Can the Apple M5 Pro run gpt-oss-120b?

gpt-oss-120b · Apple M5 Pro · 8,192 ctx · whole model residentQ3_K_M sizereconstructed sizeSpeedestimate

IT FITS

Yes. 2 of 9 formats evaluated fit in 64 GB, the largest Apple M5 Pro configuration, at 8,192 tokens. No published file exists for this pair, so every size is rebuilt from the pinned architecture. The best quality that fits is Q3_K_M, needing 59.0 GB and running at an estimated 62 tokens per second.

Fitting in memory is not the same as loading. Whether the runtime and version you have supports this architecture and format on this machine has not been tested here.

Find the Apple M5 Pro: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

2/9formats that fit

64 GBdevice memory, largest configuration

59.0 GBneeded at best quality

~62tokens per second, estimatedFaster than you read

307GB/s bandwidth

Run it in the cloud

Run gpt-oss-120b properly, for cents an hour

The Apple M5 Pro only holds it at Q3_K_M, at an estimated ~62 tokens per second. A rented card runs a better format at full speed, billed by the second.

Prices · 18:30 UTC, 22 Sept

Cheapest96 GB

8× RTX 3060

$0.59per hour

Speed: ~551 tok/sFaster than you read
Per million tokens: $0.30

Vast.ai · Marketplace · 99.7% reliable

Rent on Vast.ai

Lowest price per hour

Recommended128 GB

4× RTX 5090

$1.07per hour

Speed: ~1371 tok/sFaster than you read
Per million tokens: $0.22

Vast.ai · Marketplace · 99.6% reliable

Rent on Vast.ai

Lowest cost per token at 20+ tok/s

Fastest270 GB

1× B300

$7.89per hour

Speed: ~1473 tok/sFaster than you read
Per million tokens: $1.49

RunPod · Secure Cloud

Rent on RunPod

Highest speed estimate

Compare all 18 rentable configurations →

No GPU at all

Use gpt-oss-120b by the token

Novita AI$0.25 / M output · $0.050 / M inputOpen on Novita AI
OpenRouter$0.60 / M output · $0.15 / M inputOpen on OpenRouter

Here the rented card is as cheap as the API or cheaper per token ($0.22 per million), and it keeps your data on a machine you control. The API is still the easier start: nothing to set up and nothing to switch off.

First time renting a GPU? How it works, in four steps

Create an account and add credit. Both providers are prepaid: $10 is enough to try any card on this page for hours. A new RunPod account that arrives through a referral link gets a one-time $5 credit when it first adds $10.
Pick the card and the count shown here, and a template: llama.cpp or vLLM for an OpenAI-compatible API, or one with a web chat if you only want to talk to the model. “How to launch it” below gives the exact image and command.
Wait for the download. The weights are fetched on the machine; tens of gigabytes take a few minutes on a data-centre connection. You pay by the second from the moment it starts.
Stop it when you are done. A running machine bills even when idle. A stopped one costs nothing per hour, though its disk is usually still billed until you delete it.

How to launch it

No Q4_K_M file of gpt-oss-120b is catalogued here, so there is no exact command to vouch for. Start the machine from the llama.cpp server image ghcr.io/ggml-org/llama.cpp:server-cuda and point -hf at a Q4_K_M build from the publisher or a quantizer you trust — search Hugging Face for one. Check its file size against the memory figure on this page before you rent.

Every machine holds the whole model at Q4_K_M and 8,192 tokens of context, no offload. Speeds are this site’s single-stream decode estimates; prices are what each provider’s own API quoted, on-demand, for the whole machine. Vast hosts below 98% measured reliability are left out.

Referral links Vast.ai, RunPod and Novita pay us a share of what you spend if you sign up through these buttons. It costs you nothing, and it never decides an order or a recommendation: both are computed from the live price and the speed, and options that pay us nothing are listed and recommended on the same terms. How we rank

Runs well on your Apple M5 Pro instead

The models nearest to gpt-oss-120b — same lab first, then closest in size — that the Apple M5 Pro holds whole at the standard format and runs at a usable speed. Nearest in size is not the same as equally good; compare them on the task you care about.

gpt-oss-safeguard-20bQ4_K_M · 13.8 GB · ~70 tok/s
gpt-oss-20bQ4_K_M · 13.8 GB · ~70 tok/s
Qwen3-Next-80B-A3B-InstructQ4_K_M · 50.0 GB · ~65 tok/s
Qwen3-Next-80B-A3B-ThinkingQ4_K_M · 50.0 GB · ~65 tok/s

How long a conversation. At Q3_K_M, this pair holds 131,072 tokens of context — roughly 98,304 words — needing 63.6 GB. That is OpenAI's own configured maximum of 131,072 tokens, not the card running out. A larger device does not extend it; a documented RoPE or YaRN extension might, and is a separate question from this one.

By memory configuration

The Apple M5 Pro is sold with 24, 48 or 64 GB of unified memory, and the amount decides the fit. Each row is the best format that fits in that much at 8,192 tokens, whole model resident.

Memory	Best format that fits	Needs	Calculator
24 GB	none fits	—	Open →
48 GB	Q2_K	43.7 GB	Open →
64 GB	Q3_K_M	59.0 GB	Open →

Every format evaluated

No published file exists for this pair, so every size is rebuilt from the pinned architecture. The Basis column says which is which for each row. Decode and first-token figures are calibrated estimates, not runs on this card.

Format	Needs	Of 64 GB	Fits	Decode	First token	Basis
FP16	234.7 GB	367%	short by 170.7 GB	does not run	—	reconstructed size
Q8_0	125.2 GB	196%	short by 61.2 GB	does not run	—	reconstructed size
Q6_K	97.0 GB	152%	short by 33.0 GB	does not run	—	reconstructed size
Q5_K_M	84.1 GB	131%	short by 20.1 GB	does not run	—	reconstructed size
Q5_0	81.5 GB	127%	short by 17.5 GB	does not run	—	reconstructed size
Q4_K_M	71.9 GB	112%	short by 7.94 GB	does not run	—	reconstructed size
Q4_0	67.0 GB	105%	short by 3.01 GB	does not run	—	reconstructed size
Q3_K_M	59.0 GB	92%	yes	~62 tok/s	no comparable peak	reconstructed size
Q2_K	43.7 GB	68%	yes	~70 tok/s	no comparable peak	reconstructed size

Sized at 8,192 tokens of context with the whole model resident — weights, the KV cache and the runtime reserve, offload off. Speed is only quoted for a format that fits: a rate for a configuration that cannot load is not a fact about anything.

Why the first-token figure is the same on every row

First token is modelled from the arithmetic the prompt requires, and that count does not change with the weight format — which is why it reads the same on every row. Real prefill does vary by format, because a quantized matmul is a different kernel; this model does not capture that, and the figure should be read as an order of magnitude rather than a ranking between formats.

What the labels mean

published data: Read from a published source — a file's byte count, a model's configuration or a manufacturer's specification — or exact arithmetic on such values. Not a measurement on a machine.
reconstructed size: Weight size reconstructed from the pinned architecture, because no published file exists.
size range: Only a lower and an upper bound are claimed for this weight size.
estimate: Calculated from sourced inputs by a stated method; an estimate, not a measurement.

Change anything

This page fixes the context at 8,192 tokens and one device. Batch, concurrent users, KV-cache format, clusters and rental cost are all in the calculator, already set to this pairing.

Open in the calculator →Everything the Apple M5 Pro runs Best models for 64 GB

Where these numbers come from

gpt-oss-120b’s architecture is read from its publisher’s own config.json at a pinned revision, and the Apple M5 Pro’s 64 GB and 307 GB/s come from the manufacturer’s specification. How far each figure can be trusted is published, per format and worst case included, on the accuracy scorecard.

Building this into your own product? A free API key returns exactly these figures, and the widget puts this answer on a product page with one script tag.

Use this answer in your own product

As JSON, from the API

curl -s https://llmbottleneck.com/v1/analyze \
  -H "Authorization: Bearer $LLMB_KEY" -H "content-type: application/json" \
  -d '{"model":"openai-gpt-oss-120b","quantization":"Q3_K_M","context":8192,"hardware":"apple-m5-pro"}'

Same engine, same evidence, every field sourced. Free key in one step →

On your page, as a widget

<script src="https://llmbottleneck.com/widget.js"
  data-model="openai-gpt-oss-120b" data-quantization="Q3_K_M"
  data-hardware="apple-m5-pro" data-context="8192"></script>

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