---
title: "Best LLM for a 128GB Mac"
seoTitle: "Best LLM for a 128GB Mac: MacBook Pro and Mac Studio"
slug: "best-llm-for-128gb-mac"
category: "fit"
tags: ["128gb", "gpt-oss", "deepseek", "m5-max", "m4-max"]
updated: 2026-10-13
status: "stable"
last_updated: 2026-10-13
created: 2026-10-13
faqPage: true
summary: "gpt-oss-120b is the clear fit for a 128GB Mac; DeepSeek V4-Flash fits only below about 3.5 bits per weight, by this site's arithmetic."
description: "Which local LLMs fit a 128GB Mac (M4 Max or M5 Max): gpt-oss-120b and DeepSeek V4-Flash, worked from model cards with the formula shown and no speed claims."
related: ["[[methodology]]", "[[best-llm-for-64gb-mac]]", "[[macbook-pro-m5-llm]]", "[[mac-studio-llm]]", "[[gpt-oss-on-mac]]", "[[deepseek-on-mac]]", "[[local-coding-llm-on-mac]]", "[[mac-gpu-memory-limit]]"]
---

> **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 128GB Mac is [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b): its released files total 65.249 GB, so the site's memory formula gives about 69.7 GB at 32,768 tokens of context, which is 58.3 GB under 128 GB. DeepSeek V4-Flash is the stretch option and fits only if you run it below roughly 3.5 bits per weight. Apple lists 128GB on the M5 Max (MacBook Pro and Mac Studio) and on the M4 Max (MacBook Pro, per Apple's 2024 announcement, and the 2025 Mac Studio, per Apple Support). Every number below is a quote from a linked source or arithmetic with the formula shown, using [[methodology]]. This page gives no tokens per second because no primary source supplies one for these Macs; measure your own with [[measure-tokens-per-second-mac]].

## Which Macs have 128GB?

Apple lists 128GB on the M5 Max in the MacBook Pro and Mac Studio, and "up to 128GB" for the M4 Max in its 2024 announcement. [Apple Support 122211](https://support.apple.com/en-us/122211) lists the 2025 Mac Studio with M4 Max at 36GB, configurable to 48GB, 64GB or 128GB (128GB with the 16-core CPU and 40-core GPU).

| Mac | Chip | Unified memory | Memory bandwidth (Apple) |
| --- | --- | --- | --- |
| [MacBook Pro](https://www.apple.com/macbook-pro/specs/) | M5 Max, 40-core GPU | up to 128GB | 614GB/s |
| [MacBook Pro](https://www.apple.com/macbook-pro/specs/) | M5 Max, 32-core GPU | 128GB is listed only for the 40-core GPU | 460GB/s |
| [Mac Studio](https://www.apple.com/mac-studio/specs/) | M5 Max | 36GB base; 48GB, 64GB or 128GB | 460GB/s; 614GB/s with the 40-core GPU |
| [Mac Studio](https://www.apple.com/mac-studio/specs/) | M5 Ultra | 96GB base; 256GB or 512GB | 1.2TB/s |
| Earlier MacBook Pro | M4 Max | up to 128GB | up to 546GB/s ([Apple newsroom, 2024](https://www.apple.com/newsroom/2024/10/apple-introduces-m4-pro-and-m4-max/)) |

Bandwidth is not turned into a speed on this page. The share of unified memory macOS lets the GPU use is **unknown**; see [[mac-gpu-memory-limit]].

## Can I run gpt-oss-120b on a Mac?

Yes by the arithmetic: the weights are 65.249 GB and the total stays under 128 GB even at the model's full 131,072-token context (`max_position_embeddings` in the config).

The [model card](https://huggingface.co/openai/gpt-oss-120b) says "117B parameters with 5.1B active parameters", MXFP4-quantized MoE weights, an Apache 2.0 license, and that the model can "fit into a single 80GB GPU". The 15 safetensors files in its [file tree](https://huggingface.co/openai/gpt-oss-120b/tree/main) sum to 65,248,893,184 bytes, which is 65.249 GB. That figure is the released size, so no bits-per-weight guess is needed.

The KV formula is `kv_GB = 2 × layers × kv_heads × head_dim × context × 2 / 1e9`. The [config.json](https://huggingface.co/openai/gpt-oss-120b/blob/main/config.json) gives 36 layers, 8 KV heads and head_dim 64, so each token costs 2 × 36 × 8 × 64 × 2 = 73,728 bytes. Half the layers use sliding attention (window 128), so this over-counts and is an upper bound.

| Context | KV cache | Total with 2 GB overhead |
| ---: | ---: | ---: |
| 4,096 | 0.302 GB | 67.551 GB |
| 32,768 | 2.416 GB | 69.665 GB |
| 131,072 | 9.664 GB | 76.913 GB |

All three are under 128 GB, and under the 96GB Mac Studio base. None fits 64 GB. Setup is on [[gpt-oss-on-mac]].

## Can a 128GB Mac run DeepSeek V4-Flash?

Only below about 3.5 bits per weight, and only if a Mac runtime supports that quant, which this page has not verified.

The [card](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash) says "284B parameters (13B activated)", MIT license, one-million-token context, and "FP4 + FP8 Mixed" weights. Its [file tree](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash/tree/main) sums to 159.617 GB as released, so the original files do not fit in 128 GB.

Using `weights_GB = params × bits / 8 / 1e9` with 284B parameters:

| Bits per weight | Weights | Plus 2.886 GB KV (32,768) and 2 GB overhead | Under 128 GB? |
| ---: | ---: | ---: | --- |
| 4.0 | 142.0 GB | 146.886 GB | No |
| 3.0 | 106.5 GB | 111.386 GB | Yes, 16.6 GB spare |
| 2.0 | 71.0 GB | 75.886 GB | Yes |

The break-even is (128 − 2 − 2.886) × 8 / 284 = 3.468 bits. The KV figure uses the [config.json](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash/blob/main/config.json) values (43 layers, 1 KV head, head_dim 512), but that config has extra indexing fields, so the real cache for this architecture is **unknown**. Output quality at 3 bits or less is also unknown. See [[deepseek-on-mac]].

## 128GB or 64GB: is the upgrade worth it?

It is worth it if you want models whose totals fall between about 64 and 128 GB; otherwise no. Using the Qwen2.5-72B figures on [[methodology]], 4-bit is 39.692 GB, which fits 64 GB, and 8-bit is 76.042 GB, which needs 128 GB. gpt-oss-120b at 69.665 GB (32,768 context) also needs 128 GB. If your models stay under roughly 40 GB, see [[best-llm-for-64gb-mac]]. Prices are **unknown** here; check Apple's configurator.

## M5 Max 128GB or M5 Ultra 96GB?

For gpt-oss-120b, memory is not the difference: 69.665 GB fits both. At 96GB, DeepSeek V4-Flash needs (96 − 2 − 2.886) × 8 / 284 = 2.566 bits or less, versus 3.468 at 128GB. Apple lists the M5 Ultra at 1.2TB/s against 614GB/s for the 40-core M5 Max; what that does for speed is **unknown**. Details on [[mac-studio-llm]] and [[macbook-pro-m5-llm]].

## Can it replace a cloud coding assistant?

Unknown. Fit is answerable by arithmetic; quality against a hosted model is not, and this site has no benchmark for it. See [[local-coding-llm-on-mac]].

## Questions

### What is the best LLM for a 128GB Mac?

gpt-oss-120b. Its released weights are 65.249 GB, and the formula total is 69.665 GB at 32,768 tokens of context, well under 128 GB. DeepSeek V4-Flash fits only below about 3.5 bits per weight.

### Is a 128GB MacBook Pro worth it for local LLM?

It is worth it if you want models in the 64 to 128 GB range such as gpt-oss-120b, which needs 69.665 GB at 32,768 context and does not fit 64 GB. If your models total under about 40 GB, 64 GB is enough. Price is unknown here.

### Can I run gpt-oss-120b on a Mac?

By the arithmetic, yes on a 128GB Mac: 65.249 GB of weights plus 2.416 GB of KV at 32,768 context plus 2 GB of overhead is 69.665 GB. It does not fit 64 GB. Speed is unknown.

### Can a 128GB MacBook Pro run DeepSeek V4-Flash?

Only quantized below 3.468 bits per weight at 32,768 context, and the original 159.617 GB files do not fit. Whether a Mac runtime supports such a quant is unverified.

### Is M5 Max 128GB better than M5 Ultra 96GB for local LLMs?

For capacity, 128 GB leaves more room: gpt-oss-120b fits both, DeepSeek V4-Flash needs 2.566 bits at 96 GB against 3.468 at 128 GB. Apple lists 1.2TB/s for the M5 Ultra and 614GB/s for the 40-core M5 Max; the speed difference is unknown.

### Does a 128GB Mac need the 40-core M5 Max?

For MacBook Pro, Apple's spec page ties the 128GB option to the 40-core GPU M5 Max, which lists 614GB/s.

### How fast is a 128GB Mac for local LLMs?

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

## Sources read on 2026-10-13

- [Apple MacBook Pro specs](https://www.apple.com/macbook-pro/specs/): M5 Max memory options and 460/614GB/s bandwidth.
- [Apple Mac Studio specs](https://www.apple.com/mac-studio/specs/): M5 Max 36GB base with 48/64/128GB options; M5 Ultra 96GB base, 1.2TB/s.
- [Apple newsroom, M4 Pro and M4 Max](https://www.apple.com/newsroom/2024/10/apple-introduces-m4-pro-and-m4-max/): "up to 128GB" and "up to 546GB/s".
- [gpt-oss-120b card](https://huggingface.co/openai/gpt-oss-120b), [config.json](https://huggingface.co/openai/gpt-oss-120b/blob/main/config.json) and [file tree](https://huggingface.co/openai/gpt-oss-120b/tree/main): 117B/5.1B active, MXFP4, Apache 2.0, 80GB statement, KV shape, shard sizes.
- [DeepSeek-V4-Flash card](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash), [config.json](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash/blob/main/config.json) and [file tree](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash/tree/main): 284B/13B activated, FP4+FP8, MIT, 1M context, KV shape, shard sizes.
- [llama.cpp quantize README](https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/README.md): read; no gpt-oss or MXFP4 mention, no figure taken.
- [[methodology]]: formulas, 2 GB overhead assumption, Qwen2.5-72B totals.

## Related

- [[methodology]]
- [[best-llm-for-64gb-mac]]
- [[macbook-pro-m5-llm]]
- [[mac-studio-llm]]
- [[gpt-oss-on-mac]]
- [[deepseek-on-mac]]
- [[local-coding-llm-on-mac]]
- [[mac-gpu-memory-limit]]
- [[measure-tokens-per-second-mac]]