LLM Inference Benchmark Explorer
GLM-5.3-Flash on 4× DGX Spark — FP8, SGLang, TP4, speculative decoding k=7 inference benchmark
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| Model | GLM-5.3-Flash |
|---|---|
| Parameters | 321B |
| Intelligence Index | 41.8 |
| Agentic Index | 50.9 |
| Device | 4× DGX Spark |
| Quantization | FP8 |
| Max C | 2 |
| Chat Capacity | 8 |
| Agentic Capacity | 3 |
| TP | 4 |
| DP | — |
| PP | — |
| Engine | SGLang |
| Speculative Decoding | Yes |
| C | TTFT (ms) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 332 | 41.99 | PASS |
| 2 | 427 | 26.55 | PASS |
| 4 | 570 | 17.69 | FAIL |
| 8 | 1216 | 12.45 | FAIL |
In OpenZeka's measurement, GLM-5.3-Flash (321B parameters), served in FP8 format with SGLang and speculative decoding on 4× DGX Spark (TP=4), reached a generation speed of 42 tok/s per request and a time to first token (TTFT) of 332 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 8 users for chat use and 3 for agentic use. The realistic capacity will likely fall between these two values. In scenarios dominated by coding, tool use, long workflows and multi-agent use, capacity approaches the agentic estimate; where shorter interactions, standard conversations and lighter tasks dominate, it approaches the chat estimate.
Notes: DFlash speculative decoding k=7, 256K context (max_model_len 262144), SGLang. 1.3-1.4x the TPS of the vLLM run without speculative decoding at C1-C4. Model: zai-org/GLM-5.3-Flash (served locally as glm-5.3-flash)
Intelligence Index and Agentic Index values are published by Artificial Analysis and are reproduced here with attribution.
In OpenZeka's measurement, GLM-5.3-Flash (321B parameters), served in FP8 format with SGLang and speculative decoding on 4× DGX Spark (TP=4), reached a generation speed of 42 tok/s per request and a time to first token (TTFT) of 332 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 8 users for chat use and 3 for agentic use. The realistic capacity will likely fall between these two values. In scenarios dominated by coding, tool use, long workflows and multi-agent use, capacity approaches the agentic estimate; where shorter interactions, standard conversations and lighter tasks dominate, it approaches the chat estimate.