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GLM-4.5-Air VRAM Requirements

Z.ai, China · Released July 20, 2025

GLM-4.5-Air has 110.5B parameters (13B active per token). In BF16 its weights alone take 199 GiB; quantized to 4-bit, about 64.7 GiB. Each 32K-token request adds 5.75 GiB of KV cache. Cheapest way to run it today: 5 × RTX A4000 (16 GB) at $0.460 per hour.

Size your own setup in the calculator →

VRAM by precision and context length

Weights plus the KV cache of one request (BF16 cache), in GiB. Add 1 to 3 GiB for the inference engine itself.

Weights formatWeights only+ 8K context+ 32K context+ 128K context
BF16 / FP16199200205222
FP8 / INT8101102106124
4-bit (GGUF Q4_K_M)64.766.170.487.7

GLM-4.5-Air GGUF files

Exact size of each quantization in unsloth/GLM-4.5-Air-GGUF (opens in a new tab), and the smallest GPU that runs it with llama.cpp, Ollama or LM Studio and an 8K-token context.

QuantizationFile sizeSmallest GPU
BF16206 GiBMore than 96 GB: see the calculator
UD-Q8_K_XL119 GiBMore than 96 GB: see the calculator
Q8_0109 GiBMore than 96 GB: see the calculator
UD-Q6_K_XL94.6 GiBMore than 96 GB: see the calculator
Q6_K92.2 GiBMore than 96 GB: see the calculator
Q5_K_M77.8 GiBRTX PRO 6000 Blackwell (96 GB)
UD-Q5_K_XL77.2 GiBRTX PRO 6000 Blackwell (96 GB)
Q5_K_S73.0 GiBRTX PRO 6000 Blackwell (96 GB)
Q4_K_M68.0 GiBRTX PRO 6000 Blackwell (96 GB)
Q4_164.6 GiBRTX PRO 6000 Blackwell (96 GB)
UD-Q4_K_XL63.1 GiBRTX PRO 6000 Blackwell (96 GB)
Q4_K_S62.4 GiBRTX PRO 6000 Blackwell (96 GB)
Q4_058.5 GiBRTX PRO 6000 Blackwell (96 GB)
IQ4_NL58.4 GiBRTX PRO 6000 Blackwell (96 GB)
IQ4_XS56.3 GiBRTX PRO 6000 Blackwell (96 GB)
Q3_K_M53.3 GiBRTX PRO 6000 Blackwell (96 GB)
UD-Q3_K_XL51.0 GiBRTX PRO 6000 Blackwell (96 GB)
Q3_K_S49.0 GiBRTX PRO 6000 Blackwell (96 GB)
UD-IQ3_XXS47.9 GiBRTX PRO 6000 Blackwell (96 GB)
UD-Q2_K_XL44.2 GiBRTX PRO 6000 Blackwell (96 GB)
Q2_K_L42.3 GiBRTX 6000 Ada (48 GB)
Q2_K42.2 GiBRTX 6000 Ada (48 GB)
UD-IQ2_M41.3 GiBRTX 6000 Ada (48 GB)
UD-IQ2_XXS39.8 GiBRTX 6000 Ada (48 GB)
UD-IQ1_M37.3 GiBRTX 6000 Ada (48 GB)
UD-IQ1_S35.9 GiBRTX 6000 Ada (48 GB)
UD-TQ1_035.6 GiBRTX 6000 Ada (48 GB)

Read every day from Hugging Face. Split files are added together; vision projectors are left out.

How many GPUs to run GLM-4.5-Air

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.

GPUBF16 / FP168-bit4-bit
GeForce RTX 4090 24 GB16 (several servers)54
GeForce RTX 5090 32 GB843
RTX PRO 6000 Blackwell 96 GB321
Mac M5 Max 128 GB 96 GB usable321
NVIDIA H100 80 GB321
NVIDIA H200 141 GB211
NVIDIA B200 (HGX) 180 GB211

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-4.5-Air today

For each GPU: the fewest cards that fit GLM-4.5-Air with one 8K-token request, the most faithful weight format at that count, and the cheapest on-demand price (2026-10-14).

On-demand prices from RunPod, Vast.ai, Azure, Oracle Cloud, OVHcloud, Scaleway, Verda and Akash, 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: 46 layers · 46 full attention (8 KV × 128). Every layer keeps keys and values for every token (grouped-query attention), so the cache grows linearly with context. Only 13B parameters are active per token, but all 110B must sit in memory: VRAM depends on the total, speed on the active part.

Context per requestBF16 cacheFP8 cache
8K tokens1.44 GiB0.72 GiB
32K tokens5.75 GiB2.88 GiB
128K tokens23.0 GiB11.5 GiB

GLM-4.5-Air VRAM FAQ

How much VRAM does GLM-4.5-Air need?

In BF16 the weights alone take 199 GiB (214 GB). In FP8 that is 101 GiB, and about 64.7 GiB with 4-bit quantization (Q4_K_M). Each request then adds KV cache: 1.44 GiB at 8K tokens and 5.75 GiB at 32K (BF16 cache).

Can GLM-4.5-Air run on a single RTX 4090 (24 GB)?

No. Even in 4-bit it needs 4 RTX 4090s. In 4-bit it fits on a single RTX PRO 6000 Blackwell (96 GB).

What is the cheapest way to run GLM-4.5-Air?

On 2026-10-14, the cheapest on-demand setup is 5 × RTX A4000 (16 GB) with Q4_K_M weights on Vast.ai, at $0.460 per hour (about $335.80 per month). Next: 4 × GeForce RTX 3090 (24 GB) with Q4_K_M weights on Vast.ai, at $0.596 per hour. Sized for one 8K-token request; prices are checked every hour.

How many H100 GPUs does GLM-4.5-Air need?

With one request and an 8K-token context: 3 in BF16, 2 in FP8 and 1 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-4.5-Air 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-4-5-air.json.

GLM-4.5-Air VRAM badge

[![GLM-4.5-Air VRAM](https://studiotvai.com/badge/glm-4-5-air.svg)](https://studiotvai.com/vram-requirements/glm-4-5-air)

Other models

Same family (mixture of experts): Qwen3 30B-A3B · Nemotron 3.5 Lightning 30B-A3B

All models: Llama 3.1 8B · Mistral Small 3.2 24B · Qwen3 32B · Llama 3.3 70B · Llama 3.1 405B · Qwen3.5 4B · Qwen3.5 9B · Qwen3.8 27B · Qwen3.6 35B-A3B · Qwen3.5 122B-A10B · Qwen3.8-Flash-Next · LFM2.5 8B-A1B · LFM2 24B-A2B · Gemma 4 12B · Gemma 4 31B · Gemma 4 26B-A4B · gpt-oss 20B · gpt-oss 120B · Llama 4 Scout 17B-16E · Llama 4 Maverick 17B-128E · Kolibri-1 · Nemotron 3 Nano 30B-A3B · Nemotron 3 Super 120B-A12B · MiniMax M2.7 · DeepSeek R1 · Kimi K2.6 · GLM-5.3 · GLM-5.3-Flash · DeepSeek V4 Flash · DeepSeek V4 Pro · DeepSeek V4.1 Flash · MiMo-V2.6 Pro · MiMo-V2.6 Flash · Kimi K3 · Mistral Large 4 · Qwen3-Coder-Next 80B-A3B · Qwen3-Coder 30B-A3B · Qwen3-Coder 480B-A35B · DeepSeek V4 Flash Vision Exp · Qwen3.8 2.4T-A95B · Ornith 1.5 397B · Ornith 1.5 35B-A3B · Ornith 1.5 9B · Atria Dawn Preview · Intern S2 397B · Clef · Clef Flash · D1 3B · Humanizer · JEV 27B VL · Mellum2.1 12B-A2.5B · LightOnOCR-3 4B · Spark-X2.5 4B · Agnes-3.0-Qwen · Youtu-Parsing-Omni · Agnes-3.0-Flash · Mistral 7B v0.3 · Mistral Small 3.1 24B · Mistral Nemo · Ministral 8B · DeepSeek V3 · DeepSeek R1 Qwen3 8B · DeepSeek R1 Distill Qwen 32B · DeepSeek V3.1 · DeepSeek R1 Distill Qwen 14B · DeepSeek R1 Distill Qwen 7B · DeepSeek R1 Distill Llama 8B · Qwen3 4B · Qwen3 8B · Qwen2.5 7B · Qwen3 14B · Qwen2.5 3B · Qwen3.6 27B · Qwen3.5 27B · Qwen2.5-Coder 7B · Qwen2.5 32B · Qwen2.5-Coder 14B · Qwen3.5 35B-A3B · Qwen3 30B-A3B · Qwen2.5 14B · Qwen2.5-Coder 32B · Kimi K2.7 Code · Kimi K2.5 · Kimi K2 · GLM-5.2 · GLM-5.1 · GLM-5 · MiniMax M2.5 · MiniMax M2 · MiMo-V2.5 · Ornith-1.0 397B · Qwen2.5-Coder 3B · Qwen3 235B-A22B · Qwen3.5 397B-A17B · Gemma 4 E4B · Gemma 4 E2B · Nemotron 3 Nano 4B · Nemotron 3.5 Lightning 30B-A3B · MiniCPM-V-4.7 35B-A3B · LFM2.5 24B-A2B · GLM-4.7 · Qwen3-VL 8B · Qwen2.5-VL 7B · Qwen3-VL 4B · Qwen2.5-VL 3B · Qwen2.5-VL 32B · Qwen2-VL 7B · GLM-4.5 · phi-4 · Phi-3.5-mini · Phi-4-mini · Phi-3-mini-128k

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.