What GPU do I need to run unsloth/GLM-5.3-Flash?
321.3B parameters, published in BF16. View on Hugging Face
Set up GLM-5.3-FlashGLM-5.3-Flash is published by unsloth on Hugging Face, with 1,190 downloads and 16 likes to date. It's a Glm5NextForConditionalGeneration model built for text-generation, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 598.5 GB | 718.2 GB | RTX PRO 6000 | 8 | $10.56/hr |
| FP8 (quantized) | 299.3 GB | 359.1 GB | RTX 4090 | 8 | $4.08/hr |
| INT4 (quantized) | 149.6 GB | 179.6 GB | RTX A5000 | 8 | $1.41/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run GLM-5.3-Flash at its published (BF16) precision: 8× RTX PRO 6000, at $1.32/hr per GPU ($10.56/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
GLM-5.3-Flash: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
KV-cache size for GLM-5.3-Flash is not published for this architecture, so no figure is shown.
GLM-5.3-Flash: common questions
Can GLM-5.3-Flash run on a single GPU?
No. At BF16 it needs 718.2 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 95.0 GB RTX PRO 6000, and it takes 8 of them.
How many GPUs do I need to run GLM-5.3-Flash?
8 at BF16. It needs 718.2 GB of VRAM and the cheapest capable live offer is a 95.0 GB RTX PRO 6000, so 8 of them come to $10.56/hr in total.
Does quantizing GLM-5.3-Flash lower the GPU bill?
Yes. At BF16 the cheapest live fit is 8 RTX PRO 6000 cards at $10.56/hr. At INT4 (quantized) it drops to 8 RTX A5000 cards at $1.41/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More GLM-5 models
- GLM-5.3-Flash (321.3B, F8_E4M3)
- GLM-5.3-Flash-BF16 (321.3B, BF16)
- GLM-5.3-Flash-FP8 (321.3B, F8_E4M3)
- GLM-5.3-Flash-Uncensored-FP8 (321.3B, F8_E4M3)
- GLM-5.3-Flash-UNCENSORED-FP8 (321.3B, F8_E4M3)
Lineage
Based on GLM-5.3-Flash-BF16.
Alternatives at this size
Other models for text-generation within about a third of GLM-5.3-Flash's 321.3B parameters, from other model lines.
- MiniMax-M2.7 (228.7B, F8_E4M3)
- MiMo-V2.5 (310.8B, F8_E4M3)
- Qwen3-235B-A22B (235.1B, BF16)
- Ornith-1.5-397B (403.4B, BF16)
- Llama-3.1-405B (405.9B, BF16)
More on GLM-5.3-Flash
Fits on a 192 GB GPU at INT4: every model that fits in 192 GB.
Related reading: RTX PRO 6000 pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, Best GPU for LLM inference, and What AI model fine-tuning is.
