Mac Studio and RTX cards: reading published LLM tests
A fast answer and a model that fits are two different buying requirements. The published comparisons between Mac Studio and NVIDIA workstations make that distinction unusually clear.
Research checked 31 October 2026. Compiled from published tests. No hardware benchmarks were run for this article; measurements belong to the credited testers.
Federico Viticci’s MacStories review compares a 256GB M5 Ultra Mac Studio with his RTX 5090 PC. With Qwen3.8-27B and a roughly 6,000-token prompt, he reports prompt-processing speeds of about 1,700 tokens per second on the Mac and 3,000 on the PC. That is a relevant result for someone who repeatedly feeds a coding assistant sizeable instructions. It is also a comparison between complete software setups: oMLX and MLX model builds on macOS, versus LM Studio and a Q4_K_M GGUF on Windows. It does not isolate the chips from their runtimes or quantization choices. Federico Viticci, MacStories.
Memory changes the next question. Viticci also ran a Flash-Next configuration with a sampled process footprint around 155GB on the Mac. A single 32GB RTX 5090 cannot keep that workload entirely in its own VRAM. Comparing the speed of a smaller model on the PC with that larger model on the Mac would answer a different question: what each system can practically offer, rather than which runs the same workload faster. Viticci’s model and memory tests.
Where the RTX PRO 6000 fits
Jerry Zhou and Richard Chen’s LMSYS review provides a separate, older comparison. Their October 13, 2025 test used GPT-OSS 20B in MXFP4 through Ollama at batch size one.
| Hardware | Prompt processing, tokens/s | Generation, tokens/s |
|---|---|---|
| RTX PRO 6000 Blackwell | 10,108 | 215 |
| RTX 5090 | 8,519 | 205 |
| DGX Spark, original 128GB system | 2,053 | 49.7 |
Compiled from the figures printed in the LMSYS review. The authors received NVIDIA early access. These are historical software results, not an October 2026 leaderboard; the article itself warns that software updates can change performance.
On that particular small-model test, the professional card’s generation advantage over the 5090 was modest. That does not establish their relationship on larger models, higher concurrency, or fine-tuning. It certainly cannot place the M5 Ultra in the same table: LMSYS tested older Apple systems, not that machine.
Read the comparison around your workload
For a model that fits comfortably on a 5090, the MacStories result gives a concrete reason to consider the PC’s prompt-processing speed. For a much larger model, the Mac’s memory capacity becomes a separate capability. The professional NVIDIA card needs justification from workloads that use its additional capacity or serving features; a fast small-model score alone does not establish value.
Those results support narrower conclusions. A single ranking of all three would require matched models, quantizations, context lengths and serving conditions that these sources do not provide.
Read the series
- Mac Studio and RTX cards: reading published LLM tests (this article)
- DGX Spark 64GB: what published tests can tell us
- AMD local LLM tests: the configuration behind the score
- Why local LLM benchmark charts disagree
- Local AI cost calculations: power, usage and payback
For memory planning, use the model-fit guide. Browse all Local AI notes.
