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Can the Apple M2 Max run NVIDIA-Nemotron-3-Super-120B-A12B-BF16?

NVIDIA-Nemotron-3-Super-120B-A12B-BF16 · Apple M2 Max · 8,192 ctx · whole model residentQ5_K_M sizesize rangeSpeedestimate

IT FITS

Yes. 6 of 9 formats evaluated fit in 96 GB, the largest Apple M2 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 Q5_K_M, needing 89.3 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 M2 Max: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

6/9formats that fit
96 GBdevice memory, largest configuration
89.3 GBneeded at best quality
~4.1tokens 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 M2 Max only holds it at Q5_K_M, at an estimated ~4.1 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
—

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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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  1. 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.
  2. 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.
  3. 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 M2 Max instead

The models nearest to NVIDIA-Nemotron-3-Super-120B-A12B-BF16 — same lab first, then closest in size — that the Apple M2 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.

How long a conversation. At Q5_K_M, this pair holds 262,144 tokens of context — roughly 196,608 words — needing 91.4 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 M2 Max is sold with 32, 64 or 96 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.

MemoryBest format that fitsNeedsCalculator
32 GBnone fits—Open →
64 GBQ3_K_M62.9 GBOpen →
96 GBQ5_K_M89.3 GBOpen →

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.

FormatNeedsOf 96 GBFitsDecodeFirst tokenBasis
FP16248.4 GB259%short by 152.4 GBdoes not run—size range
Q8_0132.5 GB138%short by 36.5 GBdoes not run—size range
Q6_K102.6 GB107%short by 6.56 GBdoes not run—size range
Q5_K_M89.3 GB93%yes~4.1 tok/sSlowno comparable peaksize range
Q5_087.3 GB91%yes~4.2 tok/sSlowno comparable peaksize range
Q4_K_M76.9 GB80%yes~4.8 tok/sSlowno comparable peaksize range
Q4_073.0 GB76%yes~5.0 tok/sSlowno comparable peaksize range
Q3_K_M62.9 GB65%yes~5.8 tok/sSlowno comparable peaksize range
Q2_K50.0 GB52%yes~7.3 tok/sSlowno comparable peaksize 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 M2 Max runsBest models for 96 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 M2 Max’s 96 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.

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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":"Q5_K_M","context":8192,"hardware":"apple-m2-max"}'

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<script src="https://llmbottleneck.com/widget.js"
  data-model="nvidia-nvidia-nemotron-3-super-120b-a12b-bf16" data-quantization="Q5_K_M"
  data-hardware="apple-m2-max" data-context="8192"></script>

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