RTX 5060 vs Arc A750 for local AI
Both fit the same models we track, up to Qwen3 8B. The Arc A750 generates about 14% faster on any model both can run, because it has 512 GB/s of memory bandwidth to the RTX 5060's 448 GB/s.
Memory and bandwidth
Memory decides which models fit. Bandwidth decides how fast they generate, because every token reads the model's active weights once.
Every model, side by side
Estimated generation speed at the largest memory size each was sold in (8 GB and 8 GB).
| Model | RTX 5060 | Arc A750 |
|---|---|---|
| Qwen3 4B About 4 GB | ~99 tok/s Runs great | ~110 tok/s Runs great |
| Qwen3 8B About 7 GB | ~47 tok/s Runs great | ~54 tok/s Runs great |
| Gemma 3 12B About 10 GB | Needs 10 GB | Needs 10 GB |
| Qwen3 14B About 11 GB | Needs 11 GB | Needs 11 GB |
| gpt-oss 20B About 16 GB | Needs 16 GB | Needs 16 GB |
| Gemma 3 27B About 19 GB | Needs 19 GB | Needs 19 GB |
| Qwen3 30B-A3B About 21 GB | Needs 21 GB | Needs 21 GB |
| Qwen3 32B About 22 GB | Needs 22 GB | Needs 22 GB |
| Llama 3.3 70B About 45 GB | Needs 45 GB | Needs 45 GB |
| gpt-oss 120B About 67 GB | Needs 67 GB | Needs 67 GB |
| Qwen3 235B-A22B About 144 GB | Needs 144 GB | Needs 144 GB |
| DeepSeek R1 671B About 406 GB | Needs 406 GB | Needs 406 GB |
The Arc A750, for speed. They fit the same models. Price matters as much as either number: check used prices on the builds page, where every parts list has a measured or estimated speed.
Both columns use the same formula: memory bandwidth divided by the weights read per token, calibrated on our own rack, with Ollama's default 4-bit sizes. Real results are usually within about a third. Measured either one? Send us the number.