Can the Apple M5 Pro run DeepSeek-Coder-V2-Lite-Instruct?

DeepSeek-Coder-V2-Lite-Instruct · Apple M5 Pro · 8,192 ctx · whole model residentFP16 sizesize rangeSpeedestimate

IT FITS

Yes. 9 of 9 formats evaluated fit in 64 GB, the largest Apple M5 Pro 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 32.5 GB and running at an estimated 49 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 M5 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

64 GBdevice memory, largest configuration

32.5 GBneeded at best quality

~49tokens per second, estimatedFaster than you read

307GB/s bandwidth

How long a conversation. At FP16, this pair holds 163,840 tokens of context — roughly 122,880 words — needing 37.3 GB. That is DeepSeek's own configured maximum of 163,840 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. The nearest round setting below it is 131,072.

By memory configuration

The Apple M5 Pro is sold with 24, 48 or 64 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
24 GB	Q8_0	17.8 GB	Open →
48 GB	FP16	32.5 GB	Open →
64 GB	FP16	32.5 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 64 GB	Fits	Decode	First token	Basis
FP16	32.5 GB	51%	yes	~49 tok/s	no comparable peak	size range
Q8_0	17.8 GB	28%	yes	~74 tok/s	no comparable peak	size range
Q6_K	14.0 GB	22%	yes	~84 tok/s	no comparable peak	size range
Q5_K_M	12.3 GB	19%	yes	~86 tok/s	no comparable peak	size range
Q5_0	12.0 GB	19%	yes	~87 tok/s	no comparable peak	size range
Q4_K_M	10.7 GB	17%	yes	~91 tok/s	no comparable peak	size range
Q4_0	10.2 GB	16%	yes	~93 tok/s	no comparable peak	size range
Q3_K_M	8.91 GB	14%	yes	~98 tok/s	no comparable peak	size range
Q2_K	7.27 GB	11%	yes	~106 tok/s	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 M5 Pro runs Best models for 64 GB

Where these numbers come from

DeepSeek-Coder-V2-Lite-Instruct’s architecture is read from its publisher’s own config.json at a pinned revision, and the Apple M5 Pro’s 64 GB and 307 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-coder-v2-lite-instruct","quantization":"FP16","context":8192,"hardware":"apple-m5-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="deepseek-ai-deepseek-coder-v2-lite-instruct" data-quantization="FP16"
  data-hardware="apple-m5-pro" data-context="8192"></script>

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