GLM 5.3 744BNVIDIA Hopper
Running GLM-5.2 on H200
Quick answer
GLM-5.2 runs on H200: 3 benchmarked configs so far. See the interactivity ladder below for measured operating points.
Benchmarked configs
3
Serving engines
Dynamo SGLang
Precisions
fp8
Run dates
2026-08-25 → 2026-08-25
Throughput at every interactivity target
Serving is a trade-off: push more concurrent users through a GPU and each user's tokens arrive slower. The ladder below reads the measured frontier at each per-user speed target on the AgentX agentic coding workload, using the best engine and precision at that point.
| Per-user target | Tokens/s per GPU | $ / 1M tokens | Engine | Precision |
|---|---|---|---|---|
| 30 tok/s | - | - | Dynamo SGLang | fp8 |
| 50 tok/s | - | - | Dynamo SGLang | fp8 |
| 75 tok/s | - | - | Dynamo SGLang | fp8 |
| 100 tok/s | - | - | Dynamo SGLang | fp8 |
| 150 tok/s | - | - | Dynamo SGLang | fp8 |
| 200 tok/s | - | - | Dynamo SGLang | fp8 |
Frequently asked questions
- How fast is GLM-5.2 on H200?
- The InferenceX fleet has 3 benchmarked configs for this pairing; see the interactivity ladder above for the operating points reached so far.
- How much does it cost to serve GLM-5.2 on H200?
- Cost per million tokens is derived from measured throughput and $/GPU/hr rates from the SemiAnalysis AI Cloud TCO model; it appears once this pairing reaches the primary interactivity tier.
- Which serving engines run GLM-5.2 on H200?
- The runs behind this page used Dynamo SGLang in FP8, including disaggregated prefill and multi-node serving. Engines are rebuilt and re-benchmarked continuously, so the best config can change between visits.
- How are these GLM-5.2 numbers measured?
- Every number is measured on real H200 hardware by the InferenceX fleet, sweeping concurrency on the AgentX agentic coding workload to trace the throughput-versus-interactivity frontier; the newest run landed on 2026-08-25. The same derivation powers the InferenceX overview leaderboard.