From the NVIDIA edition of September 15, 2026
NVIDIA production note: AI-Q/LangGraph agent scaling needed NIM CPU fix and phased rollout
NVIDIA · AI platform vendor engineering blog · Global (internal OpenShift AI factory deployment)
- Publisher
- NVIDIA Technical Blog
- Original publication
- August 27, 2025
- Source retrieved
- 2026-09-15
What happened
NVIDIA engineers describe scaling an internal LangGraph deep-research agent (AI-Q blueprint) on OpenShift with local NIM: single-user profiling found Llama Nemotron Super 49B calls as the bottleneck; load tests at 10–50 concurrency informed hardware forecasts; a Helm misconfiguration starved a NIM of CPU at 100%; LLM timeouts required retries; phased rollout used NeMo Agent Toolkit OTEL + Datadog.
Why it matters
Provides a transferable operating pattern for SLED agent platforms—profile, load-test, fix config, phase rollout—while remaining an internal vendor deployment without public SLOs.
Evidence and measured results
Opened blog (dated Aug 27, 2025) details three-step process with NeMo Agent Toolkit profiler/sizing calculator, explicit CPU starvation finding, timeout handling snippet, and OTEL/Datadog monitoring during phased user expansion.
Limitations and uncertainty
Internal NVIDIA deployment; example sizing charts noted as toolkit sample not the actual agent data; dated Aug 2025 but newly relevant versus prior nvidia editions that did not cover this agent-scaling path.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-09-15; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Sponsors fear agent demos that collapse under departmental concurrent use.
- Stakeholders
- CIO
- app owner
- platform SRE
- Discovery questions
- What concurrency is year-one?
- Which model is the bottleneck?
- What observability stack exists?
- Value hypothesis
- A measured scale-up plan reduces failed enterprise agent launches.
- Potential engagement
- Sizing PoC with profiler and load calculator on representative prompts.
- Unsupported claims
- One GPU per N users is universal
- Internal NVIDIA SLOs equal customer SLOs
Pre-sales engineering
Role takeaway
- Fit
- Fit for LangGraph/NeMo Agent Toolkit agents backed by NIM on Kubernetes/OpenShift.
- Architecture and integration
- Profile single-user; scale bottleneck NIM; wire OTEL; correct Helm CPU/GPU requests.
- Prerequisites
- Agent toolkit instrumentation
- Load generator
- Metrics/tracing backend
- Retry policy
- Constraints
- Hidden CPU limits
- LLM timeouts
- Non-deterministic agent paths
- Security
- Keep confidential corpora on-prem; control tool egress.
- Proof of value
- Bottleneck identified
- CPU starvation fixed
- p95 within target at test concurrency
Delivery
Role takeaway
- Work
- PoC profiling, soak tests, phased cohorts, and monitoring handoff.
- Dependencies
- platform
- app
- observability
- Ownership
- App owns agent logic; platform owns NIM capacity.
- Skills and adoption
- Teams need profiler and trace-reading skills.
- Governance checkpoints
- Single-user baseline
- Concurrency gate
- Cohort expansion review
- Acceptance criteria
- No CPU starvation under soak
- Timeouts handled
- Tracing live in prod
- Risks
- Big-bang rollout
- Ignoring quality under optimization
- Undersized bottleneck model
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
Size the bottleneck NIM first; validate CPU/GPU allocations in Helm; add timeout/retry policy around reasoning LLMs; require tracing before wide release.
Governance
Who approves, reviews and stays accountable for outcomes?
Phased access and quality eval gates should be formal change-management steps for campus/agency agents.
Security and privacy
What data, permissions and controls need testing?
On-prem blueprint motivated by confidential information risk of public deep-research tools—relevant to SLED data-handling policies.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Reliable concurrency determines whether staff/faculty agents are usable beyond early adopters.
Procurement
What should contracts, pricing and exit terms secure?
Require load-test evidence and observability integration in agent platform contracts.
Operating model
Which teams own the service once it runs?
Agent launch is an SRE practice: cohorts, traces, and capacity forecasts.
Publication history
- 2026-09-15NVIDIA · Issue 1010 resources
Stable resource ID: nvidia-aiq-langgraph-production-scaling-nemo-toolkit-20250827