Can an RX 5700 XT run AI? Measured: 63–72 tokens a second
Yes. On a desktop with an AMD RX 5700 XT (8 GB) and 48 GB of RAM, Gemma 4 E4B wrote 63–72 tokens a second. You read about 5, so answers come out faster than you can read them.
What that means in practice
| Task | Time on this PC |
|---|---|
| Write a normal answer (400 tokens, about 300 words) | 6 s |
| Read a long document you give it (5,000 tokens, about 7 pages) | 8 s |
Worked out from the speeds measured on this PC: time is length divided by speed, and a real answer also spends a moment getting started. For scale, most people read about 4 words a second (Brysbaert, 2019), so a 400-token answer takes you over a minute just to read.
What we measured
Writing is how fast the AI types its answer, in tokens a second; a token is about three quarters of an English word. Reading is how fast it takes in what you give it: your question, a pasted text or a PDF. Reading is always much faster than writing, because the AI can read many tokens at once but has to write them one by one.
| What | Ran on | Result | Measured |
|---|---|---|---|
| Gemma 4 E4B | graphics card | 63–72 tokens a second writing · 594–609 reading | 22 September 2026 |
| Gemma 4 E4B | processor only | 5 tokens a second writing · 357 reading | 22 September 2026 |
| Gemma 4 12B | graphics card | 20–36 tokens a second writing · 108–113 reading | 22 September 2026 |
How each number was measured:
- llama.cpp's own speed test (llama-bench), the engine GreenCube runs on, with the same AI files: 128 tokens written, a 512-token text read.
Where we have several runs we show the range: speeds move a little between runs on the same PC.
What it means
The graphics card does the work
Same PC, same AI (Gemma 4 E4B): 5 tokens a second on the processor alone, 63–72 on the graphics card, about 14 times faster. A graphics card is built to do thousands of small sums at once for games, and running an AI is mostly that same kind of maths. GreenCube times both when it starts and uses the faster one.
The whole AI fits on the card
Gemma 4 E4B is a 4.2 GB file and the card has 8 GB of its own memory (VRAM). All of it fits, with room left for the conversation, so the card never waits for the slower memory in the rest of the PC. That is the biggest reason it runs this fast.
The bigger Gemma 4 12B
Only for short chats. The bigger Gemma 4 12B (6.4 GB) wrote 20–36 tokens a second on a short question, but once the chat reached 8,192 tokens (a long conversation) it crashed, with the card almost full. That is why GreenCube only suggests the 12B on cards with 10 GB or more.
Pictures
Very likely, but we haven't timed a picture on this card yet. When we do, the number goes here. A picture uses its own AI of about 6 GB and far more maths than a chat answer, so the graphics matter even more here.
How it compares
Of the 8 PCs we measured with Gemma 4 E4B, this one is number 2 for writing speed. The RTX 2080 Ti writes 65–71, about the same. The RTX 5060 writes 56 (startup test), so this one is about 20% faster.
We measured 4 cards with 8 GB, and they are far apart:
- RX 5700 XT: 63–72 tokens a second
- RTX 5060: 56 tokens a second (startup test)
- RX 6600: 42–44 tokens a second
- RX 580: 10–11 tokens a second
Memory decides which AI fits. How new the card is decides how fast it runs.
The test PC
A real tester's own PC, used the way they normally use it, not a lab machine:
- Desktop
- Graphics: AMD RX 5700 XT
- Graphics memory (VRAM): 8 GB
- RAM: 48 GB
- Processor: Intel Core i7-9700, 8 threads
Check your own PC
Not sure what your PC has? It takes 30 seconds:
- Press Ctrl + Shift + Esc to open Task Manager.
- Go to Performance.
- Click Memory: the number at the top right is your RAM.
- Click GPU: the name at the top right is your card, and Dedicated GPU memory is your VRAM. Only see Intel or AMD graphics with no dedicated memory? Then you have no separate card.
Then find the closest card in the full table. The same card with more VRAM can fit a bigger AI; a newer card of the same size is usually faster.
Other PCs we measured
- RTX 2080 Ti: 65–71 tokens a second (Gemma 4 E4B)
- RTX 5060: 56 tokens a second (Gemma 4 E4B)
- RTX 3060: 53–55 tokens a second (Gemma 4 E4B)
- RX 6600: 42–44 tokens a second (Gemma 4 E4B)
- GTX 1660 Super: 37–38 tokens a second (Gemma 4 E4B)
- GTX 1650: about 20 tokens a second (Gemma 4 E4B)
- Intel Core Ultra 5 laptop: 14–18 tokens a second (Gemma 4 E2B)
- RX 580: 10–11 tokens a second (Gemma 4 E4B)
- Intel Iris Xe laptop: 5–7 tokens a second (Gemma 4 E2B)
Can my PC run AI? All 10 PCs in one table, and what your PC will feel like.
Questions
Can an RX 5700 XT run AI locally?
Yes. On a desktop with an AMD RX 5700 XT (8 GB) and 48 GB of RAM, Gemma 4 E4B wrote 63–72 tokens a second. You read about 5, so answers come out faster than you can read them.
Can an RX 5700 XT make AI pictures?
Very likely, but we haven't timed a picture on this card yet. When we do, the number goes here.
Is AI faster on the RX 5700 XT or on the processor?
On the card, by far: 63–72 tokens a second against 5 on the processor, about 14 times faster.
Can an RX 5700 XT run Gemma 4 12B?
Only for short chats. The bigger Gemma 4 12B (6.4 GB) wrote 20–36 tokens a second on a short question, but once the chat reached 8,192 tokens (a long conversation) it crashed, with the card almost full. That is why GreenCube only suggests the 12B on cards with 10 GB or more.
Your own AI, on your own PC
GreenCube runs on your computer: no usage limits, no subscription, works without internet. €9.99, paid once.
Get GreenCube