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Artificial AnalysisSource publication:

Vera Rubin NVL72's first agentic-serving results: up to about 5.7x GB300's throughput per megawatt

Synopsis

AA-AgentPerf has published the first serving results for NVIDIA's Vera Rubin NVL72 on real coding-agent trajectories: on DeepSeek V4 Pro its total throughput per megawatt reaches up to about 5.7x the previous-generation GB300 NVL72, and about 5.4x, 3.0x and 2.8x on DeepSeek V4.1 Flash, GLM-5.2 and Kimi K3. Those multiples appear where GB300's throughput falls away as per-user speed rises, while at peak throughput the lead is only 1.14x to 1.45x. On average 97% of a request's input tokens repeat the previous request, making prefix caching and KV cache across memory tiers central.

Source-provided article image: First AA-AgentPerf results for NVIDIA Vera Rubin NVL72
Artificial Analysis

Interpretation

NVIDIA's rack-scale Vera Rubin NVL72 delivers higher total throughput per megawatt of measured GPU power than the previous-generation GB300 NVL72 on coding-agent workloads, and the lead widens as per-user output speed rises. AA-AgentPerf previously had no Vera Rubin results, with GB300 NVL72 as the prior-generation comparison system on this workload. On DeepSeek V4 Pro, Vera Rubin serves 132M tokens/s per MW at about 190 tok/s per user versus about 23M for GB300, roughly 5.7x; the lead is about 5.4x on DeepSeek V4.1 Flash at about 580 tok/s, about 3.0x on GLM-5.2 at about 165 tok/s and about 2.8x on Kimi K3 at about 67 tok/s.

The serving stack keeps each agent session's repeated context in cache, prefilling only the tokens each turn adds, and routes requests by session to the worker already holding that cache. A coding agent resends the whole conversation each turn, so context grows as it reads files, runs tests and makes edits, while replies are often just short tool calls. AA-AgentPerf replays real coding-agent trajectories with up to 132K input tokens per request and 97% of input tokens repeated on average; most Vera Rubin configs offload KV cache to CPU memory over NVLink-C2C, and the DeepSeek V4 Pro prefill workers have 120 to 200 GiB of host memory configured for KV cache.

Perspective

The results cover serving configs for four models on the AA-AgentPerf replay workload, with throughput counted against measured GPU power and excluding CPUs, networking and cooling; per-user speed is the 25th percentile across requests and excludes time to first token.

These are first results on Vera Rubin with early Rubin-specific kernels and tuning, and NVIDIA expects performance to keep improving as further optimizations land; on DeepSeek V4.1 Flash, Vera Rubin's replies were capped at 2,048 output tokens, about 2% of replies reached the cap and the average reply was about a tenth shorter, modestly favoring its throughput; measured against end-to-end speed including time to first token, GB300 matches or beats Vera Rubin's throughput per MW at most speeds on DeepSeek V4 Pro, while on GLM-5.2 Vera Rubin still leads by 1.1x to 1.8x.