Can the NVIDIA Rubin run GLM-5.3?

GLM-5.3 · NVIDIA Rubin · 8,192 ctx · whole model residentQ2_K sizereconstructed sizeSpeedestimate

IT FITS

Yes. 1 of 9 formats evaluated fit in 288 GB, 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 270.2 GB and running at an estimated 1000 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.

1/9formats that fit

288 GBdevice memory

270.2 GBneeded at best quality

~1000tokens per second, estimatedFaster than you read

22000GB/s bandwidth

Run it in the cloud

Run GLM-5.3 properly, for cents an hour

The NVIDIA Rubin only holds it at Q2_K, at an estimated ~1000 tokens per second. A rented card runs a better format at full speed, billed by the second.

Prices · 18:30 UTC, 22 Sept

Cheapest540 GB

2× B300

$15.78per hour

Speed: ~426 tok/sFaster than you read
Per million tokens: $10.28

RunPod · Secure Cloud

Rent on RunPod

Lowest price per hour

Recommended · Fastest720 GB

4× B200

$25.00per hour

Speed: ~794 tok/sFaster than you read
Per million tokens: $8.74

Vast.ai · Marketplace · 99.6% reliable

Rent on Vast.ai

Lowest cost per token at 20+ tok/s

Compare all 6 rentable configurations →

No GPU at all

Use GLM-5.3 by the token

OpenRouter$2.05 / M output · $0.65 / M inputOpen on OpenRouter
Novita AI$4.40 / M output · $1.40 / M inputOpen on Novita AI

For one person chatting, that is about 4.3× cheaper than the best-value rented card above ($8.74 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 GLM-5.3 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 NVIDIA Rubin instead

The models nearest to GLM-5.3 — same lab first, then closest in size — that the NVIDIA Rubin 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.

GLM-5.3-FlashQ4_K_M · 198.3 GB · ~1583 tok/s
GLM-4.7Q4_K_M · 223.9 GB · ~690 tok/s
GLM-4.6Q4_K_M · 215.7 GB · ~690 tok/s
GLM-4.5Q4_K_M · 223.9 GB · ~690 tok/s

How long a conversation. At Q2_K, this pair holds 170,133 tokens of context — roughly 127,600 words — needing 288.0 GB. Past that the NVIDIA Rubin 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 131,072.

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 288 GB	Fits	Decode	First token	Basis
FP16	1482.6 GB	515%	short by 1194.6 GB	does not run	—	reconstructed size
Q8_0	788.7 GB	274%	short by 500.7 GB	does not run	—	reconstructed size
Q6_K	609.4 GB	212%	short by 321.4 GB	does not run	—	reconstructed size
Q5_K_M	526.3 GB	183%	short by 238.3 GB	does not run	—	reconstructed size
Q5_0	511.2 GB	178%	short by 223.2 GB	does not run	—	reconstructed size
Q4_K_M	448.1 GB	156%	short by 160.1 GB	does not run	—	reconstructed size
Q4_0	418.8 GB	145%	short by 130.8 GB	does not run	—	reconstructed size
Q3_K_M	367.1 GB	127%	short by 79.1 GB	does not run	—	reconstructed size
Q2_K	270.2 GB	94%	yes	~1000 tok/s	no comparable peak	reconstructed 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 NVIDIA Rubin runs

Where these numbers come from

GLM-5.3’s architecture is read from its publisher’s own config.json at a pinned revision, and the NVIDIA Rubin’s 288 GB and 22000 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":"zai-org-glm-5-3","quantization":"Q2_K","context":8192,"hardware":"nvidia-rubin"}'

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On your page, as a widget

<script src="https://llmbottleneck.com/widget.js"
  data-model="zai-org-glm-5-3" data-quantization="Q2_K"
  data-hardware="nvidia-rubin" data-context="8192"></script>

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