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How to Deploy GLM 5.3 Flash Locally

GLM 5.3 Flash is a MoE model with 320B total parameters and 18B active per token. At Q4 it needs 202GB of VRAM to sit entirely on the GPU; with experts offloaded to system RAM it needs only 30GB of VRAM plus 174GB of RAM. The cheapest card that runs it today is the Mac Studio M3 Ultra 512GB at about $22,000, at roughly 19.3 tokens/s. In offload mode the cheapest card that runs it is the Tesla V100 32GB, at roughly 8 tokens/s.

GLM 5.3 Flash details

Launch dateAug 25, 2026
Hugging Face repozai-org/GLM-5.3-Flash
Revision
LicenseMIT
ArchitectureMoE
Ollama tag
Min VRAM at Q4202 GB · Q4_K_M · 8K
Experts offloaded30 GB VRAM + 174 GB RAM
Parameters320B
MoE18B
Layers45
Hidden size4,096
KV heads64
Head dim256
Max context1M
VendorZ.ai / Zhipu AI

How much VRAM does GLM 5.3 Flash need for local deployment?

MoE: all 320B parameters must stay resident in memory, while speed is set by the 18B active per token.

QuantizationWeightsGBTotal at 8KGBTotal at 32KGBTotal at 128KGB
1-bit97.6GGUF98.6166.1436.1
2-bit108.7GGUF142.4209.9479.9
3-bit147.5GGUF176.8244.3514.3
4-bit199.7GGUF201.9269.4539.4
5-bit240.3GGUF239.4306.9576.9
8-bit341GGUF355.2422.7692.7
FP16641.6GGUF649.3716.8986.8

Total = weights + KV cache (FP16) + 1GB runtime overhead. Weights marked GGUF are measured file sizes from the repository; the rest are estimated from bytes per parameter. Contexts beyond this model's 1M limit show a dash. Each tier takes whichever file of that bit width the repository has, preferring unsloth dynamic quants, so the exact file name varies by model.

Which GPUs can run GLM 5.3 Flash?

eBay
DeviceVRAMGBPriceEst. t/sSuggested context
Mac Studio M3 Ultra 512GB512$22,00011 listings19.332K

Fits when weights + KV cache + runtime overhead is at or below usable memory, single card. "Offload" on MoE models means it runs with expert weights in system RAM (only attention layers and KV cache stay in VRAM); that speed assumes 70 GB/s RAM bandwidth. The context column is the largest tier that fits.

Recommended GPUs for GLM 5.3 Flash with CPU/GPU offloading

eBay

Expert weights live in system RAM (needs at least 174 GB); VRAM holds only attention layers, shared experts and the KV cache, at least 30 GB at Q4. Speed is bound by RAM bandwidth (estimated at 70 GB/s) and is far slower than a full-VRAM setup.

DeviceVRAMGBPriceRAM neededGBEst. t/s
Tesla V100 32GB32$7201747.7
Instinct MI50 32GB32$7681747.3
Instinct MI10032$9991747.6
Radeon PRO W790048$3,4951747
RTX PRO 4500 Blackwell32$4,3001747.6
A100 40GB PCIe40$4,8991748.4
Instinct MI21064$5,0461748
MacBook Pro M4 Max 128GB128$5,5491746.3
GeForce RTX 509032$6,5001748.5
Mac Studio M4 Max 128GB128$6,5471746.3
MacBook Pro M5 Max 128GB128$7,2501746.5
RTX PRO 6000 Blackwell96$16,9851748.5
Arc Pro B65321746.1
Arc Pro B70321746.1
GeForce RTX 4080 SUPER 32GB321747.3
GeForce RTX 5090 D321748.5
Radeon AI PRO R9700321746.4
Radeon PRO W7800321746.2
CMP 170HX 40GB401748.4
GeForce RTX 4090 48GB481747.8
RTX PRO 5000 Blackwell 48GB481748.2
CMP 170HX 64GB641748.3
RTX PRO 5000 Blackwell 72GB721748.2
RTX PRO 6000D841748.4
DGX Spark 128GB1281745.2
Mac Studio M5 Max 128GB1281746.5
Ryzen AI Max+ 395 128GB1281744.3

Deploy GLM 5.3 Flash locally with Ollama, llama.cpp or vLLM

Ollama

The Ollama library has no official tag for it yet.

llama.cpp

llama.cpp pulls the GGUF straight from Hugging Face; -c sets the context length.

llama-server -hf unsloth/GLM-5.3-Flash-GGUF:Q4_K_M -c 8192
vLLM

vLLM serves the original-precision weights, which needs far more memory than GGUF and usually more than one GPU.

vllm serve zai-org/GLM-5.3-Flash

File names follow that week's model-library snapshot; the definitions are in the methodology. Methodology

FAQ

How much VRAM does GLM 5.3 Flash need?

At Q4_K_M with an 8K context GLM 5.3 Flash needs about 202GB of VRAM; 244GB leaves comfortable headroom.

What is the cheapest GPU that runs GLM 5.3 Flash?

The Mac Studio M3 Ultra 512GB: 512GB of VRAM, currently about $22,000, at roughly 19.3 tokens/s.

Can you run GLM 5.3 Flash with Ollama?

Not yet: the Ollama library has no official tag for it. You can load the GGUF with llama.cpp instead.