Can the Apple M6 run gemma-4-12B-it?

gemma-4-12B-it · Apple M6 · 8,192 ctx · whole model residentFP16 sizereconstructed sizeSpeedestimate

IT FITS

Yes. 9 of 9 formats evaluated fit in 32 GB, the largest Apple M6 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 FP16, needing 27.2 GB and running at an estimated 6 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 M6: 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

27.2 GBneeded at best quality

~6.3tokens per second, estimatedSlow

170GB/s bandwidth

Run it in the cloud

Run gemma-4-12B-it properly, for cents an hour

The Apple M6 only holds it at FP16, at an estimated ~6.3 tokens per second. A rented card runs a better format at full speed, billed by the second.

Prices · 18:30 UTC, 22 Sept

Recommended · Cheapest12 GB

1× RTX 3060

$0.036per hour

Speed: ~29 tok/sAbout reading pace
Per million tokens: $0.33

Vast.ai · Marketplace · 99.8% reliable

Rent on Vast.ai

Lowest cost per token at 20+ tok/s

2.1× faster10 GB

1× RTX 3080

$0.082per hour

Speed: ~63 tok/sFaster than you read
Per million tokens: $0.36

Vast.ai · Marketplace · 98.3% reliable

Rent on Vast.ai

Cheapest at 1.6× the speed or more

Fastest270 GB

1× B300

$7.89per hour

Speed: ~640 tok/sFaster than you read
Per million tokens: $3.42

RunPod · Secure Cloud

Rent on RunPod

Highest speed estimate

Compare all 28 rentable configurations →

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 gemma-4-12B-it 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 M6 instead

The models nearest to gemma-4-12B-it — same lab first, then closest in size — that the Apple M6 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.

gemma-3-12b-itQ4_K_M · 8.68 GB · ~19 tok/s
gemma-2-9b-itQ4_K_M · 8.49 GB · ~20 tok/s
gemma-4-E4B-itQ4_K_M · 5.86 GB · ~41 tok/s
gemma-4-E2B-itQ4_K_M · 4.02 GB · ~78 tok/s

How long a conversation. At FP16, this pair holds 81,007 tokens of context — roughly 60,755 words — needing 32.0 GB. Past that the Apple M6 runs out of memory, not the model out of context, which would allow 262,144. 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 M6 is sold with 16, 24 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	15.3 GB	Open →
24 GB	Q8_0	15.3 GB	Open →
32 GB	FP16	27.2 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 32 GB	Fits	Decode	First token	Basis
FP16	27.2 GB	85%	yes	~6.3 tok/sSlow	no comparable peak	reconstructed size
Q8_0	15.3 GB	48%	yes	~11 tok/sAbout reading pace	no comparable peak	reconstructed size
Q6_K	12.2 GB	38%	yes	~14 tok/sAbout reading pace	no comparable peak	reconstructed size
Q5_K_M	10.8 GB	34%	yes	~16 tok/sAbout reading pace	no comparable peak	reconstructed size
Q5_0	10.6 GB	33%	yes	~16 tok/sAbout reading pace	no comparable peak	reconstructed size
Q4_K_M	9.54 GB	30%	yes	~18 tok/sAbout reading pace	no comparable peak	reconstructed size
Q4_0	9.13 GB	29%	yes	~18 tok/sAbout reading pace	no comparable peak	reconstructed size
Q3_K_M	8.19 GB	26%	yes	~20 tok/sAbout reading pace	no comparable peak	reconstructed size
Q2_K	6.70 GB	21%	yes	~25 tok/sAbout reading pace	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 M6 runs Best models for 32 GB

Where these numbers come from

gemma-4-12B-it’s architecture is read from its publisher’s own config.json at a pinned revision, and the Apple M6’s 32 GB and 170 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":"google-gemma-4-12b-it","quantization":"FP16","context":8192,"hardware":"apple-m6"}'

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="google-gemma-4-12b-it" data-quantization="FP16"
  data-hardware="apple-m6" data-context="8192"></script>

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