Oct 2, 2026

Mac Studio M5 Max vs M5 Ultra for Local AI in 2026: 128GB at 614 GB/s or 96GB at 1.2 TB/s for the Same $5K

Q: Can the 96GB M5 Ultra run gpt-oss 120B?

Yes, after raising the GPU wired-memory limit with `sudo sysctl iogpu.wired_limit_mb=81920`. The model is ~68.5GB loaded; the default ~72GB GPU budget is too tight once KV cache lands on top, the 80GB limit is comfortable.

By RunAIHome Team · 12 min read

mac-studiom5-maxm5-ultraapple-siliconlocal-llmhardware

TL;DR: At the $5K mark, Apple makes you choose: the Mac Studio M5 Max 128GB ($5,099) holds more model, the M5 Ultra 96GB ($5,499) runs what fits roughly twice as fast. Buy the Ultra if your biggest model stays under ~80GB; buy the Max if capacity is the point and you’ll tolerate half the decode speed.

	M5 Max 128GB	M5 Ultra 96GB	M5 Ultra 256GB
Best for	Biggest models per dollar	Speed on 70B–120B class	235B-class MoE, multi-model serving
Price	$5,099 ($5,399 w/ 1TB)	$5,499	$10,799
Bandwidth	614 GB/s	1.2 TB/s	1.2 TB/s
The catch	~Half the decode speed	32GB less room, wired-limit squeeze on 120B	The $5,300 step up buys capacity, not more speed

Honest take: For most readers the M5 Ultra 96GB is the better $5K Mac — gpt-oss 120B fits (with one sysctl command), 70B Q4 fits easily, and everything you run decodes nearly twice as fast. Pay for the Max’s 128GB only if you specifically need the 96–128GB window.

Apple’s own configurator creates this collision. Spec a Mac Studio M5 Max up to 128GB and you land at $5,099 — $400 below the M5 Ultra’s $5,499 base. Same aluminum box, same ports, opposite philosophies: one buys 32GB of extra unified memory, the other buys 586 GB/s of extra bandwidth and twice the GPU cores. The site has compared each machine against the DGX Spark, a Strix Halo mini-PC, the RTX PRO 6000, and the used M3 Ultra market — but never against each other, and the head-to-head is the decision most Mac buyers actually face. Before reading on, it’s worth 30 seconds in our VRAM calculator to know how big your target model really is, because that one number decides this.

What Apple actually sells (October 2026)

The ladder matters because the interesting configs are not the base models (Apple Newsroom, August 25, 2026; configurator pricing checked October 2, 2026):

Config	Memory	Price	Bandwidth
M5 Max 18c CPU / 32c GPU	36GB / 512GB	$2,499	614 GB/s
M5 Max 18c CPU / 40c GPU	48GB / 512GB	$3,099	614 GB/s
M5 Max 40c GPU + memory step	128GB / 512GB	$5,099	614 GB/s
M5 Ultra 30c CPU / 64c GPU	96GB / 1TB	$5,499	1.2 TB/s
M5 Ultra 36c CPU / 80c GPU	256GB / 1TB	$10,799	1.2 TB/s
M5 Ultra 80c GPU	512GB	orders open late October	1.2 TB/s

Three traps hide in that table. First, the $2,499 headline Studio has 36GB — fine for 27B-class models, not what anyone cross-shopping an Ultra wants. Second, the 128GB Max requires the 40-core GPU tier plus Apple’s $2,000 memory step; there is no cheap path to 128GB. Third, the Ultra’s 256GB tier forces the $1,300 chip upgrade and a $4,000 memory step — the jump from $5,499 to $10,799 nearly doubles the price of the machine without adding a single token per second of decode speed, because every M5 Ultra ships the same 1.2 TB/s.

Note the SSD asymmetry too: the $5,099 Max has a 512GB drive ($5,399 with 1TB), while the Ultra’s $5,499 includes 1TB. Configured drive-for-drive, the real gap is $100, not $400. A 70B Q4 GGUF alone is 42.5GB; a 512GB drive holding a model library is cramped, so treat $5,399 vs $5,499 as the honest comparison.

Decode speed: bandwidth is the whole story

Decoding is memory-bandwidth-bound: every generated token reads the active weights from unified memory, so the hard ceiling is bandwidth divided by bytes read per token. The M5 Ultra’s 1.2 TB/s against the Max’s 614 GB/s predicts a ~1.95× gap, and measured numbers land there:

Model	M5 Max (614 GB/s)	M5 Ultra (1.2 TB/s)
Llama 3.3 70B Q4_K_M (42.5GB)	12–14 tok/s	23–27 tok/s
gpt-oss 120B MXFP4 (MoE, ~3GB/token)	65–88 tok/s (measured)	~95–115 tok/s (estimated)
Physics ceiling, 70B Q4	14.4 tok/s	28.2 tok/s

The 70B numbers are measured ranges consistent across The Byte Lab’s M5 Max testing and the llama.cpp gpt-oss benchmark thread, and both sit at 83–96% of the bandwidth ceiling — exactly where healthy Apple Silicon results land. The Ultra’s 120B figure is a bandwidth-scaled estimate (we flagged it the same way in the RTX PRO 6000 comparison): no clean public llama-bench run existed as of early October, but owner reports of 60–100 tok/s on 120B-class models bracket it and the ~400 tok/s ceiling leaves plenty of headroom.

What does 12–14 vs 23–27 tok/s mean in practice? Reading speed is roughly 7–10 tok/s. The Max runs a 70B at the edge of comfortable; the Ultra runs it fast enough that you stop thinking about it, and fast enough to burn through an agentic tool-use loop where the model generates thousands of tokens you never read. For coding agents, the 2× is the difference between usable and annoying.

Prefill (prompt processing) follows GPU compute rather than bandwidth, and the Ultra is two Max dies fused — twice the GPU cores. The M5 Max processes gpt-oss 120B prompts at roughly 575–650 tok/s in llama.cpp (the number we verified in the DGX Spark comparison); expect the Ultra to roughly double that, while still trailing CUDA machines by a wide margin. One Mac-wide caveat applies to both: MLX benchmarks show prefill degrading with context — roughly 345 tok/s at 1K context down to ~154 tok/s at 128K on gpt-oss 120B — and mlx-lm has an open issue where long-context 120B prefill drops to about a seventh of normal speed. If your workload is pasting whole repositories into context, neither machine fixes that; the money question is only how fast tokens come out afterward.

What fits: the 96GB vs 128GB window

The entire case for the Max is the 32GB window between the two machines. Here’s what actually lives there.

Fits in 96GB (both machines): 70B dense at Q4–Q6 (42.5–58GB), gpt-oss 120B MXFP4 (~65GB native, 68.5GB with full context in llama.cpp — with the wired-limit fix below), Qwen3.8-Flash-Next 125B/6B-active at 4-bit, every 27B–35B model at any practical quant. This covers the overwhelming majority of what home labs run in late 2026 — see the 128GB unified-memory model guide for the full menu.

Needs the 128GB Max: gpt-oss 120B at Q8 with very large context, 70B dense at Q8 (~75GB) plus a second resident model, GLM-class ~106B dense at Q6, or simply holding two mid-size models loaded simultaneously so Ollama isn’t swapping them on every request. Real, but narrow.

Needs the 256GB Ultra: Qwen-class 235B MoE at 4-bit (~130GB), 120B at 8-bit with giant context, serious multi-model serving. If this is you, the $10,799 config is the actual product — and at that point also read the 100B-models-on-Mac guide before ordering.

The honest framing: the Max’s extra 32GB mostly buys comfort at the 120B tier, while the Ultra’s bandwidth buys speed at every tier. Capacity you occasionally need loses to speed you use on every single token.

The 96GB trap and the one-command fix

The sharpest practical edge in this comparison: gpt-oss 120B fits the 96GB Ultra on paper and then refuses to load. macOS caps GPU-wired memory at roughly 75% of unified memory by default, so a 96GB Studio offers about 72GB to the GPU — and a 68.5GB model plus KV cache blows past it. Ollama falls back or errors with a message like:

Error: model requires more system memory (70.1 GiB) than is
available (66.4 GiB)

The fix is one command:

sudo sysctl iogpu.wired_limit_mb=81920
# expected output:
# iogpu.wired_limit_mb: 0 -> 81920

That raises the GPU ceiling to 80GB, leaving macOS 16GB — stable in daily use. The setting reverts on reboot, so put it in a LaunchDaemon if the Studio serves models 24/7. On the 128GB Max the default limit is ~96GB, so the same model loads without touching anything — that convenience is genuinely part of what the Max’s capacity buys, just not $400-plus-half-your-speed worth of it.

Power, noise, and the rest of the box

Apple rates the M5 Max Studio at 200W maximum continuous draw and the M5 Ultra at 385W (Apple’s power and thermal specs), with single-digit idle wattage on both. Either is apartment-friendly in a way no multi-GPU tower is: a used RTX 3090 alone pulls 350W under load, and the dual-3090 box from our $3K/$6K/$10K build guide idles higher than a Studio runs. At $0.12/kWh, even four hours of daily full-tilt inference costs the Ultra about $67/year — electricity is a rounding error here; buy on speed and capacity.

Everything else is identical between the two: same chassis, same port layout, same silent-under-load thermals (this chip throttles in a MacBook Pro chassis, not in a Studio), same macOS constraint that there’s no CUDA — your stack is llama.cpp, Ollama, LM Studio, or MLX, which in 2026 covers nearly everything except serious fine-tuning. If training is the goal, stop reading and rent an NVIDIA box instead.

What to actually buy

Prices as of October 2026, all taken from the comparison above:

Your situation	The machine	Price	Where
Your biggest model is ≤80GB and you want it fast — the default pick	Mac Studio M5 Ultra 96GB	$5,499	Check price
You specifically need 96–128GB resident (two models, Q8 120B)	Mac Studio M5 Max 128GB	$5,099–$5,399	Check price
235B-class MoE or multi-user serving is the actual job	Mac Studio M5 Ultra 256GB	$10,799	Check price
Everything you run fits in 24GB	Used RTX 3090	$1,150–$1,350	Check price
Undecided — test your workload before spending $5K	Rented GPU	from $0.07/hr (3090)	Vast.ai

Two sanity checks before clicking. If your models all fit in 24GB, both Studios are the wrong purchase — a used 3090 decodes 7B–32B models faster than either Mac at a quarter of the price. And if you’re torn because you might someday need 256GB, rent first: an hour on a big cloud box running your actual workload beats speculating with $10,799. For wiring whichever machine you buy into Cursor or Cline as a local coding backend, our sister site covers the agent side, and aifoss.dev’s Ollama review covers the serving stack.

Recommended Gear

Mac Studio M5 Ultra 96GB — $5,499; 1.2 TB/s, 23–27 tok/s on 70B Q4, the speed pick
Mac Studio M5 Max 128GB — $5,099–$5,399; 614 GB/s, the capacity-per-dollar pick
Used RTX 3090 24GB — $1,150–$1,350; the right answer if nothing you run exceeds 24GB

FAQ

Is the M5 Ultra really twice as fast as the M5 Max for LLMs? On decode, nearly: 1.2 TB/s vs 614 GB/s predicts 1.95×, and measured 70B results (23–27 vs 12–14 tok/s) match. On prefill the Ultra’s doubled GPU cores help similarly. On anything bandwidth-light (app responsiveness, small-model latency) you won’t feel the difference.

Can the 96GB M5 Ultra run gpt-oss 120B? Yes, after raising the GPU wired-memory limit with sudo sysctl iogpu.wired_limit_mb=81920. The model is ~68.5GB loaded; the default ~72GB GPU budget is too tight once KV cache lands on top, the 80GB limit is comfortable.

Why not the $2,499 base M5 Max? 36GB of unified memory caps you at roughly 27B-class models — at that size a used RTX 3090 is faster and $1,200 cheaper. The base Studio is a fine desktop; it’s not a serious local-AI machine.

Is the 256GB M5 Ultra worth $10,799? Only if you’ll actually load more than 96GB at once — 235B-class MoE models or several resident models. It decodes nothing faster than the $5,499 config; the extra $5,300 is pure capacity.

Should I wait for the 512GB M5 Ultra? Orders open in late October 2026 with pricing unannounced. If your target models fit in 96–256GB, waiting buys you nothing — every M5 Ultra has the same 1.2 TB/s. It only matters for 400GB+ frontier quants like DeepSeek-class 671B.

Sources

Last updated October 2, 2026. Prices and specs change; verify current rates before purchasing.

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