DeepSeek-R1 vs GLM-5.2
DeepSeek-R1 (684.5B parameters) and GLM-5.2 (753.3B 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 | DeepSeek-R1 | GLM-5.2 |
|---|---|---|
| Parameters | 684.5B | 753.3B (Mixture-of-experts: 8 of 256 experts active per token (exact active-parameter count not stated on the model card)) |
| Architecture | Multi-head latent attention; mixture of 256 experts, 8 active per token | Multi-head latent attention; mixture of 256 experts, 8 active per token |
| Context length | 163,840 tokens | 1024K tokens (1,048,576) |
| License | – | MIT |
| Published precision | F8_E4M3 | BF16 |
| VRAM needed, As published | 765 GB | 1684 GB |
| VRAM needed, FP8 | Not a smaller option | 842 GB |
| VRAM needed, INT4 | 383 GB | 421 GB |
| Cheapest live fit, As published | RTX PRO 6000 × 8 · $11.75/hr | No live fit |
| Cheapest live fit, FP8 | – | No live fit |
| Cheapest live fit, INT4 | RTX A6000 × 8 · $2.90/hr | RTX PRO 6000 × 5 · $6.88/hr |
| KV cache per token (16-bit) | 69 KB | 88 KB |
| KV cache at 32k tokens | 2.14 GB | 2.74 GB |
| KV cache at 128k tokens | 8.58 GB | 11.0 GB |
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
- DeepSeek-R1 needs less VRAM at its published precision (765 GB against 1684 GB), so it fits on a smaller GPU.
- GLM-5.2 lists the longer context window (1,048,576 tokens against 163,840).
- DeepSeek-R1 caches less per sequence at 32k tokens (2.1 GB against 2.7 GB), leaving more memory for batching.
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
- DeepSeek-R1: full VRAM table and live GPU fit
- GLM-5.2: full VRAM table and live GPU fit
- The DeepSeek model series
- The GLM model series
Other comparisons
