GLM-5.2 vs NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
GLM-5.2 (753.3B parameters) and NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 (560.5B parameters) side by side: the memory each needs at every precision, what it costs to run on a live GPU, and the context window, KV cache and license where they are published. Numbers are computed from the models' published specs; this page does not rank quality.
Side by side
| Fact | GLM-5.2 | NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 |
|---|---|---|
| Parameters | 753.3B (Mixture-of-experts: 8 of 256 experts active per token (exact active-parameter count not stated on the model card)) | 560.5B (Mixture-of-experts: 22 of 512 experts active per token (~55B active parameters, per the model's own "A55B" name)) |
| Architecture | Multi-head latent attention; mixture of 256 experts, 8 active per token | mixture of 512 experts, 22 active per token |
| Context length | 1024K tokens (1,048,576) | 256K tokens (262,144) |
| License | MIT | NVIDIA OpenMDW-1.1 |
| Published precision | BF16 | BF16 |
| VRAM needed, As published | 1684 GB | 1253 GB |
| VRAM needed, FP8 | 842 GB | 626 GB |
| VRAM needed, INT4 | 421 GB | 313 GB |
| Cheapest live fit, As published | No live fit | No live fit |
| Cheapest live fit, FP8 | No live fit | RTX PRO 6000 × 7 · $9.63/hr |
| Cheapest live fit, INT4 | RTX PRO 6000 × 5 · $6.88/hr | RTX A6000 × 7 · $2.54/hr |
| KV cache per token (16-bit) | 88 KB | Not published for this architecture |
| KV cache at 32k tokens | 2.74 GB | Not published for this architecture |
| KV cache at 128k tokens | 11.0 GB | Not published for this architecture |
VRAM is the weight size at each precision times a flat 1.2 overhead; see the methodology. The FP8 and INT4 rows need a quantized checkpoint or an engine that quantizes on load. The fit is the lowest-priced single GPU type that holds the model at that precision, or the lowest-priced multi-GPU set (up to 8) when none does. KV cache is for one sequence at 16-bit, computed from each model's config where the attention layout is known.
Which to pick
- NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 needs less VRAM at its published precision (1253 GB against 1684 GB), so it fits on a smaller GPU.
- GLM-5.2 lists the longer context window (1,048,576 tokens against 262,144).
- Licenses differ: GLM-5.2 is under MIT, which our catalog notes as permissive; NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 is under NVIDIA OpenMDW-1.1, so read its terms before commercial use.
These follow only from the facts in the table above. Whether either model does your task well is a separate question this page does not answer.
Keep reading
- GLM-5.2: full VRAM table and live GPU fit
- NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16: full VRAM table and live GPU fit
- The GLM model series
- The Nemotron model series
Other comparisons
- GLM-5.2 vs DeepSeek-R1
- GLM-5.2 vs DeepSeek-V3.2
- GLM-5.2 vs DeepSeek-V3-0324
- GLM-5.2 vs DeepSeek-V3
- NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 vs DeepSeek-R1
- NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 vs DeepSeek-V3.2
- NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 vs DeepSeek-V3-0324
- NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 vs DeepSeek-V3
