NVIDIA GeForce RTX 5090, 32GB vs MacBook Pro M5 (14-inch), 16GB for local AI
The GeForce RTX 5090, 32GB holds 27 of the 39 open models here, and the MacBook Pro M5 (14-inch), 16GB holds 10. They cost the same, though the GeForce RTX 5090, 32GB is priced as the card alone, without the PC around it. On Gemma 4 12B, the strongest model both hold, the GeForce RTX 5090, 32GB is about 11.1× faster: 155 tok/s against 14, both estimated from memory bandwidth. The GeForce RTX 5090, 32GB pays for itself sooner, in 25 years against 157 years at 500k tokens a day, though that is each machine on its own strongest model rather than on the same one.
| NVIDIA GeForce RTX 5090, 32GB | MacBook Pro M5 (14-inch), 16GB | |
|---|---|---|
| Price | $1,999card only | $1,999 |
| Memory | 32 GB | 16 GB |
| Usable by the GPU | 31 GB | 10.5 GB |
| Memory bandwidth | 1792 GB/s | 153 GB/s |
| Power under load | 575 W | 65 Wstand-in |
| Models that fit | 27 | 10 |
| Best model it runs | Qwen3.8 27B | Gemma 4 12B |
| Speed on that model | 66 tok/s estimatedQwen3.8 27B | 14 tok/s estimatedGemma 4 12B |
| Pay-back on that model | Pays back in 25 years | Pays back in 157 years |
The 65 W beside the MacBook Pro M5 (14-inch), 16GB is a stand-in, not a figure for that machine: the data borrows it from the nearest hardware it does have, and the machine's own page names which and why. So the two figures above are not like for like, and the electricity in its pay-back here is priced from a borrowed number.
Run the numbers on the NVIDIA GeForce RTX 5090, 32GB · or the MacBook Pro M5 (14-inch), 16GB
The same money, two different machines
These two cost the same: $1,999 for the GeForce RTX 5090, 32GB, card only and $1,999 for the MacBook Pro M5 (14-inch), 16GB. NVIDIA makes one and Apple the other. Every other head-to-head on this site holds a piece of the hardware equal and asks what the price gap buys. This one holds the price, so the row that usually carries the answer is the row the two machines agree on, and everything under it is what the same money buys twice. The two are less equal than they look: the GeForce RTX 5090, 32GB's price buys the card alone, so read its column as the cost of the part that does the work on top of a machine you already own.
Side by side on Gemma 4 12B
The table above gives each machine the strongest model it can hold, and those are not the same model, so the two speeds in it are not a race. Gemma 4 12B is the strongest model both machines hold, so this is the pair running the same work.
| NVIDIA GeForce RTX 5090, 32GB | MacBook Pro M5 (14-inch), 16GB | |
|---|---|---|
| Speed | 155 tok/s estimated | 14 tok/s estimated |
| Pay-back | Pays back in 152 years | Pays back in 157 years |
Run Gemma 4 12B on the NVIDIA GeForce RTX 5090, 32GB · or on the MacBook Pro M5 (14-inch), 16GB
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 the same model, at the five levels of use the calculator names. The NVIDIA GeForce RTX 5090, 32GB 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 | NVIDIA GeForce RTX 5090, 32GB | MacBook Pro M5 (14-inch), 16GB |
|---|---|---|
| 50ka few chats a day | 1,517 years | 1,569 years |
| 200klight assistant use | 379 years | 392 years |
| 1Ma moderate coding-assistant day | 76 years | 78 years |
| 4Mheavy coding with an agent | 19 years | 20 years |
| 20Magents running most of the day | 3.8 years | 4.0 yearsits ceiling |
On Gemma 4 12B the MacBook Pro M5 (14-inch), 16GB generates at most 19.8M tokens a day, so its figure at 20M tokens a day is for the most it can do, not for the whole of what was asked.
Run the NVIDIA GeForce RTX 5090, 32GB at 20M tokens a day · or the MacBook Pro M5 (14-inch), 16GB
What the extra memory buys
The NVIDIA GeForce RTX 5090, 32GB holds 17 models the MacBook Pro M5 (14-inch), 16GB cannot at 32k of context. The strongest of them are what the difference in memory actually buys.
| Model | Weights | Needs at 32k | On the NVIDIA GeForce RTX 5090, 32GB |
|---|---|---|---|
| Qwen3.8 27BSonnet-class | 16 GB | 19 GB | 66 tok/s estimated |
| Qwen3.6 27BHaiku-class | 17 GB | 19 GB | 65 tok/s estimated |
| Qwen3.6 35B-A3BHaiku-class | 22 GB | 23 GB | 235 tok/s estimated |
| Muse Glimmer 30BHaiku-class | 17 GB | 18 GB | 69 tok/s estimated |
| Gemma 4 26B-A4BHaiku-class | 17 GB | 18 GB | 172 tok/s estimated |
| Gemma 4 31B itHaiku-class | 20 GB | 26 GB | 48 tok/s estimated |
11 more, on the NVIDIA GeForce RTX 5090, 32GB page.
The extra memory buys context as well. Of the 10 models both machines hold at 32k, 7 run to a longer window on the NVIDIA GeForce RTX 5090, 32GB: the weights are a fixed size and the KV cache is not, so what the weights leave spare is what a longer context grows into. Ministral 3 8B reaches 128k there against 32k on the MacBook Pro M5 (14-inch), 16GB, each the longest window the calculator offers that the machine still holds it at.
The GeForce RTX 5090, 32GB is also head to head with another card: RTX PRO 6000 Blackwell, 96GB · GeForce RTX 4090, 24GB · GeForce RTX 3090, 24GB · Radeon AI PRO R9700, 32GB · GeForce RTX 4080, 16GB · GeForce RTX 3060, 12GB. With a complete computer: Framework Desktop, 64GB.
The MacBook Pro M5 (14-inch), 16GB is also head to head with the same machine at another memory size: 32GB.
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. 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. Change any of it in the calculator.
More head to head: everything the NVIDIA GeForce RTX 5090, 32GB runs · everything the MacBook Pro M5 (14-inch), 16GB runs · every other match-up · the quickest pay-back at each level of use · every model against the frontier