Yes, the M5 Ultra can rival Nvidia systems on some local-AI workloads, but the available benchmarks do not show that it is a universal match. Independent testing found much faster prompt processing than on the M3 Ultra, while Nvidia comparisons vary by model, runtime and hardware configuration. One RTX 5090 test generated a particular model faster; a separate comparison found two linked DGX Sparks much quicker at processing a long prompt.
What the M5 Ultra numbers actually establish
Apple announced the M5 Ultra Mac Studio on August 25, 2026. The configuration reaches an 80-core GPU, 512GB of unified memory and 1.2TB/s of memory bandwidth. Apple claims up to 4.3 times the peak AI compute performance of M3 Ultra, and up to four times faster LLM prompt processing than M3 Ultra in LM Studio. These are Apple’s own generational claims, not results from a direct comparison with Nvidia.
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Apple’s stated prompt-processing comparison used an 8K prompt, a 14-billion-parameter model quantized to 4-bit, and LM Studio v0.4.19+2. Its separate claim of up to 9.8 times faster prompt processing compares with M1 Ultra. Those ratios describe prompt processing under the specified test, not a promise that a complete conversation or every model runs that much faster.
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A theNetworkChuck benchmark compared M5 Ultra and M3 Ultra using MLX 0.32.2 and mlx-lm 0.31.3. The controlled chat-model tests ran on September 20, 2026. They covered five models at prompt lengths of 512, 4K, 16K and 32K; the 122-billion-parameter model also had a 100K-prompt test. Most cells used one discarded warm-up and five measured runs, with output capped at 128 tokens.
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The M5 system had 256GB of memory; the M3 system had 512GB. The reviewer also disclosed that Apple loaned the M5 Ultra used in the tests. The systems were on different macOS versions—27.0 and 26.5.1 respectively—and an indexing job occupied one M3 CPU core during the benchmark. The reviewer said this could only have made the M3 appear slower, but did not rerun the tests to measure its effect. Treat the results as useful measurements of these test systems, not a perfectly isolated silicon comparison.
Measured results at a 32K prompt
| Model | Prompt reading: M5 Ultra | Prompt reading: M3 Ultra | Generation: M5 Ultra | Generation: M3 Ultra |
|---|---|---|---|---|
| Qwen3 14B | 2,278 tokens/s | 551 tokens/s | 55.4 tokens/s | 31.5 tokens/s |
| Qwen3.8 27B | 1,526 tokens/s | 388 tokens/s | 42.6 tokens/s | 27.8 tokens/s |
| Gemma 4 31B | 1,217 tokens/s | 294 tokens/s | 13.6 tokens/s | 9.0 tokens/s |
| Qwen3.5 122B-A10B | 2,300 tokens/s | 771 tokens/s | 65.3 tokens/s | 42.3 tokens/s |
These are the review’s reported results for its named models and settings, not expected speeds for every app or model. Across the tested dense models, prompt reading improved by about 3.9–4.1 times at 32K; across the tested mixture-of-experts models, it improved by about 2.7–3.0 times. Output generation improved by about 1.5–1.8 times. The much larger prompt-reading gains are the key distinction: prefill processes the input context, while decode generates the answer. A long system prompt or a large set of tool results can make prefill important, but the prompt-processing ratio does not mean the whole interaction is equally faster.
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What the Nvidia comparisons show—and what they do not
RTX 5090 versus M5 Ultra
A September 2026 analysis by Samir Sengupta of MacStories’ M5 Ultra review reports that an RTX 5090 generated dense Qwen3.8 27B faster than the M5 Ultra at every tested context length. At 8K, the reported rates were 59 tokens/s on the RTX 5090 and 48 tokens/s on the M5 Ultra; at 128K, they were 44 and 32 tokens/s respectively. This is a counterexample to a blanket claim that the M5 Ultra beats Nvidia. It is still one model and software setup: the analysis does not provide matched Nvidia prompt-processing or batched-serving results.
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M5 Ultra versus two DGX Sparks
A separate MindStudio comparison published October 2, 2026 tested a 256GB M5 Ultra against two 128GB DGX Sparks linked together. On selected large models, it reports generation rates of roughly 34–38 tokens/s for the tested setups. For a 32K prompt, the Sparks took about 17 seconds to process it, compared with about 44–50 seconds on the Mac. In a DeepSeek test, changing the Mac backend from llama.cpp to MLX raised reported output from about 40 to 53 tokens/s.
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That comparison is evidence about those particular systems and software choices, not a universal ranking. It puts one Mac against two Nvidia systems, and its results illustrate how strongly runtime can affect the measured rate. MindStudio’s useful framing is that which system is “better” depends on whether a workload is read-heavy or write-heavy; that is the publisher’s characterization, not a general benchmark rule.
Why other Nvidia benchmark collections do not settle this matchup
Nvidia’s MLPerf results cover submitted data-center training and inference systems, not a directly matched Mac Studio M5 Ultra. They cannot by themselves establish which workstation is faster. Similarly, a 2025 preprint comparing MLX, MLC-LLM, Ollama, llama.cpp and PyTorch MPS on an M2 Ultra is background on Apple Silicon runtimes, not evidence of M5 Ultra performance against Nvidia.
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Memory capacity is a separate question from speed
Unified memory affects which model weights, context and working data can fit; it does not by itself establish inference speed. Apple’s announced M5 Ultra options reach 512GB, but the independent M5-versus-M3 benchmark used a 256GB M5 system. The benchmark author estimates that a 600-billion-parameter model at roughly four-bit quantization would require about 300GB for weights alone, before allowing memory for context. That example would not fit on the 256GB test unit.
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How to judge a local-AI comparison for your workload
Before treating a headline speed as relevant to your own use, compare the conditions that shape both fit and performance:
- Model and architecture: Match the exact model and distinguish dense models from mixture-of-experts models.
- Quantization and memory: Check the quantized model’s memory needs, then leave room for context and cache. Do not equate maximum system memory with a throughput result.
- Prompt length and prefill: Look for prompt-processing speed at a context length like the one you actually use.
- Generation rate: Compare decode speed separately from prompt processing; one cannot stand in for the other.
- Runtime and backend: Record whether the result used MLX, llama.cpp or another backend. The MindStudio DeepSeek result changed substantially when the Mac backend changed.
- Concurrency: Check the number of simultaneous streams and whether the test measures one user or batched serving. The cited RTX 5090 analysis does not provide matched batched-serving numbers.
- Hardware class: Keep a single RTX 5090, two linked DGX Sparks and Nvidia data-center accelerators distinct. They are not interchangeable comparison points.
- System configuration: Note the memory tier and other test conditions. The M5/M3 review’s different memory capacities, operating-system versions and M3 indexing load limit how cleanly its results isolate the chips.
Verdict by workload
The evidence makes the M5 Ultra a credible local-AI workstation, especially when its memory capacity and strong prompt processing suit the workload. It is not enough to declare it a universal Nvidia rival: the RTX 5090 led on generation in one reported Qwen3.8 27B comparison, while the dual-DGX Spark test showed a prompt-processing advantage in a different setup. There is no single cited test that matches the M5 Ultra against all Nvidia hardware classes across model fit, prompt processing, generation and multiple simultaneous users.
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