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RTX 2000 Ada vs Apple M1 for local AI

The RTX 2000 Ada fits more: up to gpt-oss 20B, against Qwen3 14B on the Apple M1. The RTX 2000 Ada generates about 3.3× as fast on any model both can run, because it has 224 GB/s of memory bandwidth to the Apple M1's 68 GB/s.

The numbers that matter

Memory and bandwidth

Memory decides which models fit. Bandwidth decides how fast they generate, because every token reads the model's active weights once.

RTX 2000 AdaApple M1
Memory16 GB VRAM8 or 16 GB unified memory
Usable for modelsAll 16 GBAbout 11 GB of 16 GB
Bandwidth224 GB/s68 GB/s
Biggest model that fitsgpt-oss 20BQwen3 14B
Best everyday pickgpt-oss 20B, ~39 tok/sQwen3 14B, ~4 tok/s
Model by model

Every model, side by side

Estimated generation speed at the largest memory size each was sold in (16 GB and 16 GB).

ModelRTX 2000 AdaApple M1
Qwen3 4B
About 4 GB
~49 tok/s Runs great~15 tok/s Usable
Qwen3 8B
About 7 GB
~24 tok/s Usable~7 tok/s Slow
Gemma 3 12B
About 10 GB
~15 tok/s Usable~5 tok/s Slow
Qwen3 14B
About 11 GB
~13 tok/s Usable~4 tok/s Slow
gpt-oss 20B
About 16 GB
~39 tok/s Runs greatNeeds 16 GB
Gemma 3 27B
About 19 GB
Needs 19 GBNeeds 19 GB
Qwen3 30B-A3B
About 21 GB
Needs 21 GBNeeds 21 GB
Qwen3 32B
About 22 GB
Needs 22 GBNeeds 22 GB
Llama 3.3 70B
About 45 GB
Needs 45 GBNeeds 45 GB
gpt-oss 120B
About 67 GB
Needs 67 GBNeeds 67 GB
Qwen3 235B-A22B
About 144 GB
Needs 144 GBNeeds 144 GB
DeepSeek R1 671B
About 406 GB
Needs 406 GBNeeds 406 GB
Which should you get?

The RTX 2000 Ada: 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.

How we estimate

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.

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