RigPerDollar

What GPU do you need to run GLM-5.3-Flash?

No single GPU. GLM-5.3-Flash needs about 201.2 GB at UD-Q4_K_XL, more than any GPU, system or build we price.

Parameters321.32B18B active (mixture-of-experts)
UD-Q4_K_XL GGUF199.71 GBsource
VRAM needed~201.2 GBfile + headroom
Single GPUs the weights fit0context unknown
ReleasedAug 25, 2026
LicenseMIT
Context window1M tokens

321.32B parameters, 18B active (mixture-of-experts) (Hugging Face, accessed 2026-10-16). UD-Q4_K_XL GGUF: 199.71 GB (source). No context estimate: config reports head_dim 0 and qk_rope_head_dim 0 for its sparse-attention layers; KV size cannot be derived (details).

No plain Q4_K_M GGUF found; size is unsloth UD-Q4_K_XL.

Other places to get it

Checked Nov 14, 2026. We list only copies we opened and checked. Check file hashes for copies from other uploaders.

Weights-only fits, context unknown. We have no KV cache data for GLM-5.3-Flash, so every fit on this page only checks that the UD-Q4_K_XL file and headroom fit. How much context fits beside the weights is unknown, so none of these is a proven fit for any context length.

No single GPU in our database fits it. Use multiple GPUs (see calculator).

Every device that runs it

Graphics cards, Macs and mini PCs from the wider hardware list behind My rig, smallest usable memory first. Not sure about yours? Check your exact rig.

Show all 1 devices that run it
DeviceTypeMemorySpeed estimateMax context estimatePrice
Apple M5 Ultra 512GBMac512 GB (~384 usable)~64 tok/sUnknownNo price yet

Speed calculator

estimate

Estimate = bandwidth ÷ GB read per token (the model's listed GGUF file: Q4_K_M for most models, otherwise the quant named in the list; scaled by active/total parameters for MoE), scaled by a per-platform efficiency and fixed per-token costs calibrated to 45 measured llama.cpp benchmarks (2025–26). Fit = file size + 1.5 GB headroom ≤ total VRAM (on Macs, the part of unified memory macOS lets the GPU use by default), and room for at least 4K tokens of context with an 8-bit KV cache. For a model with no KV cache data, only the file and headroom are checked, so a fit says nothing about context. Multi-GPU: VRAM adds, speed stays one card's (a layer split runs the cards in turn); max context uses the total. Speed is for short context. Max context = what is left after the file and a 1 GB runtime reserve ÷ KV cache bytes per token, default FP16 and 8-bit (q8_0) cache. How speed works · how context works