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Can the Apple M5 Ultra run DeepSeek-V4-Flash?

DeepSeek-V4-Flash · Apple M5 Ultra · 8,192 ctx · whole model residentQ8_0 sizereconstructed sizeSpeedestimate

IT FITS

Yes. 8 of 9 formats evaluated fit in 512 GB, the largest Apple M5 Ultra 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 Q8_0, needing 309.0 GB and running at an estimated 43 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.

8/9formats that fit
512 GBdevice memory, largest configuration
309.0 GBneeded at best quality
~43tokens per second, estimatedFaster than you read
1200GB/s bandwidth

How long a conversation. At Q8_0, this pair holds 1,048,576 tokens of context — roughly 786,432 words — needing 320.5 GB. That is DeepSeek's own configured maximum of 1,048,576 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 Ultra is sold with 96, 256 or 512 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
96 GBnone fits—Open →
256 GBQ6_K238.8 GBOpen →
512 GBQ8_0309.0 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 512 GBFitsDecodeFirst tokenBasis
FP16580.7 GB113%short by 68.7 GBdoes not run—reconstructed size
Q8_0309.0 GB60%yes~43 tok/sno comparable peakreconstructed size
Q6_K238.8 GB47%yes~52 tok/sno comparable peakreconstructed size
Q5_K_M206.4 GB40%yes~57 tok/sno comparable peakreconstructed size
Q5_0200.4 GB39%yes~57 tok/sno comparable peakreconstructed size
Q4_K_M175.9 GB34%yes~64 tok/sno comparable peakreconstructed size
Q4_0164.2 GB32%yes~65 tok/sno comparable peakreconstructed size
Q3_K_M143.7 GB28%yes~73 tok/sno comparable peakreconstructed size
Q2_K105.8 GB21%yes~84 tok/sno comparable peakreconstructed 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 Ultra runs

Where these numbers come from

DeepSeek-V4-Flash’s architecture is read from its publisher’s own config.json at a pinned revision, and the Apple M5 Ultra’s 512 GB and 1200 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":"deepseek-ai-deepseek-v4-flash","quantization":"Q8_0","context":8192,"hardware":"apple-m5-ultra"}'

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="deepseek-ai-deepseek-v4-flash" data-quantization="Q8_0"
  data-hardware="apple-m5-ultra" data-context="8192"></script>

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catalogue 2026-10-03models 327devices 135