AMD Radeon AI PRO R9700, 32GB vs NVIDIA GeForce RTX 4080, 16GB for local AI
The Radeon AI PRO R9700, 32GB holds 27 of the 39 open models here, and the GeForce RTX 4080, 16GB holds 13. The GeForce RTX 4080, 16GB costs $100 less, both priced as the card alone, without the PC around either. On Gemma 4 12B, the strongest model both hold, the GeForce RTX 4080, 16GB is about 1.1× faster: 74 tok/s against 70, both estimated from memory bandwidth. The Radeon AI PRO R9700, 32GB pays for itself sooner, in 17 years against 93 years at 500k tokens a day, though that is each machine on its own strongest model rather than on the same one. The GeForce RTX 4080, 16GB is the previous generation, so every figure here for it is priced at what it launched at rather than at a price you can pay today.
| AMD Radeon AI PRO R9700, 32GB | NVIDIA GeForce RTX 4080, 16GB | |
|---|---|---|
| Price | $1,299card only | $1,199card only |
| Memory | 32 GB | 16 GB |
| Usable by the GPU | 31 GB | 15 GB |
| Memory bandwidth | 640 GB/s | 716.8 GB/s |
| Power under load | 300 W | 320 W |
| Models that fit | 27 | 13 |
| Best model it runs | Qwen3.8 27B | Gemma 4 12B |
| Speed on that model | 30 tok/s estimatedQwen3.8 27B | 74 tok/s estimatedGemma 4 12B |
| Pay-back on that model | Pays back in 17 years | Pays back in 93 years |
Run the numbers on the AMD Radeon AI PRO R9700, 32GB · or the NVIDIA GeForce RTX 4080, 16GB
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.
| AMD Radeon AI PRO R9700, 32GB | NVIDIA GeForce RTX 4080, 16GB | |
|---|---|---|
| Speed | 70 tok/s estimated | 74 tok/s estimated |
| Pay-back | Pays back in 101 years | Pays back in 93 years |
Run Gemma 4 12B on the AMD Radeon AI PRO R9700, 32GB · or on the NVIDIA GeForce RTX 4080, 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 4080, 16GB 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 | AMD Radeon AI PRO R9700, 32GB | NVIDIA GeForce RTX 4080, 16GB |
|---|---|---|
| 50ka few chats a day | 1,010 years | 934 years |
| 200klight assistant use | 253 years | 234 years |
| 1Ma moderate coding-assistant day | 51 years | 47 years |
| 4Mheavy coding with an agent | 13 years | 12 years |
| 20Magents running most of the day | 2.5 years | 2.3 years |
Run the AMD Radeon AI PRO R9700, 32GB at 20M tokens a day · or the NVIDIA GeForce RTX 4080, 16GB
What the extra memory buys
The AMD Radeon AI PRO R9700, 32GB holds 14 models the NVIDIA GeForce RTX 4080, 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 |
| 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 |
| Gemma 4 31B itHaiku-class | 20 GB | 26 GB | 22 tok/s estimated |
8 more, on the AMD Radeon AI PRO R9700, 32GB page.
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 AMD Radeon AI PRO R9700, 32GB runs · everything the NVIDIA GeForce RTX 4080, 16GB runs · every other match-up · the quickest pay-back at each level of use · every model against the frontier