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 mini M4 Pro or M5 Pro (64GB) — 51.84 GiB GPU limit on the M4 Pro log
  • Mac Studio M2 Max (64GB) — dedicated desktop host
  • M3 Max, M4 Max or M5 Max MacBook Pro (64GB)
  • PC with 64GB DDR5 plus a 16-24GB GPU — holds bigger MoE files split across RAM and VRAM
  • Two 32GB GPUs (64GB total VRAM) — see the 64GB VRAM guide

FAQ

What is the best local LLM for 64GB RAM in 2026?

Qwen3.8 27B at Q8_0. Unsloth's Q8_0 file is 27.05 GiB. A 64GB Mac gives the GPU 51.84 GiB by default, so about 24.8 GiB stays free for context and a second model. For speed, run Qwen3.6 35B-A3B at Q8_0 (34.37 GiB). It has only 3B active parameters per token. For agentic coding, run Laguna XS 2.1 at Q4_K_M (18.88 GiB).

Does gpt-oss 120B fit in 64GB RAM?

Not on a 64GB Mac at default settings. The MXFP4 GGUF is 59.03 GiB, and macOS gives the GPU 51.84 GiB on a 64GB Mac. You can raise the limit by hand, but that leaves macOS under 4 GiB. On a PC, 64GB of system RAM plus a 16-24GB GPU can hold the file split across both. For gpt-oss 120B on a Mac, buy 96GB or 128GB.

Does Llama 4 Scout fit in 64GB RAM?

Not at Q4 on a 64GB Mac at default settings. Unsloth's Q4_K_M is 60.87 GiB and UD-Q4_K_XL is 57.74 GiB. Both are over the 51.84 GiB GPU limit. Scout was released in April 2025. Qwen3.8 27B at Q8_0 is the better 64GB pick now.

Can I run Mistral Small 4 on 64GB RAM?

Not at 4-bit. Unsloth's UD-Q4_K_M of Mistral Small 4 119B is 68.70 GiB, which is more than the full 64GB. The largest build under the 51.84 GiB Mac GPU limit is UD-Q3_K_M at 50.64 GiB. That leaves almost no room for context, so we do not recommend it on 64GB.

Is 64GB worth it over 48GB for local LLMs?

Not for a bigger model. A 64GB Mac gives the GPU 51.84 GiB and a 48GB Mac gives about 37.4 GiB. Both hold Qwen3.8 27B at Q8_0 (27.05 GiB) and Qwen3.6 35B-A3B at Q8_0 (34.37 GiB). 64GB adds context and room for a second model. The next model class (gpt-oss 120B, 59.03 GiB) needs 96GB or more on a Mac.

What is the best LLM for 64GB VRAM?

64GB of VRAM is a GPU setup, usually two 32GB cards. All 59.03 GiB of gpt-oss 120B fits there, which it does not on a 64GB Mac. See the 64GB VRAM guide for the GPU-specific answer.

What is the best local AI model for 64GB RAM?

Qwen3.8 27B at Q8_0 (27.05 GiB). It is a dense 27B model, so Q8_0 keeps almost all of its quality. It leaves about 24.8 GiB of the 51.84 GiB Mac GPU limit for context. Run Qwen3.6 35B-A3B at Q8_0 when speed matters more than quality.

What local LLM runs on an M1 Max 64GB?

The same picks as any 64GB Apple Silicon Mac: Qwen3.8 27B at Q8_0 (27.05 GiB), or Qwen3.6 35B-A3B at Q8_0 (34.37 GiB) for speed. The M1 Max reads memory at 400 GB/s. A dense 27B at Q8_0 reads all 27 GiB per token, so it is slower than the MoE model.

Before you order parts, check the tested hardware list for current prices by tier.

See Also

Hardware check

Which local LLM can your machine run?

Pick your setup. You get the model size that actually fits, plus a practical upgrade that moves you up a tier.

Read next

Best LLM for 128GB RAM (2026): Local Picks, Llama 4 Maverick
Best local LLMs for 128GB RAM in 2026. Llama 4 Maverick (400B MoE, ~95GB Q4), gpt-oss 120B at Q6, Laguna XS 2.1 (agentic coding, Q8 + huge context), Llama 4 Scout (10M context), DeepSeek V4 Flash via Ollama cloud. Mac Studio M4 Max territory.
Best Local LLM for 48GB VRAM or RAM (2026): Qwen 3.6 27B Q8
Best local LLMs for 48GB RAM in 2026. Qwen 3.6 27B at Q8 (near-FP16), Laguna XS 2.1 (agentic coding, ~36GB Q8), Qwen 3.6 35B-A3B MoE, gpt-oss 20B Q8, Gemma 4 26B-A4B (~15GB). M3 Max 48GB territory.
Best Local LLM for 32GB RAM (2026): Chat and Coding Picks
Best local LLM for 32GB RAM: Qwen3.8 27B at UD-Q6_K (20.47 GiB). gpt-oss 20B is 11.28 GiB, not 22GB. What fits under the ~24 GiB Mac GPU limit, Ollama commands, what to skip.
Best Local LLM for 96GB RAM (2026): gpt-oss 120B at Q5
Best local LLMs for 96GB RAM in 2026. Llama 4 Scout (10M context, ~58GB Q4), DeepSeek V4 Flash (~80GB Q4), gpt-oss 120B at Q5 (~80GB), Qwen 3.5 122B-A10B, Mistral Small 4 at Q5. Mac Studio M5 Ultra territory.