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zai-org / glm4v_moe_text

GLM-4.5V

107.7 billion parameters, routing 8 of 128 experts per token.

Open in the calculator →

Architecturepublished data
Quick answer
GLM-4.5V needs about 66.3 GB at Q4_K_M — the format most people download — with 8,192 tokens of context. It fits entirely on 5 of 18 common devices, starting with the AMD Ryzen AI Max+ 395 with Radeon 8060S (128 GB). What else fits in 128 GB. 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.

Find the AMD Ryzen AI Max+ 395 with Radeon 8060S, the smallest card listed here that holds it: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

Weight-file size by format

FormatSizeRangeBasis
FP16212.2 GB207.9 GB – 216.5 GBreconstructed size
Q8_0112.8 GB110.5 GB – 115.1 GBreconstructed size
Q6_K87.1 GB85.3 GB – 88.9 GBreconstructed size
Q5_K_M75.2 GB73.7 GB – 76.7 GBreconstructed size
Q5_073.1 GB71.6 GB – 74.6 GBreconstructed size
Q4_K_M63.9 GB62.7 GB – 65.2 GBreconstructed size
Q4_059.9 GB58.7 GB – 61.1 GBreconstructed size
Q3_K_M52.3 GB47.9 GB – 56.8 GBreconstructed size
Q2_K38.6 GB36.5 GB – 40.7 GBreconstructed size

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 · 5 of 18 devices hold it entirely
DeviceMemoryVerdictDecodeCalculator
NVIDIA GeForce RTX 507066.3 GB of 12 GB40 layers on system RAM~9.0 tok/s with offloadOpen →
AMD Radeon™ RX 9070 XT66.3 GB of 16 GB37 layers on system RAM~9.6 tok/s with offloadOpen →
NVIDIA GeForce RTX 408066.3 GB of 16 GB37 layers on system RAM~9.6 tok/s with offloadOpen →
NVIDIA GeForce RTX 508066.3 GB of 16 GB37 layers on system RAM~9.8 tok/s with offloadOpen →
NVIDIA GeForce RTX 5070 Ti66.3 GB of 16 GB37 layers on system RAM~9.7 tok/s with offloadOpen →
NVIDIA GeForce RTX 5060 Ti 16GB66.3 GB of 16 GB37 layers on system RAM~9.3 tok/s with offloadOpen →
NVIDIA GeForce RTX 409066.3 GB of 24 GB31 layers on system RAM~11 tok/s with offloadOpen →
AMD Radeon™ RX 7900 XTX66.3 GB of 24 GB31 layers on system RAM~11 tok/s with offloadOpen →
NVIDIA GeForce RTX 309066.3 GB of 24 GB31 layers on system RAM~11 tok/s with offloadOpen →
NVIDIA GeForce RTX 509066.3 GB of 32 GB25 layers on system RAM~14 tok/s with offloadOpen →
NVIDIA RTX 6000 Ada Generation66.3 GB of 48 GB14 layers on system RAM~21 tok/s with offloadOpen →
Apple M4 Pro66.3 GB of 64 GBdoes not fitnot calibratedOpen →
Apple M5 Pro66.3 GB of 64 GBdoes not fitnot calibratedOpen →
AMD Ryzen AI Max+ 395 with Radeon 8060S66.3 GB of 128 GBfits~23 tok/sAbout reading paceOpen →
Apple M3 Max66.3 GB of 128 GBfits~34 tok/sOpen →
Apple M4 Max66.3 GB of 128 GBfits~44 tok/sOpen →
NVIDIA DGX Spark66.3 GB of 128 GBfits~21 tok/sAbout reading paceOpen →
Apple M2 Ultra66.3 GB of 192 GBfits~57 tok/sOpen →

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-4.5V

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
18 configurations in stock · 14 out of stock
MachineSpeedPer hourPer M tokensWhereRent
8× RTX 306096 GBcheapest~221 tok/sFaster than you read$0.59$0.74Vast.aiMarketplace · 99.7% reliableRent on Vast.ai
4× RTX 309096 GB~287 tok/sFaster than you read$0.60$0.58Vast.aiMarketplace · 99.4% reliableRent on Vast.ai
4× RTX 5090128 GBrecommendedbest value~551 tok/sFaster than you read$1.07$0.54Vast.aiMarketplace · 99.6% reliableRent on Vast.ai
2× RTX 6000 Ada96 GB~147 tok/sFaster than you read$1.07$2.02Vast.aiMarketplace · 99.4% reliableRent on Vast.ai
1× RTX PRO 600096 GB~137 tok/sFaster than you read$1.14$2.29Vast.aiMarketplace · 98.8% reliableRunPod $1.69/h Rent on Vast.ai
1× A100 80GB PCIe80 GB~148 tok/sFaster than you read$1.19$2.22RunPodCommunity CloudRent on RunPod
2× RTX A600096 GB~118 tok/sFaster than you read$1.20$2.83Vast.aiMarketplace · 99.4% reliableRent on Vast.ai
4× L496 GB~92 tok/sFaster than you read$1.29$3.87Vast.aiMarketplace · 99.4% reliableRent on Vast.ai

No GPU at all

Use GLM-4.5V by the token

Here the rented card is as cheap as the API or cheaper per token ($0.54 per million), and it keeps your data on a machine you control. The API is still the easier start: nothing to set up and nothing to switch off.

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-4.5V 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-4.5V 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 ed47433b3711; parameter count from the safetensors index at the same revision.

Architecture
glm4v_moe_text
Layers
46
Hidden size
4,096
Attention heads
96
KV heads
8
Head dimension
128
Feed-forward width
10,944
Vocabulary
151,552
Context ceiling
65,536
RoPE theta
10,000
Experts
128
Experts / token
8
Expert width
1,408
Shared experts
1

Where the memory goes

tokenembedding× 46 decoder blocksGrouped-query attention96 query · 8 KV headsfull contextRouted experts8 of 128 per tokenall residentoutputprojectiongrows with contextcapacity ≠ traffic

Grouped-query attention shares each key/value head across 12 query heads, so the KV cache is 8% of what multi-head attention would need at the same context.

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 ↗

More from zai-org

GLM-4.5GLM-4.5-AirGLM-4.6GLM-4.6VGLM-4.6V-FlashGLM-4.7GLM-4.7-FlashGLM-5

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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-4-5v","quantization":"Q4_K_M","context":8192,"hardware":"nvidia-geforce-rtx-4090"}'

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  data-model="zai-org-glm-4-5v" data-quantization="Q4_K_M"
  data-hardware="nvidia-geforce-rtx-4090" data-context="8192"></script>

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Citing this page

LLM Bottleneck. “GLM-4.5V VRAM and hardware requirements.” Architecture from zai-org/GLM-4.5V at revision ed47433b3711, retrieved 2026-09-01. https://llmbottleneck.com/models/zai-org-glm-4-5v

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