Can a MacBook Pro M5 (14-inch), 32GB run local LLMs?

Yes — 22 of the 48 current models on this site fit in its 21 GB of usable memory, and four more if you keep the window shorter than 32k. The strongest it holds, Qwen3.8 27B, runs at 6.2 tok/s with a 32k window, worked out from memory bandwidth. Whether that saves you money is a different question: at $2,399 and 500k tokens a day, it pays back in 157 years.

Run the numbers on this machine

What the MacBook Pro M5 (14-inch), 32GB runs

They do not all hold the same window. The weights are a fixed size, but the key-value cache grows with every token you keep, so what is left of 21 GB after the weights is how far the context goes. On 5 of the 12 below, this machine's memory is what runs out first: Qwen3.6 27B stops soonest, at 32k. The rest reach 256k, where the calculator's list ends, or the longest setting their own context limit allows.

Speed and memory are at 32k context, the setting the calculator starts on; the memory column is the weights plus the cache for that much of it. Each speed says how it was arrived at, and every one here is estimated from this machine's 153 GB/s of memory bandwidth rather than taken from a published benchmark. There is a page on how that sum works, and what each size needs, and one on what 32 GB runs, machine by machine. The longest context is the longest setting the calculator offers that this machine still holds the model at, cache included, and each one opens the calculator on that model at that length. A figure tagged memory is one this machine ran out of room for, and the rest are stopped by the model's own limit or by the end of the list.

For scale, the calculator starts from 80 tok/s for a hosted API and times a local machine against it. Nothing above reaches it, and the quickest is 45 tok/s.

On a laptop, sustained speed drops once the chassis warms up and the fans cap out, so a desktop with the same chip will hold a higher tokens/sec over a long generation than the figures here suggest.

10 more fit. Ranked below the twelve above: Gemma 4 E4B, LFM2.5 2.6B, Devstral Small 2 24B, Ministral 3 14B, Ministral 3 8B. The other 5 have no intelligence-index score, so the ranking cannot place them: LFM3 1.7B, Ornith 1.5 9B, Spark-X2.5 4B, Spark-X2.6 4B, LFM2.5 24B-A2B. Their pages show what each one needs and what runs it.

The 48 counted above are the current models. This site prices 16 superseded ones as well, because people still run them, and this machine holds seven of those at 32k, strongest first: Llama 3.1 8B Instruct at Q4_K_M, Llama 3.1 8B Instruct at Q8_0, Mistral Small 3.2 24B Instruct, Qwen3 14B, Qwen3 8B, Gemma 3 27B it, Gemma 3 12B it. Each of those pages asks whether the model is still worth running.

Four more, at a shorter window

The table above counts what fits at 32k, the context the calculator starts on. Four more models fit this machine at a shorter one. Same weights, smaller cache.

Each window is the longest setting the calculator offers that this machine still holds the model at, and it opens the calculator on that model at that length. The memory figures are the weights plus the key-value cache at that window, against the 21 GB this machine's GPU can use.

Other machines to weigh against the MacBook Pro M5 (14-inch), 32GB

Every row uses the same defaults as the figures above: 500k tokens a day at 15:1 input to output, 32k context, and each machine's strongest model that fits, counted against the same 48 current models. The GeForce RTX 5090, 32GB is priced as the card alone, so add the PC around it before comparing it with a complete computer. All seven cards here are ranked by what each one holds.

Head to head with another computer: Mac Studio M5 Max, 36GB · Mac mini M5 Pro, 48GB. With the same machine at another memory size: 16GB. There are 111 machine match-ups on the site.

The specifics

Chip

M5 (14-inch) — 10-core CPU / 10-core GPU

Memory bandwidth

153 GB/s

Usable by the GPU

21 GB — macOS lets the GPU wire roughly two-thirds of unified memory by default (llama.cpp discussion #2182: 66.7% at 32 GiB or less, 75% above). Raise it with `sudo sysctl iogpu.wired_limit_mb`.

Power under load

65 W (stand-in) — Apple has published no power figures for the 2026 Macs, and none at all for sustained GPU load on a laptop. Stand-in: the desktop maximum for the equivalent chip (M4 Mac mini, 65 W). A laptop draws less than that, so the electricity line here is an over-estimate.

Availability

Shipping.

Sources

Apple (specs), Apple (store), Apple Support