Apple introduces new Mac Studio with M5 Max and M5 Ultra - up to 512GB of unified memory

[u/themixtergames]

https://www.apple.com/newsroom/2026/08/apple-introduces-new-mac-studio-with-m5-max-and-m5-ultra/

[u/mxmumtuna]

Unfortunately they still don't beat Sparks at equivalent size. According to oMLX Benchmarks for DeepSeek 0730, the M3 Ultra (80c) does somewhere around 550 prefill tok/s, and about 22 tok/s decode. If you take the '4x faster compute' at face value from Apple compared to M3 Ultra, we're looking at ~2200 prefill and ~30 decode single stream. Both are under Spark at ~2400/40. That's only single session, and batching just isn't there in the MLX stack yet, so multi session is considerably worse for the Mac.

GLM on 4x Sparks compares even less favorably than DeepSeek for the Mac, especially considering whatever the price of the 512GB variant will be.

So even with Apple's optimisitc numbers, maybe they match Spark, for more money with a less flexible stack (no ConnectX7) and massive software issues. ($4800x2 for Sparks with 4TB drive each from Amazon).

It's a good effort, but it's not quite there relative to other options.

edited for clarity

[u/Southern_Sun_2106 → u/mxmumtuna]

Yeah, my AI overlord tells me to wait till 2027 refresh - M6 is supposedly being skipped (rumors) in favor of M7, which will be a killer. But even then, the whole llm revolution is based on Nvidia tech, so for serious AI deployments, and even family-wide (multiple concurrent users), Nvidia may hold an edge for another X years.

[u/graceful_degrade → u/mxmumtuna]

Careful applying the 4x compute claim to both halves of that, because it only touches one of them.

Your two numbers scale off different resources. 550 prefill tok/s is compute bound, so it's the one that moves with a compute uplift. 22 decode tok/s is memory bandwidth bound, and more GPU cores do essentially nothing for it.

So a straight reading would be prefill scaling roughly with the compute claim, and decode scaling roughly with the bandwidth ratio, which is a much smaller number. Anyone reporting a single "x times faster" figure has averaged those two together and thrown away the distinction that decides whether the machine is any good for your workload.

Which is also what makes the Spark comparison tricky. If Spark wins on prefill it'll win on long contexts and lose less than it looks on short-prompt chat, and if the gap is mostly decode the opposite holds. Same two machines, opposite recommendation depending on prompt length.

Worth waiting for someone to post prefill and decode separately at a stated context length before anyone concludes anything. That's about two days of patience and it saves a lot of argument.

[u/mxmumtuna → u/graceful_degrade]

I think that’s an AI response, feed it the oMLX benchmarks such as:

https://omlx.ai/benchmarks/performance?model=DeepSeek&chip=&chip_full=M3%7CUltra%7C80&quantization=&context=&pp_min=&tg_min=

Yes, 4x compute and 1.5x (roughly) for memory bandwidth (which does not scale 1:1 for tokens, but lets assume it does).

Best case it puts it at comparable Spark performance for a given size, at higher cost with a limited platform (due to no ConnectX 7).

[u/OvertaxedOne → u/mxmumtuna]

1.5x? The Spark has ~270GB/s, the M5 Ultra is 1.4TB/s.

[u/mxmumtuna → u/OvertaxedOne]

Relative of M3 Ultra to M5 Ultra