More Than 98% of NVIDIA Reference Performance Across Six Training Configurations, Validated Under Full Load
PALO ALTO, Calif., Aug. 18, 2026 /PRNewswire/ — PaleBlueDot AI (“the Company”), a Silicon Valley-based AI intelligence platform founded in 2024, today announced that its NVIDIA HGX B300 cluster has achieved NVIDIA Exemplar Cloud status for large-model training workloads. Working closely with NVIDIA’s engineering team, the Company met NVIDIA’s performance requirements across every benchmarking recipe, exceeding the 95% performance threshold across all tests. This recognition validates the cluster’s performance, resiliency and scalability, giving AI laboratories and enterprise customers greater confidence when running demanding training workloads at scale.
What Is NVIDIA Exemplar Cloud?
NVIDIA established Exemplar Cloud in 2025 to address a real problem: running production-scale AI workloads is a data-center-scale challenge, requiring optimization across the entire infrastructure stack. When that optimization breaks down, performance suffers. Users see slow responses, rising compute costs, unpredictable reliability and higher TCO, while innovation slows. Exemplar Cloud gives providers a standard benchmark to validate their infrastructure against, so buyers can compare against a standard rather than a claim.
This Exemplar Cloud status delivers tangible advantages to AI laboratories and enterprise customers via a credible performance reference during procurement reviews and project budget approvals.
“Achieving NVIDIA Exemplar Cloud status on NVIDIA HGX B300 is an important validation of the engineering discipline behind our AI infrastructure,” said Stephen Watts, CEO of PaleBlueDot AI. “Customers need more than access to leading GPUs. They need predictable performance and sustained reliability at scale. We focus on optimizing the full stack, from compute, networking and storage to scheduling and operations, so customers can run their most demanding training workloads with confidence.“
Performance Validated On Real-World Training Workloads
PaleBlueDot AI’s benchmark campaign encompassed six mainstream large-model training workloads: DeepSeek-V3, GPT-OSS, Nemotron-H, Qwen3, and two distinct Llama 3.1 configurations. This selection covers the model families that define frontier training today.
Every test run exceeded 98% of NVIDIA reference performance. Results held across divergent model architectures, parameter scales ranging from moderate to frontier-class, and multiple numerical precision formats, demonstrating near-reference training performance as a standing property of the cluster rather than the outcome of any single favorable configuration.
These results demonstrate our ability to deliver consistent, optimized training performance across different model architectures, parameter scales and numerical precision formats.
Engineered For Performance And Reliability At Scale
PaleBlueDot AI’s Blackwell Ultra cluster is built on NVIDIA HGX B300 systems. Each compute node contains eight NVIDIA Blackwell Ultra GPUs connected through NVIDIA NVLink and NVIDIA NVLink Switch, creating a fully interconnected AI infrastructure compute domain within each node.
The HGX B300 training cluster adopts an 800Gb/s non-blocking NVIDIA Quantum-X800 InfiniBand networking architecture, eliminating cross-node communication bottlenecks that commonly restrict distributed training efficiency at production scale. Each GPU is equipped with a dedicated 800Gb/s high-speed network connection, providing aggregate compute-network bandwidth of up to 6.4 Tb/s per node.
PaleBlueDot AI has optimized the infrastructure as an integrated system spanning:
- Accelerated computing and system tuning: Customized hardware configurations aligned with NVIDIA Blackwell Ultra GPUs and the data center’s high-density power and air-cooling design help maximize per-GPU computing output.
- High-performance network architecture: The 800Gb/s non-blocking NVIDIA Quantum-X800 InfiniBand fabric is designed to provide lossless, low-latency communications for large-scale distributed training.
- High-throughput storage: A parallel storage system and 63.36TB of local NVMe cache per compute node accelerate training-data loading and frequent checkpoint operations.
- Workload scheduling and resource orchestration: Optimized scheduling logic improves resource utilization, reduces computing waste and helps lower idle training costs.
- Full-lifecycle cluster monitoring and operations: Automated 24/7 alerting and operational mechanisms reduce unexpected interruptions to long-running training workloads.
The cluster also incorporates topology-aware scheduling, NVIDIA GPUDirect RDMA, collective communication optimization and automatic isolation of unhealthy nodes. Together, these capabilities improve large-scale training efficiency and reduce the impact of infrastructure faults on active workloads.
Validated For Sustained Full-Load Operation
Beyond NVIDIA’s benchmark assessment, PaleBlueDot AI completed a continuous full-load stability test on its HGX B300 cluster.
This week-long, non-stop simulation replicated production scenarios in which enterprise training jobs run continuously for days or weeks. The test covered the cluster’s compute, networking, storage and scheduling systems, verifying stable operation under sustained heavy load.
It has also deployed high-density power delivery and purpose-built air-cooling infrastructure to support continuous full-load operation. These systems help maintain stable operating conditions and optimal GPU performance.
In addition, the Company has established a multi-stage quality assurance framework covering:
- Hardware burn-in testing
- Single-node acceptance testing
- Cluster-level long-duration stability testing
This process helps identify hardware or configuration inconsistencies before production deployment and maintain consistent performance and configuration across the cluster.
For AI laboratories and enterprises, these capabilities translate into more predictable workload performance, faster data loading and checkpoint operations, lower risk of disruption and greater confidence when scaling complex training workloads.
Advancing Production-Ready AI Infrastructure
This achievement represents another milestone in PaleBlueDot AI’s continued investment in high-performance AI infrastructure. The Company will continue optimizing capabilities across computing, networking, storage, scheduling, monitoring and operations to provide reliable, production-ready infrastructure for increasingly demanding AI workloads.
“Performance at scale is determined by how well every layer of the infrastructure works together,” added Stephen Watts. “Our core focus is translating cutting-edge NVIDIA GPU hardware into standardized, production-ready and reliable computing capacity that enterprises can adopt efficiently at scale.”
The Exemplar Cloud achievement builds on PaleBlueDot AI’s longstanding collaboration with NVIDIA. The Company will continue working closely to bring next-generation NVIDIA architectures to training and inference workloads for enterprise customers around the world.
About PaleBlueDot AI
PaleBlueDot AI is a Silicon Valley-based AI Intelligence platform with a growing global footprint. The company delivers high-performance agentic AI infra through a unified platform designed for enterprise-scale deployment. Guided by its mission to make intelligence universally accessible, PaleBlueDot AI enables organizations to build, deploy, and scale AI faster, better, and cheaper. Named after the image of Planet Earth taken on the 1990 Voyager space mission coined by Carl Sagan as “a pale blue dot,” the company shares a belief in the transformative potential of AI technology to benefit all of humanity.
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