NVIDIA GeForce RTX 5090, 32GB vs AMD Radeon AI PRO R9700, 32GB for local AI
Both hold 34 of the 50 current models here. The Radeon AI PRO R9700, 32GB costs $700 less, both priced as the card alone, without the PC around either. On Qwen3.8 27B, the strongest model both hold, the GeForce RTX 5090, 32GB is about 2.2× faster: 66 tok/s against 30, both estimated from memory bandwidth. The Radeon AI PRO R9700, 32GB pays for itself sooner, in 84 years against 123 years at 500k tokens a day.
| NVIDIA GeForce RTX 5090, 32GB | AMD Radeon AI PRO R9700, 32GB | |
|---|---|---|
| Price | $1,999card only | $1,299card only |
| Memory | 32 GB | 32 GB |
| Usable by the GPU | 31 GB | 31 GB |
| Memory bandwidth | 1792 GB/s | 640 GB/s |
| Power under load | 575 W | 300 W |
| Models that fit | 34 | 34 |
| Best model it runs | Qwen3.8 27B | Qwen3.8 27B |
| Speed on that model | 66 tok/s estimated | 30 tok/s estimated |
| Pay-back on that model | Pays back in 123 years | Pays back in 84 years |
Run the numbers on the NVIDIA GeForce RTX 5090, 32GB · or 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 Qwen3.8 27B, the strongest model both hold, 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 | NVIDIA GeForce RTX 5090, 32GB | AMD Radeon AI PRO R9700, 32GB |
|---|---|---|
| 50ka few chats a day | 1,228 years | 836 years |
| 200klight assistant use | 307 years | 209 years |
| 1Ma moderate coding-assistant day | 61 years | 42 years |
| 4Mheavy coding with an agent | 15 years | 10 years |
| 20Magents running most of the day | 3.1 years | 2.1 years |
Run the NVIDIA GeForce RTX 5090, 32GB at 20M tokens a day · or the AMD Radeon AI PRO R9700, 32GB
Memory is not what separates them
Every model on this list that fits one machine fits the other, and not only at 32k of context: across all 50 models the calculator counts, at every context from 4k to 256k, there is no model one holds and the other does not. Both leave 31 GB to the GPU. So the choice between them is speed, price and power, not what they can hold.
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 · GeForce RTX 4080, 16GB · GeForce RTX 3060, 12GB. With a complete computer: Framework Desktop, 64GB · MacBook Pro M5 (14-inch), 16GB.
The Radeon AI PRO R9700, 32GB is also head to head with another card: RTX PRO 6000 Blackwell, 96GB · 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 Air M5 (15-inch), 16GB · 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. Both are priced as the card alone, so neither figure includes the PC to put it in. 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 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