Z.ai · Chat and reasoning · 753B parameters · 40B active · mit
GLM-5.2
Needs 995 GB at FP8 / INT8. The smallest real configuration today is 4× MI325X at Cyfuture AI, $12.29 per hour, about $8,972 a month at 730 hours.
Parameters753B40B active per token
Context1M tokensmaximum published
Checkpoint1,507 GBBF16, as published
Licensemitcheck before commercial use
ReleasedJun 16, 2026documented date
Smallest configuration · FP8 / INT8
4× MI325X1024 GB VRAM · 97% used
- Per hour
- $12.29
- Per month
- $8,972
- Cheapest at
Cyfuture AI
How much VRAM it needs
Weights plus runtime overhead, then the KV cache that grows with the context you serve.
| Precision | Weights | Weights + headroom | 4k context | 32k context | 128k context | 256k context | 1M context |
|---|---|---|---|---|---|---|---|
| BF16published checkpoint | 1,507 GB | 1,808 GB | 1,809 GB | 1,811 GB | 1,820 GB | 1,832 GB | 1,903 GB |
| FP8 / INT8published checkpoint | 829 GB | 995 GB | 995 GB | 998 GB | 1,007 GB | 1,018 GB | 1,089 GB |
| INT4 / MXFP4published checkpoint | 482 GB | 579 GB | 579 GB | 582 GB | 591 GB | 603 GB | 673 GB |
| KV cacheone sequence, FP16 cache, included above | — | — | 1 GB | 3 GB | 12 GB | 24 GB | 95 GB |
Cross-checked on Hugging Face Oct 28, 2026: 753.3B parameters in the safetensors headers, 1506.7 GB of weight files, attention layout from config.json (78 layers, latent rank 512). Method: weights as published at the native precision, verified on Hugging Face 2026-10-28; other precisions estimated at 1 (8-bit) and 0.6 (4-bit) bytes per parameter; × 1.2 runtime headroom; KV cache = 1152 bytes per token per layer × 78 full-attention layers (FP16 cache, one sequence).config.json ↗
What it costs to run, GPU by GPU
The fewest GPUs of one type whose total memory fits the model at short context, priced from the cheapest eligible on-demand offer for exactly that count (45-day window, newest reading per provider), with the next providers beside it. Monthly = 730 hours.
| GPU | Smallest config | VRAM used | $ / hour | $ / month | Cheapest at |
|---|---|---|---|---|---|
| MI325XBest fit | 4× 256 GB1024 GB total | 97% tight | $12.29 | $8,972 | |
| MI300X | 8× 192 GB1536 GB total | 65% | $13.68 | $9,986 | |
| H200 | 8× 141 GB1128 GB total | 88% | $21.04 | $15,359 | |
| B300 | 4× 288 GB1152 GB total | 86% | $23.29 | $17,004 | |
| MI355X | 8× 288 GB2304 GB total | 43% | $23.60 | $17,228 | |
| H200 NVL | 8× 141 GB1128 GB total | 88% | $26.09 | $19,042 | |
| B200 | 8× 180 GB1440 GB total | 69% | $32.91 | $24,026 | |
| B300 CC | 4× 288 GB1152 GB total | 86% | $46.31 | $33,806 | |
| GB300 | 4× 288 GB1152 GB total | 86% | $52.21 | $38,113 | |
| B200 CC | 8× 180 GB1440 GB total | 69% | $72.37 | $52,830 |
Tokens per second, and what a million costs
Chat and reasoning. Speed estimated from memory bandwidth and model size, never measured. The cost per 1M output tokens is an estimate at the prices read on Oct 4, 2026; it is not recomputed here.
| GPU | Cards | Precision | tok/s per user | per 1M output tokens · estimate |
|---|---|---|---|---|
| MI300X | 8 | FP8 | 26.7 | $4.63 |
| H200 | 8 | FP8 | 34.6 | $5.63 |
| B200 | 8 | FP8 | 46.5 | $6.15 |
cards, precision and speed estimated from memory bandwidth and model size; cost recomputed by the site from verified prices · cheapest per output at the prices of Oct 4, 2026: MI300XWhy estimates, and their limits →
