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zai-org / glm5_next_text

GLM-5.3-Flash

321.3 billion parameters, routing 8 of 288 experts per token.

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Architecturepublished data
Quick answer
GLM-5.3-Flash needs about 198.3 GB at Q4_K_M — the format most people download — with 8,192 tokens of context. None of the 18 common devices listed below holds it entirely at this setting. No hardware for it? Rent a GPU that runs it, priced live.

Sized at 8,192 tokens of context with the whole model in device memory, across 18 common devices. Speeds are estimates, not benchmarks.

Weight-file size by format

FormatSizeRangeBasis
FP16642.9 GB449.9 GB – 649.1 GBsize range
Q8_0341.7 GB239.0 GB – 346.0 GBsize range
Q6_K263.9 GB184.5 GB – 267.7 GBsize range
Q5_K_M229.5 GB154.6 GB – 267.7 GBsize range
Q5_0224.2 GB154.6 GB – 267.7 GBsize range
Q4_K_M197.3 GB126.5 GB – 267.7 GBsize range
Q4_0187.0 GB126.5 GB – 267.7 GBsize range
Q3_K_M160.7 GB96.6 GB – 267.7 GBsize range
Q2_K127.2 GB73.8 GB – 267.7 GBsize range

A file size is not the memory a run needs: the KV cache and the runtime reserve come on top, and the calculator adds both.

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.

Hardware ladder

Q4_K_M at 8,192 tokens · 0 of 18 devices hold it entirely
DeviceMemoryVerdictDecodeCalculator
NVIDIA GeForce RTX 5070198.3 GB of 12 GB43 layers on system RAM~6.8 tok/s with offloadOpen →
AMD Radeon™ RX 9070 XT198.3 GB of 16 GB42 layers on system RAM~6.9 tok/s with offloadOpen →
NVIDIA GeForce RTX 4080198.3 GB of 16 GB42 layers on system RAM~6.9 tok/s with offloadOpen →
NVIDIA GeForce RTX 5080198.3 GB of 16 GB42 layers on system RAM~6.9 tok/s with offloadOpen →
NVIDIA GeForce RTX 5070 Ti198.3 GB of 16 GB42 layers on system RAM~6.9 tok/s with offloadOpen →
NVIDIA GeForce RTX 5060 Ti 16GB198.3 GB of 16 GB42 layers on system RAM~6.9 tok/s with offloadOpen →
NVIDIA GeForce RTX 4090198.3 GB of 24 GB40 layers on system RAM~7.3 tok/s with offloadOpen →
AMD Radeon™ RX 7900 XTX198.3 GB of 24 GB40 layers on system RAM~7.3 tok/s with offloadOpen →
NVIDIA GeForce RTX 3090198.3 GB of 24 GB40 layers on system RAM~7.3 tok/s with offloadOpen →
NVIDIA GeForce RTX 5090198.3 GB of 32 GB38 layers on system RAM~7.7 tok/s with offloadOpen →
NVIDIA RTX 6000 Ada Generation198.3 GB of 48 GB35 layers on system RAM~8.2 tok/s with offloadOpen →
Apple M4 Pro198.3 GB of 64 GBdoes not fitnot calibratedOpen →
Apple M5 Pro198.3 GB of 64 GBdoes not fitnot calibratedOpen →
AMD Ryzen AI Max+ 395 with Radeon 8060S198.3 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M3 Max198.3 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M4 Max198.3 GB of 128 GBdoes not fitnot calibratedOpen →
NVIDIA DGX Spark198.3 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M2 Ultra198.3 GB of 192 GBdoes not fitnot calibratedOpen →

Decode is a calibrated estimate from the published calibration; capacity is the manufacturer's published ceiling, not guaranteed free memory. Fitting in memory is not the same as loading: whether the runtime and version you have supports this architecture and format on that machine has not been tested here. Offloaded rows assume there is enough system RAM for the overflow — the calculator checks that against the RAM you declare. Each “Open” link keeps this model, format and context.

Run it in the cloud

Rent a GPU that runs GLM-5.3-Flash

Every rentable machine that holds the whole model, from Vast.ai and RunPod, with this site's speed estimate for each and the provider's own price, read live. Cheapest first; sort by cost per token or speed instead, or switch the format.

Prices · 18:30 UTC, 22 Sept
7 configurations in stock · 9 out of stock
MachineSpeedPer hourPer M tokensWhereRent
8× L40S384 GBrecommendedcheapestbest value~414 tok/sFaster than you read$3.73$2.51Vast.aiMarketplace · 99.9% reliableRent on Vast.ai
8× RTX PRO 5000384 GB~644 tok/sFaster than you read$6.81$2.94Vast.aiMarketplace · 98.3% reliableRent on Vast.ai
2× H200 NVL282 GB~671 tok/sFaster than you read$7.20$2.98Vast.aiMarketplace · 98.7% reliableRent on Vast.ai
1× B300270 GB~554 tok/sFaster than you read$7.89$3.95RunPodSecure CloudRent on RunPod
4× H100 SXM320 GBfastest~887 tok/sFaster than you read$9.07$2.84Vast.aiMarketplace · 99.9% reliableRunPod $13.96/h Rent on Vast.ai
2× H200282 GB~671 tok/sFaster than you read$9.18$3.80RunPodSecure CloudVast.ai $10.00/h Rent on RunPod
4× H100 PCIe320 GB~530 tok/sFaster than you read$9.59$5.02Vast.aiMarketplace · 98.3% reliableRent on Vast.ai
No GPU at all

Use GLM-5.3-Flash by the token

For one person chatting, that is about 13× cheaper than the best-value rented card above ($2.51 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
  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.
  4. 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-Flash 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

Buying a card instead, or paying by the token? Compare a year of GLM-5.3-Flash three ways →

Under the hood

Architecture, read from the publisher’s file

The numbers every figure above is computed from, with the file they came from.

Architecture

✓ Architecture read from the published config.json

Retrieved 2026-09-01 at pinned commit 03eb5366286a; parameter count from the safetensors index at the same revision.

Architecture
glm5_next_text
Layers
45
Hidden size
4,096
Attention heads
64
KV heads
64
Head dimension
0
Feed-forward width
12,288
Vocabulary
154,880
Context ceiling
1,048,576
Experts
288
Experts / token
8
Expert width
2,048
Shared experts
1
Latent KV rank
512

Where the memory goes

tokenembedding× 45 decoder blocksLatent attentioncompressed KV rankfull contextRouted experts8 of 288 per tokenall residentoutputprojectiongrows with contextcapacity ≠ traffic

Latent attention projects keys and values into a compressed rank before caching them, which is why its KV cache is a fraction of a comparable dense model.

This is a multimodal checkpoint (vision); the diagram and memory sizing above cover the text decoder. Encoder/audio/image activations are not included in the KV or weight figures.

Open the exact official config.json ↗

GLM-5.3-Flash, card by card

One page per device: whether GLM-5.3-Flash fits, at which formats, and how fast.

All 30Fewer

Derivatives this page also answers for

Each of these repositories declares GLM-5.3-Flash as its base, and its published configuration matches this one on every field that decides memory. Its page lists what was compared and any files it publishes. Paste any other repository.

cantina-security/apex-flash-1

More from zai-org

GLM-4.5GLM-4.5-AirGLM-4.5VGLM-4.6GLM-4.6VGLM-4.6V-FlashGLM-4.7GLM-4.7-Flash

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  -H "Authorization: Bearer $LLMB_KEY" -H "content-type: application/json" \
  -d '{"model":"zai-org-glm-5-3-flash","quantization":"Q4_K_M","context":8192,"hardware":"nvidia-geforce-rtx-4090"}'

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  data-model="zai-org-glm-5-3-flash" data-quantization="Q4_K_M"
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Citing this page

LLM Bottleneck. “GLM-5.3-Flash VRAM and hardware requirements.” Architecture from zai-org/GLM-5.3-Flash at revision 03eb5366286a, retrieved 2026-09-01. https://llmbottleneck.com/models/zai-org-glm-5-3-flash

Every figure above is either the published value or a reconstruction whose measured error is on the accuracy page.

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catalogue 2026-10-03models 327devices 135