Can the Apple M4 Max run DeepSeek-V4-Flash-0731?

DeepSeek-V4-Flash-0731 · Apple M4 Max · 8,192 ctx · whole model residentQ2_K sizesize rangeSpeedestimate

IT FITS

Yes. 1 of 9 formats evaluated fit in 128 GB, the largest Apple M4 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 Q2_K, needing 121.3 GB and running at an estimated 52 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 M4 Max: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

1/9formats that fit

128 GBdevice memory, largest configuration

121.3 GBneeded at best quality

~52tokens per second, estimatedFaster than you read

546GB/s bandwidth

Run it in the cloud

Run DeepSeek-V4-Flash-0731 properly, for cents an hour

The Apple M4 Max only holds it at Q2_K, at an estimated ~52 tokens per second. A rented card runs a better format at full speed, billed by the second.

Prices · 18:30 UTC, 22 Sept

Recommended · Cheapest192 GB

4× L40S

$1.87per hour

Speed: ~203 tok/sFaster than you read
Per million tokens: $2.55

Vast.ai · Marketplace · 99.2% reliable

Rent on Vast.ai

Lowest cost per token at 20+ tok/s

2.8× faster282 GB

2× H200 NVL

$7.20per hour

Speed: ~574 tok/sFaster than you read
Per million tokens: $3.48

Vast.ai · Marketplace · 98.7% reliable

Rent on Vast.ai

Cheapest at 1.6× the speed or more

Fastest320 GB

4× H100 SXM

$9.07per hour

Speed: ~787 tok/sFaster than you read
Per million tokens: $3.20

Vast.ai · Marketplace · 99.9% reliable

Rent on Vast.ai

or RunPod $13.96/h

Compare all 9 rentable configurations →

No GPU at all

Use DeepSeek-V4-Flash-0731 by the token

OpenRouter$0.64 / M output · $0.040 / M inputOpen on OpenRouter
Novita AI$1.32 / M output · $0.44 / M inputOpen on Novita AI

For one person chatting, that is about 4.0× cheaper than the best-value rented card above ($2.55 per million tokens). A rented GPU pays off when you keep it busy — many requests batched together, long agent runs — or when the data must stay on a machine you control, or the exact file you want is not served anywhere.

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 DeepSeek-V4-Flash-0731 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 M4 Max instead

The models nearest to DeepSeek-V4-Flash-0731 — same lab first, then closest in size — that the Apple M4 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.

DeepSeek-R1-Distill-Llama-70BQ4_K_M · 46.0 GB · ~11 tok/s
DeepSeek-R1-Distill-Qwen-32BQ4_K_M · 22.8 GB · ~21 tok/s
DeepSeek-R1-Distill-Qwen-14BQ4_K_M · 11.4 GB · ~39 tok/s
DeepSeek-Coder-V2-Lite-InstructQ4_K_M · 10.7 GB · ~116 tok/s

How long a conversation. At Q2_K, this pair holds 618,162 tokens of context — roughly 463,622 words — needing 128.0 GB. Past that the Apple M4 Max runs out of memory, not the model out of context, which would allow 1,048,576. This is the wall a long chat hits after it has already loaded fine. The nearest round setting below it is 524,288.

By memory configuration

The Apple M4 Max is sold with 36, 48, 64 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	none fits	—	Open →
128 GB	Q2_K	121.3 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	609.2 GB	476%	short by 481.2 GB	does not run	—	size range
Q8_0	324.2 GB	253%	short by 196.2 GB	does not run	—	size range
Q6_K	250.6 GB	196%	short by 122.6 GB	does not run	—	size range
Q5_K_M	218.1 GB	170%	short by 90.1 GB	does not run	—	size range
Q5_0	213.1 GB	166%	short by 85.1 GB	does not run	—	size range
Q4_K_M	187.5 GB	146%	short by 59.5 GB	does not run	—	size range
Q4_0	177.8 GB	139%	short by 49.8 GB	does not run	—	size range
Q3_K_M	152.9 GB	119%	short by 24.9 GB	does not run	—	size range
Q2_K	121.3 GB	95%	yes	~52 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 M4 Max runs Best models for 128 GB

Where these numbers come from

DeepSeek-V4-Flash-0731’s architecture is read from its publisher’s own config.json at a pinned revision, and the Apple M4 Max’s 128 GB and 546 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-0731","quantization":"Q2_K","context":8192,"hardware":"apple-m4-max"}'

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-0731" data-quantization="Q2_K"
  data-hardware="apple-m4-max" data-context="8192"></script>

No key needed. Unbranded, with your own buy button, on Pro and Business →