GLM-5.2 has 753.3B parameters (42B active per token). In BF16 its weights alone take 1385 GiB; quantized to 4-bit, about 418 GiB. Each 32K-token request adds 2.83 GiB of KV cache. Cheapest way to run it today: 7 × NVIDIA A100 80GB at $6.54 per hour.
Read every day from Hugging Face. Split files are added together; vision projectors are left out.
How many GPUs to run GLM-5.2
Fewest GPUs that fit the weights plus one 8K-token request. Data-center GPUs use vLLM (90% of memory usable), the others llama.cpp. Several GPUs split the model with tensor or pipeline parallelism.
GPU
BF16 / FP16
8-bit
4-bit
GeForce RTX 4090 24 GB
96 (several servers)
48 (several servers)
24 (several servers)
GeForce RTX 5090 32 GB
64 (several servers)
32 (several servers)
16 (several servers)
RTX PRO 6000 Blackwell 96 GB
16 (several servers)
8
5
Mac M5 Max 128 GB 96 GB usable
16 (several servers)
8
5
NVIDIA H100 80 GB
24 (several servers)
16 (several servers)
7
NVIDIA H200 141 GB
16 (several servers)
6
4
NVIDIA B200 (HGX) 180 GB
16 (several servers)
5
3
8-bit: FP8 with vLLM (INT8 on GPUs without FP8), Q8_0 with llama.cpp. 4-bit: AWQ / GPTQ with vLLM, Q4_K_M with llama.cpp, both counted at the Q4_K_M size.
Cheapest way to run GLM-5.2 today
For each GPU: the fewest cards that fit GLM-5.2 with one 8K-token request, the most faithful weight format at that count, and the cheapest on-demand price (2026-10-11).
On-demand prices from RunPod, Vast.ai, Verda and Azure, checked every hour. Serving many users needs more KV cache, so more memory: size it in the calculator. Some links are affiliate links: StudioTV may earn a commission, at no extra cost to you.
KV cache: how memory grows with context
Architecture: 78 layers · MLA (512 + 64 latent) on 78 · indexer keys on 21. Its cache stays compact because MLA stores one compressed latent per token and layer instead of full keys and values. Only 42B parameters are active per token, but all 753B must sit in memory: VRAM depends on the total, speed on the active part.
Context per request
BF16 cache
FP8 cache
8K tokens
0.71 GiB
0.41 GiB
32K tokens
2.83 GiB
1.65 GiB
128K tokens
11.3 GiB
6.58 GiB
1M tokens
90.5 GiB
52.7 GiB
GLM-5.2 VRAM FAQ
How much VRAM does GLM-5.2 need?
In BF16 the weights alone take 1385 GiB (1487 GB). In FP8 that is 694 GiB, and about 418 GiB with 4-bit quantization (Q4_K_M). Each request then adds KV cache: 0.71 GiB at 8K tokens and 2.83 GiB at 32K (BF16 cache).
Can GLM-5.2 run on a single RTX 4090 (24 GB)?
No. Even in 4-bit it needs 24 (several servers) RTX 4090s. In 4-bit it needs 7 H100 GPUs.
What is the cheapest way to run GLM-5.2?
On 2026-10-11, the cheapest on-demand setup is 7 × NVIDIA A100 80GB with 4-bit weights on Vast.ai, at $6.54 per hour (about $4,772.74 per month). Next: 5 × RTX PRO 6000 Blackwell (96 GB) with Q4_K_M weights on Vast.ai, at $7.00 per hour. Sized for one 8K-token request; prices are checked every hour.
How many H100 GPUs does GLM-5.2 need?
With one request and an 8K-token context: 24 (several servers) in BF16, 16 (several servers) in FP8 and 7 in 4-bit (vLLM, 90% of memory usable). Serving many users at once needs more memory for their KV caches: the calculator sizes that for you.
GLM-5.2 VRAM badge
For a model card or a README: the 4-bit size and the smallest GPU it fits on, linked to this page. Data as JSON: /api/models/glm-5-2.json.
Estimates, not guarantees: computed from the official config.json with the same engine as the LLM VRAM Calculator. Real usage depends on your engine version and settings.