Can the Apple M1 Pro run granite-4.1-8b?

granite-4.1-8b · Apple M1 Pro · 8,192 ctx · whole model residentFP16 sizepublished dataSpeedestimate

IT FITS

Yes. 9 of 9 formats evaluated fit in 32 GB, the largest Apple M1 Pro configuration, at 8,192 tokens. Every row is sized from a published file. The best quality that fits is FP16, needing 19.7 GB and running at an estimated 10 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 M1 Pro: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

9/9formats that fit

32 GBdevice memory, largest configuration

19.7 GBneeded at best quality

~9.8tokens per second, estimatedSlow

200GB/s bandwidth

How long a conversation. At FP16, this pair holds 83,084 tokens of context — roughly 62,313 words — needing 32.0 GB. Past that the Apple M1 Pro runs out of memory, not the model out of context, which would allow 131,072. This is the wall a long chat hits after it has already loaded fine. The nearest round setting below it is 65,536.

By memory configuration

The Apple M1 Pro is sold with 16 or 32 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
16 GB	Q8_0	11.5 GB	Open →
32 GB	FP16	19.7 GB	Open →

Every format evaluated

Every row is sized from a published file. 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 32 GB	Fits	Decode	First token	Basis
FP16	19.7 GB	62%	yes	~9.8 tok/sSlow	no comparable peak	published data
Q8_0	11.5 GB	36%	yes	~17 tok/sAbout reading pace	no comparable peak	published data
Q6_K	9.36 GB	29%	yes	~20 tok/sAbout reading pace	no comparable peak	published data
Q5_K_M	8.40 GB	26%	yes	~22 tok/sAbout reading pace	no comparable peak	published data
Q5_0	8.25 GB	26%	yes	~23 tok/sAbout reading pace	no comparable peak	published data
Q4_K_M	7.49 GB	23%	yes	~25 tok/sAbout reading pace	no comparable peak	published data
Q4_0	7.20 GB	22%	yes	~26 tok/sAbout reading pace	no comparable peak	published data
Q3_K_M	6.49 GB	20%	yes	~28 tok/sAbout reading pace	no comparable peak	published data
Q2_K	5.55 GB	17%	yes	~33 tok/s	no comparable peak	published data

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 M1 Pro runs Best models for 32 GB

Where these numbers come from

granite-4.1-8b’s architecture is read from its publisher’s own config.json at a pinned revision, and the Apple M1 Pro’s 32 GB and 200 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":"ibm-granite-granite-4-1-8b","quantization":"FP16","context":8192,"hardware":"apple-m1-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="ibm-granite-granite-4-1-8b" data-quantization="FP16"
  data-hardware="apple-m1-pro" data-context="8192"></script>

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