GLM‑5.3 runs at 607.8 tok/s across two Nvidia DGX stations with vLLM
nvidia
| Source: HN | Original article
GLM-5.3 is being served at 607.8 tokens per second using vLLM across two Nvidia DGX stations.
A team of engineers has demonstrated that the Chinese‑origin large language model GLM‑5.3 can be served at a sustained rate of 607.8 tokens per second by distributing the workload across two Nvidia DGX stations running the open‑source inference engine vLLM. The result, posted in a technical brief, shows that a pair of high‑end DGX servers can deliver sub‑second latency for a model of GLM‑5.3’s size when orchestrated with vLLM’s dynamic batching and speculative decoding features.
The achievement matters because inference speed is a key bottleneck for deploying LLM‑driven applications at scale. By reaching more than 600 tok/s on commodity DGX hardware, the experiment suggests that enterprises can achieve production‑grade throughput without resorting to custom ASICs or massive server farms. It also validates vLLM’s claim to improve utilization on Nvidia GPUs, reinforcing the growing ecosystem of software that extracts maximum performance from the same hardware that fuels the AI boom.
Observers will be watching whether the same configuration can sustain higher token rates as model sizes continue to grow, and whether other vendors can replicate the result on alternative GPU platforms. The next steps are likely to involve broader benchmarking against competing inference stacks, testing on larger clusters, and measuring cost per token in real‑world workloads. If the performance holds up, the combination of GLM‑5.3, Nvidia DGX, and vLLM could become a reference point for firms in the Nordics and beyond that need fast, reliable LLM serving without excessive capital outlay.
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