Can the Apple M3 Max run NVIDIA-Nemotron-3-Super-120B-A12B-BF16?

NVIDIA-Nemotron-3-Super-120B-A12B-BF16 · Apple M3 Max · 8,192 ctx · whole model residentQ6_K sizesize rangeSpeedestimate

IT FITS

Yes. 7 of 9 formats evaluated fit in 128 GB, the largest Apple M3 Max 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 Q6_K, needing 102.6 GB and running at an estimated 4 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 M3 Max: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

7/9formats that fit

128 GBdevice memory, largest configuration

102.6 GBneeded at best quality

~3.6tokens per second, estimatedSlow

400GB/s bandwidth

Run it in the cloud

Run NVIDIA-Nemotron-3-Super-120B-A12B-BF16 properly, for cents an hour

The Apple M3 Max only holds it at Q6_K, at an estimated ~3.6 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: not calibrated
Per million tokens: —

Vast.ai · Marketplace · 99.7% reliable

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Lowest price per hour

Recommended80 GB

1× H100 SXM

$1.74per hour

Speed: ~29 tok/sAbout reading pace
Per million tokens: $16.14

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Rent on Vast.ai

or RunPod $3.49/h

2.3× faster270 GB

1× B300

$7.89per hour

Speed: ~68 tok/sFaster than you read
Per million tokens: $31.90

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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.
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How to launch it

No Q4_K_M file of NVIDIA-Nemotron-3-Super-120B-A12B-BF16 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.

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Runs well on your Apple M3 Max instead

The models nearest to NVIDIA-Nemotron-3-Super-120B-A12B-BF16 — same lab first, then closest in size — that the Apple M3 Max 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.

NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16Q4_K_M · 20.3 GB · ~17 tok/s
Nemotron-Cascade-2-30B-A3BQ4_K_M · 20.3 GB · ~17 tok/s
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16Q4_K_M · 20.3 GB · ~17 tok/s
NVIDIA-Nemotron-Nano-9B-v2Q4_K_M · 6.54 GB · ~48 tok/s

How long a conversation. At Q6_K, this pair holds 262,144 tokens of context — roughly 196,608 words — needing 104.6 GB. That is NVIDIA's own configured maximum of 262,144 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 M3 Max is sold with 36, 48, 64, 96 or 128 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
36 GB	none fits	—	Open →
48 GB	none fits	—	Open →
64 GB	Q3_K_M	62.9 GB	Open →
96 GB	Q5_K_M	89.3 GB	Open →
128 GB	Q6_K	102.6 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 128 GB	Fits	Decode	First token	Basis
FP16	248.4 GB	194%	short by 120.4 GB	does not run	—	size range
Q8_0	132.5 GB	104%	short by 4.49 GB	does not run	—	size range
Q6_K	102.6 GB	80%	yes	~3.6 tok/sSlow	no comparable peak	size range
Q5_K_M	89.3 GB	70%	yes	~4.1 tok/sSlow	no comparable peak	size range
Q5_0	87.3 GB	68%	yes	~4.2 tok/sSlow	no comparable peak	size range
Q4_K_M	76.9 GB	60%	yes	~4.8 tok/sSlow	no comparable peak	size range
Q4_0	73.0 GB	57%	yes	~5.0 tok/sSlow	no comparable peak	size range
Q3_K_M	62.9 GB	49%	yes	~5.8 tok/sSlow	no comparable peak	size range
Q2_K	50.0 GB	39%	yes	~7.3 tok/sSlow	no comparable peak	size range

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 M3 Max runs Best models for 128 GB

Where these numbers come from

NVIDIA-Nemotron-3-Super-120B-A12B-BF16’s architecture is read from its publisher’s own config.json at a pinned revision, and the Apple M3 Max’s 128 GB and 400 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":"nvidia-nvidia-nemotron-3-super-120b-a12b-bf16","quantization":"Q6_K","context":8192,"hardware":"apple-m3-max"}'

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On your page, as a widget

<script src="https://llmbottleneck.com/widget.js"
  data-model="nvidia-nvidia-nemotron-3-super-120b-a12b-bf16" data-quantization="Q6_K"
  data-hardware="apple-m3-max" data-context="8192"></script>

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