The rise of generative artificial intelligence (GenAI) and Large Language Models (LLMs) has created a fundamental shift in enterprise infrastructure requirements. The previous decade’s dominant model of general-purpose, multi-tenant cloud computing is often inadequate for the high-performance demands of modern AI workloads. As organizations move from pilot projects to production-grade
During the 2024 Olympics, streamers delivered over 3,800 hours of live content to millions of concurrent viewers. The technical feat behind such a broadcast is immense, yet for the end-user, the expectation is simple: instant playback, zero buffering, and pristine resolution. When buffering wheels spin, users churn. As streaming platforms
We’re excited to announce a new feature in the Hivelocity Storefront — customers can now upgrade their primary drives when configuring select servers for the Tampa 1 (TPA1) location. This update gives you more flexibility and control over your hardware, all while streamlining your ordering and deployment process. Upgrade your primary
Databases are the heartbeat of modern applications, powering everything from e-commerce product catalogs to high-frequency trading platforms. In an era where microseconds translate to revenue, the infrastructure underpinning these data stores is critical. While the convenience of virtualized cloud instances has dominated the last decade, a growing number of database
The competitive digital entertainment landscape, particularly the real-time multiplayer sector, is at a significant architectural turning point. As player expectations for fidelity and responsiveness push the limits of human perception, the “Cloud First” orthodoxy is being challenged. A “Performance First” mandate is emerging, increasingly pointing back to the raw, unadulterated
Deploying Large Language Models (LLMs) locally offers significant advantages in data privacy, cost efficiency, and performance control. However, the setup process can often be complex and resource-intensive, particularly for those looking to avoid the high costs associated with GPU hardware. Ollama simplifies this by enabling efficient LLM deployment on CPU-based
The landscape for deploying Large Language Models (LLMs) is undergoing a fundamental realignment. Escalating model complexity and a persistent scarcity of high-performance GPUs have forced a re-evaluation of CPU inference capabilities. While top-tier GPUs remain the standard for high-throughput serving, their cost and supply constraints present significant challenges. This post
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Cloud infrastructure needs evolve as businesses mature. What works for a startup may not serve a scaling enterprise, and recognizing when your organization has outgrown its current cloud provider is crucial for long-term success. Amazon Web Services (AWS) remains a powerful platform, but it’s not the right fit for every
Enterprises will waste an estimated 21% of their cloud infrastructure spending on underutilized resources in 2025, totaling $44.5 billion. That’s not a typo. Approximately 21-32 % of cloud investment delivers no business value, with idle resources, overprovisioned instances, and hidden costs bleeding budgets dry. If your AWS bill has become
The narrative of the last decade has been dominated by a one-way migration to the public cloud. Enterprises stampeded toward providers like AWS, driven by the promise of infinite scalability, agility, and a “cloud-first” ideology. However, the operational realities of running extensive workloads in the public cloud have begun to