# AI factory

NVIDIA has introduced Minerva, an AIOps platform for AI factories that feeds telemetry from GPUs, switches, storage and training jobs into a knowledge graph updated as jobs move, then applies supervised and unsupervised models to catch the gray failures static thresholds miss. In one NVIDIA case, 81 separate events triggered by a switch restart were merged into a single incident, saving about 54 hours of cluster uptime; in another, the platform found one quietly degrading NVLink connection among roughly 400,000 interfaces. These figures come from NVIDIA's internal testing and case descriptions, within its supported scenarios.

NVIDIA has introduced Minerva, an AIOps platform for AI factories that feeds telemetry from GPUs, switches, storage and training jobs into a knowledge graph updated as jobs move, then applies supervised and unsupervised models to catch the gray failures static thresholds miss. In one NVIDIA case, 81 separate events triggered by a switch restart were merged into a single incident, saving about 54 hours of cluster uptime; in another, the platform found one quietly degrading NVLink connection among roughly 400,000 interfaces. These figures come from NVIDIA's internal testing and case descriptions, within its supported scenarios.

NVIDIA describes the deterministic execution model of the Groq 3 LPX low-latency accelerator within its Vera Rubin platform: the LPU compiler schedules compute and data movement down to the clock cycle, letting it predict per-cycle current demand and use Preemptive Power (PEP) and Clock Period Synthesis (CPS) to reduce voltage droop and shrink the voltage guardband, with internal testing showing over 60% less voltage drop, an estimated high single-digit percentage decrease in the baseline voltage that must be continually supplied, a potentially low-double-digit percentage reduction in power for the same workload, and up to 35x higher throughput per megawatt at the platform level versus the previous-generation GB200 NVL72 for 2T+ parameter models at long context and high interactivity.

NVIDIA describes the deterministic execution model of the Groq 3 LPX low-latency accelerator within its Vera Rubin platform: the LPU compiler schedules compute and data movement down to the clock cycle, letting it predict per-cycle current demand and use Preemptive Power (PEP) and Clock Period Synthesis (CPS) to reduce voltage droop and shrink the voltage guardband, with internal testing showing over 60% less voltage drop, an estimated high single-digit percentage decrease in the baseline voltage that must be continually supplied, a potentially low-double-digit percentage reduction in power for the same workload, and up to 35x higher throughput per megawatt at the platform level versus the previous-generation GB200 NVL72 for 2T+ parameter models at long context and high interactivity.

In this technical article, NVIDIA explains how NVLink 6 supports large-scale AI factories through a multi-layer resiliency framework spanning the physical, link, application, and system layers: the physical layer combines lightweight FEC with Physical Layer Retry (PLR) and UPHY recovery for a natively lossless fabric, the link layer uses credit-based flow control (CBFC) to eliminate packet drops by design, and the application layer uses Dynamo Shadow Engine Recovery and checkpointing to shorten interruptions, with the article reporting that in benchmarked deployments on B200 GPUs shadow engine recovery reduced inference downtime from 283 seconds to 7.3 seconds.

In this technical article, NVIDIA explains how NVLink 6 supports large-scale AI factories through a multi-layer resiliency framework spanning the physical, link, application, and system layers: the physical layer combines lightweight FEC with Physical Layer Retry (PLR) and UPHY recovery for a natively lossless fabric, the link layer uses credit-based flow control (CBFC) to eliminate packet drops by design, and the application layer uses Dynamo Shadow Engine Recovery and checkpointing to shorten interruptions, with the article reporting that in benchmarked deployments on B200 GPUs shadow engine recovery reduced inference downtime from 283 seconds to 7.3 seconds.

NVIDIA and Nscale jointly evaluated DSX MaxLPS policy-governed dynamic power allocation with Kimi K2.5 (FP4) inference workloads on NVIDIA GB300 NVL72 systems at Nscale's data center at the Verne campus in Keflavík, Iceland: under the same 264.4 kW approved power budget, managed GPUs rose from 140 to 192 (+37.1%), aggregate throughput rose from 1,084,503 to 1,618,443 tokens/s (+49.2%), throughput per provisioned watt rose from 4.10 to 6.12 tokens/s/W (+49.2%), median and P75 latency stayed within 5% of baseline, while P99 time to first token increased 17% from the 15.7-second baseline.

NVIDIA and Nscale jointly evaluated DSX MaxLPS policy-governed dynamic power allocation with Kimi K2.5 (FP4) inference workloads on NVIDIA GB300 NVL72 systems at Nscale's data center at the Verne campus in Keflavík, Iceland: under the same 264.4 kW approved power budget, managed GPUs rose from 140 to 192 (+37.1%), aggregate throughput rose from 1,084,503 to 1,618,443 tokens/s (+49.2%), throughput per provisioned watt rose from 4.10 to 6.12 tokens/s/W (+49.2%), median and P75 latency stayed within 5% of baseline, while P99 time to first token increased 17% from the 15.7-second baseline.

NVIDIA introduced DSX Ready, a qualification program for partner products and solutions that meet applicable NVIDIA DSX AI factory reference design requirements, launching with two initial categories, battery energy storage systems (BESS) and cooling distribution units (CDUs), and naming the first qualified BESS and CDU suppliers.

NVIDIA introduced DSX Ready, a qualification program for partner products and solutions that meet applicable NVIDIA DSX AI factory reference design requirements, launching with two initial categories, battery energy storage systems (BESS) and cooling distribution units (CDUs), and naming the first qualified BESS and CDU suppliers.

Using a reception at the Grand Egyptian Museum as its entry point, this article traces how Egypt's and Africa's AI ecosystems are moving from a "potential" narrative to production-scale execution, citing a more than tenfold one-year growth in Egypt's Deep Learning Institute learner base, an estimated $400 million Hassan Allam and A15 data center investment, four AI factories announced or online across Africa within a year with another 656 megawatts in the pipeline, Cassava Technologies' deployments across Egypt, Kenya, Nigeria and Morocco that could reach $720 million, Stratos Lab's more than 50 NVIDIA HGX B300 systems delivering over 7 exaflops, Morocco's Nexus AI factory outside Casablanca with a $1.

Using a reception at the Grand Egyptian Museum as its entry point, this article traces how Egypt's and Africa's AI ecosystems are moving from a "potential" narrative to production-scale execution, citing a more than tenfold one-year growth in Egypt's Deep Learning Institute learner base, an estimated $400 million Hassan Allam and A15 data center investment, four AI factories announced or online across Africa within a year with another 656 megawatts in the pipeline, Cassava Technologies' deployments across Egypt, Kenya, Nigeria and Morocco that could reach $720 million, Stratos Lab's more than 50 NVIDIA HGX B300 systems delivering over 7 exaflops, Morocco's Nexus AI factory outside Casablanca with a $1.

Emerald AI, Google and NVIDIA announced the launch of the AI Energy Management Alliance (AEMA), a coalition convening the full AI and power value chain around a technology-neutral, performance-based approach that lets data centers dynamically manage electricity use to speed interconnection, strengthen reliability and protect affordability.

Emerald AI, Google and NVIDIA announced the launch of the AI Energy Management Alliance (AEMA), a coalition convening the full AI and power value chain around a technology-neutral, performance-based approach that lets data centers dynamically manage electricity use to speed interconnection, strengthen reliability and protect affordability.