Reducing the Carbon Footprint: Energy-Saving Strategies for Data Centers

The NVIDIA H200 is crucial for high-throughput batch inference in scalable AI, addressing throughput (measured in tokens/sec) as the true bottleneck, rather than raw FLOPs. Its 141 GB HBM3e memory and 4.8 TB/s bandwidth, coupled with the FP8 Transformer Engine and NVLink + NVSwitch, enable efficient handling of large models and multi-batch inference, significantly increasing tokens per second.

Achieving this requires a “bandwidth-first” architectural design, involving memory-aware batch scheduling, network fabric optimisation (e.g., GPUDirect RDMA, InfiniBand), and orchestration using Kubernetes and NVIDIA Triton Inference Server. When correctly architected, H200 clusters deliver substantial performance and cost gains, including +33% sustained GPU utilisation, +81% tokens/sec, and reductions of 36-38% in inference and power costs. Uvation specialises in deploying these optimised, cost-effective GPU clusters.

NVIDIA H200 installations, vital for enterprise AI and generative AI, require a pre-flight stress test to prevent costly production failures. Many teams wrongly assume readiness post-installation, but real issues like latency spikes, thermal throttling, and unexpected reboots occur under actual LLM workloads. Skipping this crucial validation can lead to interconnect failures, silent memory corruption, and GPU underutilization.

Uvation’s comprehensive stress test evaluates both hardware and software resilience, covering aspects such as thermal load cycling, power spike simulation, memory burn-in, I/O flooding, and driver/container stack validation. By simulating production-grade cluster I/O, Uvation identifies real stress points, not just pass/fail conditions. This process significantly improves LLM inference latency, boosts GPU utilisation to over 92%, and eliminates power stability incidents and container restarts in production, ensuring a robust, scalable AI infrastructure.

Business leaders across the world are coming to terms with the realities of a permanent hybrid work
model, one in which many employees will work remotely on a permanent basis, at least part of the time.
An astonishing 75% of global CEOs expect their office spaces to shrink as result, Forrester reports, where
“70% of U.S. and European countries will pivot to a hybrid work model” even after COVID-19 subsides.

As the cybersecurity landscape continues to evolve, federal agencies are struggling to keep pace. “Every
day, our adversaries are using known vulnerabilities to target federal agencies,” CISA Director Jen
Easterly said in a 2021 report.

In 2021, roughly 3.9 million workers quit their jobs every month, breaking the previous record of 3.5
million in 2019, SHRM reports. This was in part due to the increasing demand for technical talent and
the lack of qualified candidates to fill these positions. In December 2021,

Business leaders across the world are coming to terms with the realities of a permanent hybrid work model, one in which many employees will work remotely on a permanent basis, at least part of the time. An astonishing 75% of global CEOs expect their office spaces to shrink as result, Forrester reports, where “70% of U.S. and European countries will pivot to a hybrid work model” even after COVID-19 subsides.

Devices enabled by the Internet of Things (IoT) have become ubiquitous, touching dozens of aspects of everyday life. Countless organizations across manufacturing, retail, healthcare, and others employ IoT as part of their critical daily operations as well.

As enterprise digital technologies grow more diverse and complex, working with multiple vendors—and integrating their solutions into your enterprise technology stack—is becoming more difficult. Growing companies may lack the technical expertise they need to choose and implement the right digital solutions as well.

From a business perspective, cloud computing refers to the use and availability of computing resources, such as servers or data storage, networking, analytics, over the internet under a pay-as-you-use model. By working with a cloud solutions provider,