MacBook Pro M5 Pro (16-inch): 48GB vs 64GB for local AI
Both hold 27 of the 39 open models here. The MacBook Pro M5 Pro (16-inch), 48GB costs $400 less. On Qwen3.8 27B, the strongest model both hold, they run at much the same speed: 12 and 12 tok/s, both estimated from memory bandwidth. The MacBook Pro M5 Pro (16-inch), 48GB pays for itself sooner, in 47 years against 52 years at 500k tokens a day.
| MacBook Pro M5 Pro (16-inch), 48GB | MacBook Pro M5 Pro (16-inch), 64GB | |
|---|---|---|
| Price | $3,599 | $3,999 |
| Memory | 48 GB | 64 GB |
| Usable by the GPU | 36 GB | 48 GB |
| Memory bandwidth | 307 GB/s | 307 GB/s |
| Power under load | 140 Wstand-in | 140 Wstand-in |
| Models that fit | 27 | 27 |
| Best model it runs | Qwen3.8 27B | Qwen3.8 27B |
| Speed on that model | 12 tok/s estimated | 12 tok/s estimated |
| Pay-back on that model | Pays back in 47 years | Pays back in 52 years |
Neither power figure above is measured on the machine beside it. Both are stand-ins borrowed from the nearest hardware the data does have, so what the row shows is one borrowed figure printed twice. Each machine's page names the figure it borrows and why. Every pay-back figure on this page prices its electricity from these numbers.
Run the numbers on the MacBook Pro M5 Pro (16-inch), 48GB · or the MacBook Pro M5 Pro (16-inch), 64GB
How much use it takes to pay back
Everything above is at 500k tokens a day. Pay-back moves with how much you actually run, so here are both machines on Qwen3.8 27B, the strongest model both hold, at the five levels of use the calculator names. The MacBook Pro M5 Pro (16-inch), 48GB pays back sooner at every level of use, so this is not a choice that turns on how hard you work it.
| A day's use | MacBook Pro M5 Pro (16-inch), 48GB | MacBook Pro M5 Pro (16-inch), 64GB |
|---|---|---|
| 50ka few chats a day | 466 years | 518 years |
| 200klight assistant use | 117 years | 129 years |
| 1Ma moderate coding-assistant day | 23 years | 26 years |
| 4Mheavy coding with an agent | 5.8 years | 6.5 years |
| 20Magents running most of the day | 16 monthsits ceiling | 18 monthsits ceiling |
On Qwen3.8 27B neither machine can generate 20M tokens a day: the MacBook Pro M5 Pro (16-inch), 48GB manages at most 17.1M and the MacBook Pro M5 Pro (16-inch), 64GB at most 17.1M. Both figures on that row are for the most each can do.
Run the MacBook Pro M5 Pro (16-inch), 48GB at 20M tokens a day · or the MacBook Pro M5 Pro (16-inch), 64GB
The same models, not to the same length
Every model on this list that fits one machine fits the other at 32k of context, so memory does not change what they run. What it changes is how far you can take the context on 3 of them. The MacBook Pro M5 Pro (16-inch), 64GB has 48 GB usable against 36 GB, and spare memory is what the KV cache grows into as you keep more tokens.
| Model | MacBook Pro M5 Pro (16-inch), 48GB | MacBook Pro M5 Pro (16-inch), 64GB |
|---|---|---|
| Gemma 4 31B it20 GB of weights | 64k | 128k |
| Qwen3-Coder 30B-A3B19 GB of weights | 128k | 256k |
| Ministral 3 8B5.2 GB of weights | 128k | 256k |
Each figure is the longest context the calculator offers that the machine still holds that model at, and no model is taken past its own context limit. The other 36 models the calculator counts reach the same length on both machines, at every setting from 4k to 256k.
Run Gemma 4 31B it on the MacBook Pro M5 Pro (16-inch), 48GB at 64k · or on the MacBook Pro M5 Pro (16-inch), 64GB at 128k
The assumptions behind both columns
Both columns use the same usage: 500k tokens a day at 15:1 input to output, 32k of context, $0.17 per kWh, and today's API prices held flat. Speeds marked estimated are worked out from memory bandwidth rather than measured, and pay-back scales with them. Graphics cards are priced as the card alone, so add the PC around one before comparing it with a complete computer. Change any of it in the calculator.
More head to head: everything the MacBook Pro M5 Pro (16-inch), 48GB runs · everything the MacBook Pro M5 Pro (16-inch), 64GB runs · every other match-up · the quickest pay-back at each level of use · every model against the frontier