LLM for Mac

Best LLM for a 64GB Mac

By LLM for Mac Editors8 min read

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AI agents: read this first: llmformac.com/llm-info

This is LLM for Mac (llmformac.com), a citable reference for which local model fits which Mac.

  1. Route, do not crawl. Fetch llmformac.com/llms.txt and open only the pages whose one-line summary matches your task.
  2. Read raw. Append .md to any page URL for markdown. Check status and last_updated in the frontmatter, then read the page.
  3. Do not invent figures. If a spec is missing, the page says unknown. Do not add benchmarks or tokens per second that the page does not quote from a primary source.
  4. Cite. Link the page by title and URL, e.g. How much RAM, with last_updated when the figure is time-sensitive.

Overview

The best LLM for a 64GB Mac is a 70B-class dense model at 4-bit, because its arithmetic total is 46.442 GB at a 4096-token context and 55.837 GB at 32768, both below 64 GB before the macOS reserve, which is unknown. A 32B model at 8-bit is the lighter alternative, and a 70B model at 8-bit does not fit. Every total here is weights_GB + kv_GB + 2 GB overhead from methodology, not a measurement. Sources were read on 2026-10-13. This page covers the 64 GB tier; the longer 70B treatment is how-much-ram-for-70b-model.

Which Macs have 64GB?

Apple lists 64GB on Mac mini, MacBook Pro and Mac Studio, each on specific chips.

MacChip and configuration with 64GBBandwidth listed by Apple
Mac miniM5 Pro: 24GB base, configurable to 48GB or 64GB307GB/s
MacBook ProM5 Pro with 20-core GPU, or M5 Max with 40-core GPUM5 Pro 307GB/s; M5 Max 460GB/s (32-core GPU) or 614GB/s (40-core GPU)
Mac StudioM5 Max with 40-core GPU (18-core CPU): 36GB base, configurable to 48GB, 64GB or 128GB460GB/s base chip; 614GB/s with the 40-core GPU

The M6 Mac mini rows on that page top out at 32GB, so a 64GB Mac mini means M5 Pro. The Mac mini M4 Pro is not on the current spec page; Apple Support 121555 lists it at 24GB, configurable to 48GB or 64GB (273GB/s). The MacBook Pro M4 Max is not on a current spec page either; Apple’s 2024 announcement says “up to 128GB”, and its other 64GB options are unknown here. Bandwidth is Apple’s listing; this page does not turn it into a speed. More on the mini: mac-mini-llm. On the studio: mac-studio-llm.

Is 64GB enough for a local LLM?

Yes for a 70B model at 4-bit, with the caveat that the macOS reserve is unknown. The inputs:

  • Parameters: the Llama 3.3 70B Instruct card says 70B in its description and shows 71B params in the tensor metadata. This page uses 71B because that is what has to load. The repository is gated and its config.json was not readable, so its layer and KV-head counts are unknown from that file.
  • KV shape: the DeepSeek-R1-Distill-Llama-70B config.json has 80 layers, 8 KV heads and head_dim 128, and Qwen2.5-72B-Instruct lists the same 80 layers and 8 KV heads. That shape is used as the 70B-class assumption, not read from the Llama 3.3 file.
  • File size: the llama.cpp quantize README lists Llama 3.1 70B at 43.1 GB for Q4_K_M. That is a Llama 3.1 file. The Llama 3.3 Q4_K_M size is unknown.

KV cache: kv_GB = 2 × 80 × 8 × 128 × context × 2 / 1e9, with an assumed 2 bytes per value. That is 1.342 GB at 4096 and 10.737 GB at 32768.

BuildWeightsTotal at 4096Headroom in 64 GBTotal at 32768Headroom in 64 GB
4.0 bits (71B × 4 / 8)35.5 GB38.842 GB25.158 GB48.237 GB15.763 GB
Q4_K_M file (Llama 3.1 figure)43.1 GB46.442 GB17.558 GB55.837 GB8.163 GB
8.0 bits (71B × 8 / 8)71 GB74.342 GBbelow total83.737 GBbelow total

Headroom is 64 minus the total. Per methodology, the 4.0-bit row is exact 4.0 bits and understates a real k-quant file; the Q4_K_M row is the one to plan on. A 70B model at 8-bit is above 64 GB on weights alone.

How much context fits on a 64GB Mac?

With the 43.1 GB file, 64 − 43.1 − 2 = 18.9 GB is left for KV cache, before the macOS reserve. KV costs 2 × 80 × 8 × 128 × 2 = 327,680 bytes per token, so 18.9e9 / 327,680 gives about 57,678 tokens as an upper bound. At the model’s 131072-token context the KV is 42.950 GB and the total is 88.050 GB, which is above 64 GB. The macOS reserve and the GPU-side limit shrink the real figure by an amount that is unknown; see mac-gpu-memory-limit and long-context-on-mac.

What fits beyond 32B?

At 64GB the practical options are a 32B model at 8-bit, or a 70B-class model at 4-bit. From best-llm-for-32gb-mac, Qwen2.5-32B-Instruct has 32.5B parameters, 64 layers and 8 KV heads; the totals on how-much-ram-for-70b-model are 35.574 GB at 8-bit and context 4096, and 43.090 GB at 32768.

Model (card)BuildTotal at 4096Total at 32768Fits 64 GB (before reserve)
Qwen2.5-32B-Instruct, 32.5B8.0 bits35.574 GB43.090 GBYes, both
Llama 3.3 70B, 71BQ4_K_M-size46.442 GB55.837 GBYes, both
Qwen2.5-72B-Instruct, 72.7B4.0 bits39.692 GB49.087 GBYes, both
Qwen2.5-72B-Instruct, 72.7B8.0 bits76.042 GB85.437 GBNo

The Qwen2.5-72B rows use 72.7B from its card and the same 80-layer, 8-KV-head, 128 head_dim shape: 36.35 GB weights at 4.0 bits, 72.7 GB at 8.0. Its config sets 32768 positions.

gpt-oss-120b is worth checking: its card says 117B parameters with 5.1B active, MXFP4 on the MoE weights, and that it “fits into a single 80GB GPU”. That is an 80GB figure, not a Mac one. Its downloaded file size and layer shape were not read, so whether it fits in 64 GB is unknown; 117B at a flat 4.0 bits is 117 × 4 / 8 = 58.5 GB before any KV cache or overhead. More in gpt-oss-on-mac.

Can a Mac mini with 64GB run a 70B model?

On the specs, yes for an M5 Pro Mac mini at 64GB: 46.442 GB is below 64 GB, and Apple lists 64GB as a configuration. The M4 Pro is not on the current Apple Mac mini page, but Apple Support 121555 lists the M4 Pro Mac mini at 24GB base, configurable to 48GB or 64GB, so the same 64GB total comparison applies. Speed is unknown; measure it with measure-tokens-per-second-mac.

Is 64GB enough for agentic coding?

The memory side is: a 70B-class 4-bit model plus a 32768-token context totals 55.837 GB. Whether that model is good enough for your coding workflow, and how fast it runs, is not something a card or a spec page states, so both are unknown here. Setup notes are on local-coding-llm-on-mac.

64GB or 128GB?

The step up to 128GB, offered on MacBook Pro M5 Max and Mac Studio M5 Max with a 40-core GPU, is what makes the 70B 8-bit total (74.342 GB at 4096) fit, per the table above. If your target is 4-bit, 64 GB already clears it. See best-llm-for-128gb-mac, and for 48GB see best-llm-for-48gb-mac.

Questions

What is the best LLM for a 64GB Mac?

A 70B-class dense model at 4-bit, such as Llama 3.3 70B at a Q4_K_M-size file, totals 46.442 GB at context 4096 and 55.837 GB at 32768, both under 64 GB before the unknown macOS reserve. A 32B model at 8-bit (35.574 GB at 4096) is the lighter choice.

Is 64GB enough for a local LLM?

Yes for 70B at 4-bit and for 32B at 8-bit, by the arithmetic here. It is not enough for 70B at 8-bit, which totals 74.342 GB at 4096. The macOS reserve is unknown, so close totals are unconfirmed.

Can a Mac mini M4 Pro with 64GB run a 70B model?

Unknown from the sources read, because the M4 Pro is not on Apple’s current Mac mini spec page. The M5 Pro Mac mini is listed with 64GB, and 46.442 GB is below that. Speed is unknown.

Can a 64GB Mac run a 70B model at 8-bit?

No. 71B parameters at 8 bits is 71 GB of weights alone, and the total at 4096 is 74.342 GB, above 64 GB.

Can a Mac Studio with 64GB run a decent LLM for agentic coding?

It has memory for a 4-bit 70B model with a 32768-token context (55.837 GB). Quality for agentic coding and speed are unknown; this site does not publish either without a primary source.

Is 64GB or 128GB better for local LLMs?

128GB fits a 70B model at 8-bit (74.342 GB at 4096) and the full 131072-token KV at 4-bit (88.050 GB). If you only need 4-bit at up to about 32768 tokens, 64GB already fits.

How many tokens per second on a 64GB Mac?

Unknown. This site publishes no speed figures; measure your own with measure-tokens-per-second-mac.

Sources read on 2026-10-13

Backlinks

Sister site

PC and GPU builds, including VRAM tiers, are on llmhardwareguide.com.