MacBook Air M5 (15-inch), 16GB vs AMD Radeon AI PRO R9700, 32GB for local AI
The Radeon AI PRO R9700, 32GB holds 33 of the 49 current models here, and the MacBook Air M5 (15-inch), 16GB holds 12. The Radeon AI PRO R9700, 32GB costs $200 less, though the Radeon AI PRO R9700, 32GB is priced as the card alone, without the PC around it. On Gemma 4 12B, the strongest model both hold, the Radeon AI PRO R9700, 32GB is about 5× faster: 70 tok/s against 14, both estimated from memory bandwidth. The Radeon AI PRO R9700, 32GB pays for itself sooner, in 84 years against 118 years at 500k tokens a day, though that is each machine on its own strongest model rather than on the same one.
| MacBook Air M5 (15-inch), 16GB | AMD Radeon AI PRO R9700, 32GB | |
|---|---|---|
| Price | $1,499 | $1,299card only |
| Memory | 16 GB | 32 GB |
| Usable by the GPU | 10.5 GB | 31 GB |
| Memory bandwidth | 153 GB/s | 640 GB/s |
| Power under load | 65 Wstand-in | 300 W |
| Models that fit | 12 | 33 |
| Best model it runs | Gemma 4 12B | Qwen3.8 27B |
| Speed on that model | 14 tok/s estimatedGemma 4 12B | 30 tok/s estimatedQwen3.8 27B |
| Pay-back on that model | Pays back in 118 years | Pays back in 84 years |
The 65 W beside the MacBook Air M5 (15-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 MacBook Air M5 (15-inch), 16GB · or the AMD Radeon AI PRO R9700, 32GB
Jump to: Side by side on Gemma 4 12B · How much use it takes to pay back · What the extra memory buys · The assumptions behind both columns
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.
| MacBook Air M5 (15-inch), 16GB | AMD Radeon AI PRO R9700, 32GB | |
|---|---|---|
| Speed | 14 tok/s estimated | 70 tok/s estimated |
| Pay-back | Pays back in 118 years | Pays back in 101 years |
Run Gemma 4 12B on the MacBook Air M5 (15-inch), 16GB · or on the AMD Radeon AI PRO R9700, 32GB
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 AMD Radeon AI PRO R9700, 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 | MacBook Air M5 (15-inch), 16GB | AMD Radeon AI PRO R9700, 32GB |
|---|---|---|
| 50ka few chats a day | 1,177 years | 1,010 years |
| 200klight assistant use | 294 years | 253 years |
| 1Ma moderate coding-assistant day | 59 years | 51 years |
| 4Mheavy coding with an agent | 15 years | 13 years |
| 20Magents running most of the day | 3.0 yearsits ceiling | 2.5 years |
On Gemma 4 12B the MacBook Air M5 (15-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 MacBook Air M5 (15-inch), 16GB at 20M tokens a day · or the AMD Radeon AI PRO R9700, 32GB
What the extra memory buys
The AMD Radeon AI PRO R9700, 32GB holds 21 models the MacBook Air M5 (15-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 AMD Radeon AI PRO R9700, 32GB |
|---|---|---|---|
| Qwen3.8 27BSonnet-class | 16 GB | 19 GB | 30 tok/s estimated |
| Qwen3.6 27BHaiku-class | 17 GB | 19 GB | 29 tok/s estimated |
| Nemotron 3.5 Bolt 74B-A9BHaiku-class | 22 GB | 22 GB | 114 tok/s estimated |
| Qwen3.6 35B-A3BHaiku-class | 22 GB | 23 GB | 132 tok/s estimated |
| Muse Glimmer 30BHaiku-class | 17 GB | 18 GB | 31 tok/s estimated |
| Gemma 4 26B-A4BHaiku-class | 17 GB | 18 GB | 97 tok/s estimated |
15 more, on the AMD Radeon AI PRO R9700, 32GB page.
The extra memory buys context as well. Of the 12 current models both machines hold at 32k, 7 run to a longer window on the AMD Radeon AI PRO R9700, 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 Air M5 (15-inch), 16GB, each the longest window the calculator offers that the machine still holds it at.
The MacBook Air M5 (15-inch), 16GB is also head to head with another computer: Mac mini M5 Pro, 24GB · Mac Studio M5 Max, 128GB · DGX Spark, 128GB · GMKtec EVO-X2, 128GB · MacBook Pro M5 Pro (16-inch), 64GB · MacBook Air M5 (13-inch), 16GB. With a graphics card: RTX PRO 6000 Blackwell, 96GB.
The Radeon AI PRO R9700, 32GB is also head to head with another card: RTX PRO 6000 Blackwell, 96GB · GeForce RTX 5090, 32GB · GeForce RTX 4090, 24GB · GeForce RTX 3090, 24GB · GeForce RTX 4080, 16GB · GeForce RTX 3060, 12GB. With a complete computer: Mac mini M5 Pro, 24GB · Mac Studio M5 Max, 128GB · DGX Spark, 128GB · GMKtec EVO-X2, 128GB · MacBook Pro M5 Pro (16-inch), 64GB · Mac mini M6, 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 Radeon AI PRO R9700, 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 MacBook Air M5 (15-inch), 16GB runs · everything the AMD Radeon AI PRO R9700, 32GB runs · every other match-up · the quickest pay-back at each level of use · every model against the frontier