{"resourceId":"nvidia-agentx-rubin-30x-per-mw-20260824","versions":[{"version":"external-5645d0871c9dce3d28dc57914eaadae9d9316521e3b8b49b8f220bba807695a8","resource":{"id":"nvidia-agentx-rubin-30x-per-mw-20260824","title":"NVIDIA technical blog: Vera Rubin up to 30× AgentX throughput per MW vs GB300","organization":"NVIDIA","sector":"AI factory benchmarking / agentic inference","geography":"Global (vendor technical blog)","publishedAt":"August 24, 2026","publicationDate":"2026-08-24","eventDate":"2026-08-24","sourceName":"NVIDIA Technical Blog","sourceLabel":"Vendor technical claim","sourceUrl":"https://developer.nvidia.com/blog/nvidia-vera-rubin-and-blackwell-set-a-new-standard-for-agentic-ai-performance-per-watt/","evidenceClass":"vendor-claim","outcomeClass":"emerging","topics":["developers-agents","infrastructure","operating-model"],"finding":"NVIDIA’s Aug 24, 2026 technical blog reports SemiAnalysis AgentX results showing Vera Rubin NVL72 delivering up to 30× higher AI-factory throughput per megawatt than GB300 NVL72 at 160 tokens per second per user on an AgentX DeepSeek V4-Pro workload, and cites GB300 advantages versus H200 on other AgentX workloads. It describes AgentX as replaying prerecorded production-style coding sessions with interleaved reasoning and tool use, and points to system software (SGLang/TensorRT-LLM/vLLM, Dynamo, precision formats) as co-design factors. Newly relevant as Sep multi-rack and DSX narratives continue to lean on agentic efficiency claims.","sledRelevance":"Provides the primary vendor statement of the 30× AgentX per-MW figure SLED teams will see in decks—must be read as coding-agentic, point-specific, vendor-reported preview results, not general SLED chatbot SLOs.","evidence":"Publish Aug 24, 2026; up to 30× Rubin vs GB300 at 160 TPS/user on AgentX DeepSeek V4-Pro; AgentX uses prerecorded production-style sessions; GB300 vs H200 up to 15× / 80× cited for other models; stack mentions SGLang, TRT-LLM, vLLM, Dynamo, MXFP4/8.","architectureImplications":"Agentic efficiency depends on MoE serving runtimes, KV-cache behavior, and power-normalized factory metrics—not peak chip TOPS alone.","governanceImplications":"Label as vendor-reported AgentX preview; require matching interactivity point and workload class before using in business cases.","securityPrivacyImplications":"Benchmark uses anonymized coding sessions per SemiAnalysis design; source does not address tenant isolation.","caveats":"Vendor blog summarizing AgentX; extreme ratios are operating-point sensitive; not a campus productivity study.","streamIds":["nvidia"],"roles":{"sales":"If using the 30× figure, open with AgentX’s coding-session scope and the 160 TPS/user point, then ask what interactivity and tool-use mix the buyer actually runs. Pair with independent caveats elsewhere in this edition—never present 30× as a general tokens/MW guarantee for SLED knowledge work.","engineering":"Fit as a benchmark interpretation aid for agentic serving stacks on Rubin/Blackwell. Reproduce only with pinned engines and power metering. Proof of value is a buyer-relevant replay or production trace—not the blog chart alone.","delivery":"Keep AgentX numbers out of acceptance criteria unless the workload is coding-agentic and the point is matched. Risks: wrong SLO class; power-normalization disputes; preview software drift."},"roleDetails":{"sales":{"customerProblem":"Buyers see headline 30× agentic efficiency claims without workload context.","stakeholders":["AI platform","CFO","engineering"],"discoveryQuestions":["Is your workload coding-agentic with tool use?","What TPS/user target matters?","Will you accept AgentX or need domain traces?"],"valueHypothesis":"Point-specific AgentX gains may inform architecture choice if scoped tightly.","potentialEngagement":"Scoped AgentX-like replay on one domain with power metering.","unsupportedClaims":["30× for all SLED chatbots","Guaranteed factory TCO","Preview equals GA software"]},"engineering":{"fit":"Relevant for agentic coding inference evaluations on NVL72.","architectureIntegration":"Pin serving runtime, precision, Dynamo/KV settings, and power domains.","prerequisites":["power metering","engine pins","trace ethics approval"],"constraints":["Vendor-reported","160 TPS/user point","coding-session corpus"],"security":"Control access to traces and evaluation clusters.","proofOfValue":["Matched-point replay","Power-normalized metrics logged","Sensitivity sweep"]},"delivery":{"work":"Run scoped evaluation; document point and caveats in decision package.","dependencies":["benchmark lead","facilities metering","vendor SE"],"ownership":"AI platform evaluation owner.","skillsAdoption":"Teams need agentic benchmark literacy.","governanceCheckpoints":["No 30× in unmarked slides","Workload class approved","Preview software labeled"],"acceptanceCriteria":["Config pin filed","Metrics reproducible","Caveats in memo"],"risks":["SLO mismatch","Software drift","Overfit to AgentX coding mix"]}},"retrievedAt":"2026-09-22T03:12:35Z","enrichedAt":"2026-09-22T03:21:46Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Agentic coding efficiency may affect developer tooling capacity; no accessibility features claimed.","procurementImplications":"Require workload-class and point disclosure whenever AgentX ratios appear in proposals.","operatingModelImplications":"Factory KPIs may shift toward power-normalized agentic throughput—only if workloads match.","sourceVerification":{"openedUrl":"https://developer.nvidia.com/blog/nvidia-vera-rubin-and-blackwell-set-a-new-standard-for-agentic-ai-performance-per-watt/","referenceExcerpt":"NVIDIA Vera Rubin NVL72 delivers up to 30x higher AI-factory throughput per megawatt than GB300 NVL72","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}