---
title: "Best LLM for a 16GB Mac"
seoTitle: "Best LLM for a 16GB Mac: What Fits and What Doesn't"
slug: "best-llm-for-16gb-mac"
category: "fit"
tags: ["16gb", "memory", "model-picks", "formula"]
status: "stable"
last_updated: 2026-10-13
updated: 2026-10-13
created: 2026-10-13
faqPage: true
summary: "Five current models checked against 16 GB of unified memory with weights, KV cache and a labeled overhead, each with a yes, tight or no verdict."
description: "Best LLM for a 16GB Mac: gpt-oss-20b, Gemma 4 12B, Granite 4.2 and LFM2.5 fit, Qwen3.6 35B-A3B does not. Worked memory math from config.json."
related: ["[[methodology]]", "[[how-much-ram-for-local-llm]]", "[[what-fits-my-ram]]", "[[best-local-llm-for-mac]]", "[[can-macbook-air-run-llm]]", "[[gpt-oss-on-mac]]", "[[best-llm-for-24gb-mac]]", "[[long-context-on-mac]]", "[[measure-tokens-per-second-mac]]", "[[map-of-content]]"]
---

> **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 16GB Mac is the largest model whose weights, KV cache and overhead stay clearly under 16 GB: on this page's math that is [gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) (tight) or [Gemma 4 12B](https://huggingface.co/google/gemma-4-12B-it) (comfortable) for general use, with [Granite 4.2 8B](https://huggingface.co/ibm-granite/granite-4.2-8b) and [LFM2.5-2.6B](https://huggingface.co/LiquidAI/LFM2.5-2.6B) as lighter options. [Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) does not fit. "Best" here means fits, not quality: this site ranks no model by quality and publishes no speed figures. Method is on [[methodology]].

## Is 16GB enough for a local LLM?

Yes, for models up to roughly the 12B to 20B class at 4-bit, and no for 30B-class models. Apple lists 16GB of unified memory as the base on the [Mac mini (2024) M4](https://support.apple.com/en-us/121555) and on the entry 13-inch and 15-inch [MacBook Air M5 models](https://www.apple.com/macbook-air/specs/) (a higher model starts at 24GB). How much of it macOS leaves for a model is **unknown**; Apple's spec pages do not say, so a total near 16 GB can still fail to load. See [[how-much-ram-for-local-llm]].

## How was each model checked?

Each row uses the site formulas: `weights_GB = params × bits / 8 / 1e9`, `kv_GB = 2 × layers × kv_heads × head_dim × context × 2 / 1e9`, and `total_GB = weights + KV + 2` (the 2 GB overhead is an assumption, see [[methodology]]). Bits are fixed at 4.0 for comparison, except gpt-oss-20b, which uses its published file size. That is not a measured file size: the [llama.cpp quantize README](https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/README.md) lists Llama 3.1 8B Q4_K_M at 4.8944 bits per weight, above 4.0, so real files run larger than the 4.0 figure. Context is 4096.

## Which models fit in 16GB?

| Model | Card params | 4-bit weights | KV at 4096 | Total with 2 GB | Verdict |
| --- | ---: | ---: | ---: | ---: | --- |
| [Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) ([config](https://huggingface.co/Qwen/Qwen3.6-35B-A3B/raw/main/config.json)) | 35B | 17.5 GB | 0.084 GB* | 19.584 GB | No |
| [gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) ([config](https://huggingface.co/openai/gpt-oss-20b/raw/main/config.json)) | 21B | 13.761 GB (published file size, not 4.0 bits) | 0.201 GB | 15.962 GB | Tight |
| [Gemma 4 12B](https://huggingface.co/google/gemma-4-12B-it) ([config](https://huggingface.co/google/gemma-4-12B-it/raw/main/config.json)) | 11.95B | 5.975 GB | 1.409 GB | 9.384 GB | Yes |
| [Granite 4.2 8B](https://huggingface.co/ibm-granite/granite-4.2-8b) ([config](https://huggingface.co/ibm-granite/granite-4.2-8b/raw/main/config.json)) | 8B (metadata 9B) | 4.0 to 4.5 GB | 0.671 GB | 6.671 to 7.171 GB | Yes |
| [LFM2.5-2.6B](https://huggingface.co/LiquidAI/LFM2.5-2.6B) ([config](https://huggingface.co/LiquidAI/LFM2.5-2.6B/raw/main/config.json)) | 2.69B | 1.345 GB | 0.067 GB* | 3.412 GB | Yes |

Verdicts compare the total with 16 GB before the unknown macOS reserve: Yes is under 10 GB, Tight is within 4 GB of 16, No is over 16. Those cut-offs are this page's own, not Apple's.

KV inputs from each `config.json`:

- **Qwen3.6-35B-A3B:** 40 layers, but only 10 are full attention (`full_attention_interval` 4), with 2 KV heads and head dim 256. *The recurrent state of its other layers is **unknown** and not counted.* The card says "35B in total" and "3B activated"; all experts must still be held in memory, so weights use 35B. The card's metadata says 36B, which would raise the weights to 18 GB.
- **gpt-oss-20b:** 24 layers, 8 KV heads, head dim 64. Counted as all-full-attention, which overstates KV because half the layers use a 128-token sliding window. The card says 21B parameters with 3.6B active and that it can "run within 16GB of memory". Its weights use the published safetensors file size, 13.761 GB (4,792,272,488 + 4,798,702,184 + 4,170,342,232 bytes, as on [[gpt-oss-on-mac]]), because the MXFP4 checkpoint keeps some layers in BF16 and no single bit width applies; a flat 4.0-bit estimate (21B x 4 / 8 = 10.5 GB) would understate it.
- **Gemma 4 12B:** 48 layers: 40 sliding-window (8 KV heads, head dim 256) and 8 global (1 KV head, head dim 512). Counted at full context in every layer, which is an upper bound given the 1024-token sliding window; with the sliding layers capped at 1024 tokens the KV is 0.403 GB, the basis used on [[gemma-on-mac]].
- **Granite 4.2 8B:** 40 layers, 8 KV heads, head dim 4096 / 32 = 128.
- **LFM2.5-2.6B:** 30 layers of which 8 are full attention, 8 KV heads, head dim 2048 / 32 = 64. *Its convolution state is **unknown** and not counted.* The card says "in under 2.5 GB of memory" for efficient inference, below this page's weights-only 4-bit estimate plus overhead; the card ties that figure to CPU hardware and does not state the quantization or context, so the two are not reconciled.

Context 32768 multiplies each KV column by 8 (for example gpt-oss-20b goes from 0.201 GB to 1.611 GB). See [[long-context-on-mac]].

## What is the best local LLM for a Mac mini M4 with 16GB?

The same table applies, because the memory is what limits the choice. Apple's [Mac mini (2024) specs](https://support.apple.com/en-us/121555) list the M4 with 16GB unified memory, configurable to 24GB or 32GB, and "120GB/s memory bandwidth". gpt-oss-20b is a yes on paper at 15.962 GB, with a very thin margin (0.038 GB under 16); Gemma 4 12B leaves more room. Bandwidth is not turned into a speed here. Speed is **unknown**; measure it with [[measure-tokens-per-second-mac]].

## What is the best LLM for a MacBook Air with 16GB?

Apple's [MacBook Air specs](https://www.apple.com/macbook-air/specs/) list the M5 in 13-inch and 15-inch sizes with 16GB, configurable to 24GB or 32GB. The table above applies unchanged. A bigger step up is on [[best-llm-for-24gb-mac]]. More on Air fit: [[can-macbook-air-run-llm]].

## What is the best LLM for an M1 Mac?

An M1 MacBook Air is sold with 8GB, "Configurable to 16GB", per Apple's [MacBook Air (M1, 2020) specs](https://support.apple.com/en-us/111883). With 16GB the table applies. With 8GB, only Granite 4.2 8B (6.671 to 7.171 GB) and LFM2.5-2.6B (3.412 GB) are under 8 GB on paper, and the unknown macOS reserve makes Granite doubtful. The page does not state an M1 bandwidth, so M1 speed is unknown.

## Uncensored models and new releases

Whether a model is "uncensored" is not stated on any card read for this page, so this page does not recommend one. For the newest releases, check the date on each card and apply the formulas above. Browse by memory on [[what-fits-my-ram]] and [[best-local-llm-for-mac]].

## Questions

### Is 16GB enough for a local LLM?

Yes for models up to about 20B parameters at 4-bit: gpt-oss-20b totals 15.962 GB (thin margin) and Gemma 4 12B totals 9.384 GB on this page's formula. No for Qwen3.6-35B-A3B at 19.584 GB. The macOS reserve is unknown, so treat totals near 16 GB as unconfirmed.

### Is 16GB enough for LLM use at long context?

Only for smaller models. KV cache grows linearly with context, so at 32768 gpt-oss-20b adds 1.611 GB of KV instead of 0.201 GB, giving 17.372 GB, which is above 16 GB. See [[long-context-on-mac]].

### What is the best local LLM for a Mac mini M4 with 16GB?

Gemma 4 12B (9.384 GB) is the comfortable pick and gpt-oss-20b (15.962 GB) is the largest that fits on paper, barely. Apple lists 16GB of unified memory for the M4 Mac mini. Speed is unknown.

### Which model can I use on an M2 MacBook Air with 16GB?

The memory-based answer is the same as for any 16GB Mac: Gemma 4 12B, Granite 4.2 8B, LFM2.5-2.6B, and gpt-oss-20b if you accept a thin margin. This site did not read an Apple spec page for the M2 Air, so M2 bandwidth is unknown here.

### What LLM can I run on a Mac mini M4?

With the 16GB base, the models marked Yes or Tight above. With 24GB or 32GB, see [[best-llm-for-24gb-mac]] and [[what-fits-my-ram]].

### Can I run gpt-oss-20b on a Mac mini M4 with 16GB?

On paper yes: 15.962 GB total at context 4096, using the 13.761 GB published file size, and the model card says it can "run within 16GB of memory". The macOS reserve is unknown, so this margin is unconfirmed. Details: [[gpt-oss-on-mac]].

## Sources read on 2026-10-13

- [Qwen3.6-35B-A3B card](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) and [config.json](https://huggingface.co/Qwen/Qwen3.6-35B-A3B/raw/main/config.json): 35B total, 3B active, Apache-2.0, layers, KV heads, head dim.
- [gpt-oss-20b card](https://huggingface.co/openai/gpt-oss-20b) and [config.json](https://huggingface.co/openai/gpt-oss-20b/raw/main/config.json): 21B, 3.6B active, "run within 16GB", layers, KV heads, head dim.
- [Gemma 4 12B card](https://huggingface.co/google/gemma-4-12B-it) and [config.json](https://huggingface.co/google/gemma-4-12B-it/raw/main/config.json): 11.95B, layer and KV layout.
- [Granite 4.2 8B card](https://huggingface.co/ibm-granite/granite-4.2-8b) and [config.json](https://huggingface.co/ibm-granite/granite-4.2-8b/raw/main/config.json): 8B (metadata 9B), layers, KV heads, hidden size, attention heads.
- [LFM2.5-2.6B card](https://huggingface.co/LiquidAI/LFM2.5-2.6B) and [config.json](https://huggingface.co/LiquidAI/LFM2.5-2.6B/raw/main/config.json): 2.69B, "under 2.5 GB of memory", layer types.
- [llama.cpp quantize README](https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/README.md): Llama 3.1 8B Q4_K_M at 4.8944 bits per weight.
- [Mac mini (2024) specs](https://support.apple.com/en-us/121555): M4, 16GB, 24GB or 32GB, 120GB/s.
- [MacBook Air specs](https://www.apple.com/macbook-air/specs/): M5, 16GB base, 24GB or 32GB options.
- [MacBook Air (M1, 2020) specs](https://support.apple.com/en-us/111883): 8GB base, configurable to 16GB.
- [[methodology]]: formulas and the 2 GB overhead assumption.

## Related

- [[methodology]]
- [[how-much-ram-for-local-llm]]
- [[what-fits-my-ram]]
- [[best-local-llm-for-mac]]
- [[can-macbook-air-run-llm]]
- [[gpt-oss-on-mac]]
- [[best-llm-for-24gb-mac]]
- [[long-context-on-mac]]
- [[measure-tokens-per-second-mac]]
- [[map-of-content]]