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Z.ai · Mixture of experts · released August 2026

GLM-5.3 GPU requirements: 377 GB at 4-bit, 1.51 TB at BF16

GLM-5.3 needs 1.51 TB of GPU memory for its weights at BF16, 756 GB at FP8 and 377 GB at 4-bit, plus 2.94 GB of KV cache for a 32K context. The cheapest node that runs it here is 8× A100 PCIe 80GB at 4-bit, for $0.72 an hour on spot.

Architecture read from the model's config.json on Hugging Face, 26 October 2026 · prices from the live catalogue · all 47 GPUs

Cheapest node · 32K context per node-hour, spot
4-bit8× A100 PCIe 80GB · 380 GB needed $0.72$1.52 on-demand
FP8 (native)4× MI325X · 759 GB needed $4.36$6.36 on-demand
BF168× MI325X · 1.51 TB needed $8.72$12.72 on-demand
Deploy 8× A100 PCIe All models

Weights plus a 32K 16-bit KV cache, with 10% of the node's memory kept free. Billed per minute.

Parameters
744B40B active per token
Weights, 4-bit
377 GBBF16: 1.51 TB
KV cache, 32K
2.94 GBOne sequence, 16-bit cache
Context window
1MNative
Memory by format

How much VRAM GLM-5.3 needs, format by format.

Weights are parameters × bytes per parameter, except the native FP8 row, which is the size of the published checkpoint. Each context column adds the KV cache of one sequence of that length.

GPUs by memory
Format Weights + 8Kcontext+ 32Kcontext+ 128Kcontext+ 1Mcontext Cheapest node, 32K
BF16Published weights, fine-tuning 1.51 TB 1.51 TB1.51 TB1.52 TB1.6 TB 8× MI325X · $8.72/h
FP8 (native)Hopper, Ada, Blackwell, MI300 756 GB 756 GB759 GB767 GB850 GB 4× MI325X · $4.36/h
4-bitAWQ, GPTQ, GGUF Q4 377 GB 377 GB380 GB388 GB471 GB 8× A100 PCIe 80GB · $0.72/h
Arithmetic on published facts, not a benchmark.

The serving engine adds its own buffers, and an 8-bit KV cache halves the context columns. We keep 10% of a node's memory free when we say a model fits. Speed is a separate question: it follows memory bandwidth and, for a mixture of experts, the active parameters.

Where it runs

GPUs that run GLM-5.3, cheapest first.

For each format, the smallest node of each GPU model that holds the weights and a 32K context, sorted by spot price. One row per GPU model; larger nodes of the same GPU work too.

All prices
Format GPU GPU memory Spot On-demand 730 h spot
4-bit 8× A100 PCIe 80GBAmpere · 380 GB needed 640 GB $0.72 $1.52 $525.60 Deploy
4-bit 8× A100 SXM 80GBAmpere · 380 GB needed 640 GB $1.52 $2.32 $1,109.60 Deploy
4-bit 8× RTX PRO 6000 BlackwellBlackwell · 380 GB needed 768 GB $1.52 $3.12 $1,109.60 Deploy
4-bit 2× MI325XCDNA 3 · 380 GB needed 512 GB $2.18 $3.18 $1,591.40 Deploy
FP8 (native) 4× MI325XCDNA 3 · 759 GB needed 1024 GB $4.36 $6.36 $3,182.80 Deploy
FP8 (native) 8× H200 NVLHopper · 759 GB needed 1128 GB $5.52 $7.92 $4,029.60 Deploy
FP8 (native) 8× H200 SXMHopper · 759 GB needed 1128 GB $6.00 $8.72 $4,380.00 Deploy
FP8 (native) 8× MI300XCDNA 3 · 759 GB needed 1536 GB $7.92 $11.60 $5,781.60 Deploy
BF16 8× MI325XCDNA 3 · 1.51 TB needed 2048 GB $8.72 $12.72 $6,365.60 Deploy

Spot capacity can be reclaimed with a 2-minute notice; for a live endpoint, use on-demand. How spot works · serving a language model.

Context and the KV cache

What a long context costs in memory.

Multi-head latent attention: one compressed 576-value vector per token and layer, 78 layers.

Context KV cache, 1 sequence 8 sequences
8K tokens 0.74 GB 5.89 GB
32K tokens 2.94 GB 23.6 GB
128K tokens 11.8 GB 94.2 GB
1M tokens 94.2 GB 754 GB

Serving several users

KV cache

Every concurrent request keeps its own cache, so memory for context grows with users × context length. A server batching 8 requests of 32K needs eight times the single-sequence figure — 23.6 GB here.

  • Recommended node8× A100 PCIe 80GB
  • Weights at 4-bit377 GB
  • Context that fits, 1 sequence1M

Paged attention (vLLM, SGLang) allocates the cache in blocks as requests grow, so a node holds more short requests than the worst case suggests.

Model facts

GLM-5.3, as published.

Developer
Z.aiFlagship GLM with sparse attention, 1M-token context
Parameters
744B total, 40B active per token
Architecture
Mixture of experts, 78 layersMulti-head latent attention: one compressed 576-value vector per token and layer, 78 layers
Context window
1,048,576 tokensAs set in the published configuration
Published format
FP8, 756 GB checkpoint
Licence
Custom licence (see the model card)
Released
25 August 2026
Source
zai-org/GLM-5.3config.json read 26 October 2026

Serve it with vLLM

template: vLLM

On 8× A100 PCIe 80GB, from the instance terminal:

terminal
$ vllm serve zai-org/GLM-5.3 \
    --tensor-parallel-size 8 \
    --max-model-len 32768

Very recent architectures need a recent vLLM; the model card gives the minimum version and any extra flags.

FAQ

GLM-5.3 on a GPU — the questions that come up

How much VRAM does GLM-5.3 need?

For the weights alone: 1.51 TB at BF16, 756 GB at FP8 and 377 GB at 4-bit. Add 2.94 GB of KV cache for one 32K-token context, or 94.2 GB at the full 1M window, and keep about 10% of the memory free for activations.

Can GLM-5.3 run on a single GPU?

One RTX 4090 (24 GB): no. One RTX 5090 (32 GB): no. One H100 SXM (80 GB): no. One H200 SXM (141 GB): no. One B200 SXM (180 GB): no. Those answers assume a 32K context and the widest format that still fits.

What is the cheapest way to run GLM-5.3?

Here, 8× A100 PCIe 80GB at 4-bit: $0.72 per hour on spot and $1.52 on-demand, billed per minute — $525.60 for a 730-hour month on spot. That node has 640 GB of GPU memory for the 380 GB this setup needs.

Does GLM-5.3 need less memory because only 40B parameters are active?

No. A mixture-of-experts model routes each token through a few experts, but every expert has to be resident in GPU memory, so the memory bill follows the 744B total. What the 40B active parameters change is the work per token: generation reads far less of the weights each step than a dense model of the same size would, which is why it is fast for its size.

Which format should I run it in?

GLM-5.3 is published in FP8, a 756 GB checkpoint — the format its developers serve it in, so it is the safe default. BF16 keeps the published weights untouched and is the format for fine-tuning. FP8 halves the memory and runs in hardware on Hopper, Ada, Blackwell and MI300-class GPUs. 4-bit quantisation (AWQ, GPTQ, GGUF) quarters it, at a quality cost that is larger on small models than on big ones; check it against your own evaluation before relying on it.

How do I deploy it?

Pick the node in the table above and launch it with the vLLM template; the model downloads from Hugging Face (zai-org/GLM-5.3) on first start. Spread the weights over the node with --tensor-parallel-size equal to the GPU count, and set --max-model-len to the context you need. New architectures need a recent vLLM: check the model card for the minimum version.

Sizing another model? GPU requirements for 64 open models · serving a language model · fine-tuning.

Get started

GLM-5.3 on 8× A100 PCIe 80GB for $0.72 an hour.

Launch the node with the vLLM template, point it at zai-org/GLM-5.3, stop it when you are done. Prepaid, billed by the minute.

Deploy 8× A100 PCIe All open models

Billed per minute from the moment the instance is reachable. Minimum credit $30, no subscription.