---
title: "Best LLM for a 32GB Mac"
seoTitle: "Best LLM for a 32GB Mac: Models That Fit (2026)"
slug: "best-llm-for-32gb-mac"
category: "fit"
tags: ["32gb", "ram", "gemma", "qwen", "nemotron", "mac-mini"]
status: "stable"
last_updated: 2026-10-13
updated: 2026-10-13
created: 2026-10-13
faqPage: true
summary: "On a 32GB Mac, 4-bit Gemma 4 31B, Qwen3.6-27B and Nemotron 3.5 Lightning 30B-A3B fit at short context by formula; long context and macOS reserve are the limits."
description: "Best LLM for a 32GB Mac: Gemma 4 31B, Qwen3.6-27B and Nemotron 3.5 Lightning 30B-A3B, with RAM math from model cards and Apple's 32GB configs."
related: ["[[methodology]]", "[[how-much-ram-for-local-llm]]", "[[best-local-llm-for-mac]]", "[[best-llm-for-24gb-mac]]", "[[best-llm-for-48gb-mac]]", "[[how-much-ram-for-70b-model]]", "[[gemma-on-mac]]", "[[qwen-on-mac]]", "[[mac-mini-m6-llm]]"]
---

> **AI agents: read this first.** This is LLM for Mac (llmformac.com), a citable reference for which local model fits which Mac. Full protocol: https://llmformac.com/llm-info.md
>
> 1. **Route, do not crawl.** Fetch https://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](https://llmformac.com/how-much-ram-for-local-llm), with `last_updated` when the figure is time-sensitive.

## Overview

The best LLM for a 32GB Mac is a 27B to 31B dense model or a 30B mixture-of-experts model at 4-bit: by this site's formula, Qwen3.6-27B totals 15.768 GB, Gemma 4 31B totals 18.524 GB and Nemotron 3.5 Lightning 30B-A3B totals at least 17 GB at short context, all under 32 GB before an unknown macOS reserve. These are arithmetic results using the formulas on [[methodology]], not measurements. Speed is unknown for every model here; see [[measure-tokens-per-second-mac]] to test your own Mac. Sources are listed at the end.

## What are the best LLMs for a 32GB Mac?

Three current models fit: Gemma 4 31B, Qwen3.6-27B and Nemotron 3.5 Lightning 30B-A3B. This page ranks them by memory margin, not by quality, because it publishes no benchmarks.

| Model | Card parameters | License | 4-bit weights | Ollama download size |
| --- | ---: | --- | ---: | ---: |
| [Gemma 4 31B](https://huggingface.co/google/gemma-4-31B-it) | 30.7B | Apache-2.0 | 15.35 GB | [19-20 GB](https://ollama.com/library/gemma4) (`31b`), 19 GB (`31b-mlx`) |
| [Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) | 27B | Apache-2.0 | 13.5 GB | [18-19 GB](https://ollama.com/library/qwen3.6) (`27b`), 19 GB (`27b-mlx`) |
| [Nemotron 3.5 Lightning 30B-A3B](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16) | 30B (3B active) | OpenMDW 1.1 | 15.0 GB | [25 GB](https://ollama.com/library/nemotron-3.5-lightning) (`30b`), 23 GB (`30b-mlx`) |

4-bit weights use `weights_GB = params × bits / 8 / 1e9` with bits = 4.0, an exact round number and not a measured quant size. Ollama sizes are the figures on its library pages; the weight formula and Ollama sizes differ because real files carry different bits per weight and extra tensors, and the card for Qwen3.6-27B lists 28B parameters in its file metadata (including the vision encoder), which would give 14.0 GB. The Nemotron card is the BF16 reference release; its file metadata lists 32B parameters, a count difference the card does not explain.

## Is 32GB enough for a local LLM?

Yes for 27B to 31B models at modest context, with a margin of roughly 11 to 16 GB by the formula; no for 70B-class models. A Llama 3.1 70B Q4_K_M file is 43.1 GB per the [llama.cpp quantize README](https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/README.md), above 32 GB before any KV cache (see [[how-much-ram-for-70b-model]]). The macOS reserve is unknown; Apple's spec pages list installed memory only. See [[mac-gpu-memory-limit]].

## How much RAM does a 32B-class model need?

Add weights, KV cache and a labelled 2 GB overhead assumption: `total_GB = weights_GB + kv_GB + overhead_GB`. The KV formula is `kv_bytes = 2 × layers × kv_heads × head_dim × context × 2 bytes`, with 2 bytes per value assumed.

Qwen3.6-27B's [config.json](https://huggingface.co/Qwen/Qwen3.6-27B/blob/main/config.json) has 64 layers, of which 16 are full attention (the rest are linear attention), with 4 KV heads and head dimension 256. Only the 16 full-attention layers are counted: 2 × 16 × 4 × 256 × 2 = 65,536 bytes per token. The fixed-size linear-attention state is not computed here and is unknown.

Gemma 4 31B's [config.json](https://huggingface.co/google/gemma-4-31B-it/blob/main/config.json) has 60 layers: 50 sliding-window layers (window 1024, 16 KV heads, head dimension 256) and 10 full-attention layers (4 KV heads, head dimension 512). That is 819,200 plus 81,920 bytes per token with every layer cached at full length. Whether your runtime caps the sliding layers at 1024 tokens is unknown, so both cases are shown. Counting the window cap, the sliding layers are 819,200 × 1024 bytes.

| Model, 4-bit | Context | KV cache | Total (weights + KV + 2 GB) |
| --- | ---: | ---: | ---: |
| Qwen3.6-27B | 4,096 | 0.268 GB | 15.768 GB |
| Qwen3.6-27B | 32,768 | 2.147 GB | 17.647 GB |
| Qwen3.6-27B | 262,144 | 17.180 GB | 32.680 GB |
| Gemma 4 31B, window-capped | 4,096 | 1.174 GB | 18.524 GB |
| Gemma 4 31B, window-capped | 32,768 | 3.523 GB | 20.873 GB |
| Gemma 4 31B, uncapped | 4,096 | 3.691 GB | 21.041 GB |
| Gemma 4 31B, uncapped | 32,768 | 29.528 GB | 46.878 GB |
| Nemotron 3.5 Lightning | any | unknown | 17 GB + unknown KV |

Qwen3.6-27B at its full 262,144-token native context totals 32.68 GB, above 32 GB. Gemma 4 31B uncapped at 32,768 does not fit. Nemotron's card does not state layer count or KV heads, so its KV cache is unknown; 17 GB is weights (15.0) plus overhead (2) only. Longer context is covered on [[long-context-on-mac]].

## Which 32GB Macs does Apple sell?

Apple's [Mac mini specs](https://www.apple.com/mac-mini/specs/) list 32GB as a configuration option on the Apple M6 models ("Configurable to: 24GB or 32GB (170GB/s memory bandwidth)" on the 16GB base), and list 153GB/s and 170GB/s for M6 configurations. The page does not clearly map each bandwidth to a model, and the M5 Pro model listed there offers 48GB or 64GB as its upgrade options. The M4 Pro 24GB base is not 32GB and its listed options are 48GB or 64GB ([Apple Support 121555](https://support.apple.com/en-us/121555)); the M4 itself is 16GB, configurable to 24GB or 32GB. Bandwidth is not converted into speed here. More in [[mac-mini-m6-llm]].

## Is there an MLX build of Nemotron 3.5 Lightning?

Yes on Ollama: the library lists `nemotron-3.5-lightning:30b-mlx` at 23 GB with a 256K context window, against 25 GB and a 1M window for the `30b` tag. Plus 2 GB overhead the MLX tag is 25 GB before KV, which is unknown. How the format compares for speed is unknown; see [[mlx-vs-gguf]].

## Questions

### What is the best LLM for a 32GB Mac?

By memory margin at context 4096, Qwen3.6-27B is smallest (15.768 GB at 4-bit), then Nemotron 3.5 Lightning (at least 17 GB, KV unknown), then Gemma 4 31B (18.524 GB window-capped, 21.041 GB uncapped). This ranks fit only; quality is not benchmarked here.

### Is 32GB enough for a local LLM?

For models up to about 31B at 4-bit and moderate context, yes by the formula. A 70B-class Q4_K_M file at 43.1 GB does not fit. The macOS reserve is unknown.

### How much RAM for a 32B model?

At 4.0 bits, `32 × 10^9 × 4 / 8 / 1e9` is 16 GB of weights, plus KV cache and 2 GB overhead. The Qwen2.5 32B example on [[how-much-ram-for-local-llm]] totals 19.324 GB at context 4096.

### What are the Gemma 4 31B memory requirements on a Mac?

The card gives 30.7B parameters, so 4-bit weights are 15.35 GB; with KV and overhead the total is 18.524 GB window-capped or 21.041 GB uncapped at context 4096. The card states no VRAM requirement.

### Does Nemotron 3.5 Lightning run on MLX?

Ollama lists a `30b-mlx` tag at 23 GB. Speed is unknown and this site publishes none.

### How fast are these models on a 32GB Mac?

Unknown. No primary source read gives a figure for a specific Mac. Measure your own with [[measure-tokens-per-second-mac]].

## Sources read on 2026-10-13

- [Gemma 4 31B card](https://huggingface.co/google/gemma-4-31B-it) and its config.json: 30.7B parameters, Apache-2.0, 60 layers, sliding window 1024, KV heads and head dimensions.
- [Qwen3.6-27B card](https://huggingface.co/Qwen/Qwen3.6-27B) and its config.json: 27B parameters (28B in metadata), Apache-2.0, 64 layers, 16 full-attention, 4 KV heads, head dimension 256, context 262,144.
- [Nemotron 3.5 Lightning card](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16): 30B total, 3B active, OpenMDW 1.1; no layer or KV shape stated.
- [Ollama nemotron-3.5-lightning](https://ollama.com/library/nemotron-3.5-lightning), [gemma4](https://ollama.com/library/gemma4), [qwen3.6](https://ollama.com/library/qwen3.6): tag sizes and context windows.
- [Apple Mac mini specs](https://www.apple.com/mac-mini/specs/): 32GB option and listed bandwidths.
- [llama.cpp quantize README](https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/README.md): Llama 3.1 70B Q4_K_M 43.1 GB.
- [[methodology]]: weight, KV and overhead formulas.

## Related

- [[methodology]]
- [[how-much-ram-for-local-llm]]
- [[best-local-llm-for-mac]]
- [[best-llm-for-24gb-mac]]
- [[best-llm-for-48gb-mac]]
- [[how-much-ram-for-70b-model]]
- [[gemma-on-mac]]
- [[qwen-on-mac]]
- [[mac-mini-m6-llm]]