Apple M3 Pro vs Jetson AGX Orin for local AI
The Jetson AGX Orin fits more: up to Llama 3.3 70B, against Qwen3 32B on the Apple M3 Pro. The Jetson AGX Orin generates about 37% faster on any model both can run, because it has 205 GB/s of memory bandwidth to the Apple M3 Pro's 150 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.
| Apple M3 Pro | Jetson AGX Orin | |
|---|---|---|
| Memory | 18 or 36 GB unified memory | 32 or 64 GB unified memory |
| Usable for models | About 24 GB of 36 GB | About 48 GB of 64 GB |
| Bandwidth | 150 GB/s | 205 GB/s |
| Biggest model that fits | Qwen3 32B | Llama 3.3 70B |
| Best everyday pick | Qwen3 30B-A3B, ~26 tok/s | Qwen3 30B-A3B, ~36 tok/s |
Every model, side by side
Estimated generation speed at the largest memory size each was sold in (36 GB and 64 GB).
| Model | Apple M3 Pro | Jetson AGX Orin |
|---|---|---|
| Qwen3 4B About 4 GB | ~33 tok/s Runs great | ~45 tok/s Runs great |
| Qwen3 8B About 7 GB | ~16 tok/s Usable | ~22 tok/s Usable |
| Gemma 3 12B About 10 GB | ~10 tok/s Usable | ~14 tok/s Usable |
| Qwen3 14B About 11 GB | ~9 tok/s Slow | ~12 tok/s Usable |
| gpt-oss 20B About 16 GB | ~26 tok/s Usable | ~36 tok/s Runs great |
| Gemma 3 27B About 19 GB | ~5 tok/s Slow | ~7 tok/s Slow |
| Qwen3 30B-A3B About 21 GB | ~26 tok/s Usable | ~36 tok/s Runs great |
| Qwen3 32B About 22 GB | ~4 tok/s Slow | ~6 tok/s Slow |
| Llama 3.3 70B About 45 GB | Needs 45 GB | ~3 tok/s Slow |
| 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 Jetson AGX Orin: it fits bigger models and runs faster. 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.