What GPU do I need to run zai-org/GLM-5.3-Flash-BF16?
321.3B parameters, published in BF16. View on Hugging Face
Set up GLM-5.3-Flash-BF16GLM-5.3-Flash-BF16 is published by zai-org on Hugging Face, with 9,183 downloads and 55 likes to date. It's a Glm5NextForConditionalGeneration model built for image-text-to-text, 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 | AMD MI300X | 4 | $10.52/hr |
| FP8 (quantized) | 299.3 GB | 359.1 GB | RTX 4090 | 8 | $5.18/hr |
| INT4 (quantized) | 149.6 GB | 179.6 GB | AMD MI300X | 1 | $2.63/hr |
| cheaper alt. | 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-BF16 at its published (BF16) precision: 4× AMD MI300X, at $2.63/hr per GPU ($10.52/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-BF16: 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-BF16 is not published for this architecture, so no figure is shown.
GLM-5.3-Flash-BF16: common questions
Can GLM-5.3-Flash-BF16 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 192.0 GB AMD MI300X, and it takes 4 of them.
How many GPUs do I need to run GLM-5.3-Flash-BF16?
4 at BF16. It needs 718.2 GB of VRAM and the cheapest capable live offer is a 192.0 GB AMD MI300X, so 4 of them come to $10.52/hr in total.
Does quantizing GLM-5.3-Flash-BF16 lower the GPU bill?
Yes. At BF16 the cheapest live fit is 4 AMD MI300X cards at $10.52/hr. At INT4 (quantized) it drops to one AMD MI300X at $2.63/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-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)
- GLM-5.3-Flash (321.3B, BF16)
Lineage
Quantized and fine-tuned versions
Alternatives at this size
Other models for image-text-to-text within about a third of GLM-5.3-Flash-BF16's 321.3B parameters, from other model lines.
- Qwen3-VL-235B-A22B-Instruct (235.7B, BF16)
- MiniMax-M3 (427.0B, BF16)
- Inkling-Small (266.0B, BF16)
- step3 (321.0B, BF16)
- Intern-S1 (240.7B, BF16)
More on GLM-5.3-Flash-BF16
Fits on a 192 GB GPU at INT4: every model that fits in 192 GB.
Best vision-language models: how GLM-5.3-Flash-BF16 ranks against the rest.
Related reading: AMD MI300X pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
