Hivelocity Added NVIDIA L4 GPU Acceleration

The company integrated new GPU hardware into its bare metal server bundles to support localized AI inference workloads.

Updated on Sept. 29, 2026 in Data Centers

Isometric editorial illustration of a modular server rack unit with internal hardware modules, representing dedicated data center infrastructure. — Hivelocity has integrated NVIDIA L4 GPU acceleration into its bare-metal server bundles, allowing clients to host AI workloads on single-tenant hardware. AI Illustration. Upload story photo >

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Hivelocity has expanded its bare metal server offerings by adding NVIDIA L4 Tensor Core GPU acceleration to its Tier 3 bundles. This hardware enables businesses to run small language models independently on single-tenant infrastructure.

Why it matters

Teams are shifting away from shared, token-metered AI services toward locally managed models to ensure consistent inference latency. Single-tenant hardware allows these users to maintain data residency while securing predictable monthly costs.

The NVIDIA L4 Tensor Core GPU features 24 GB of memory and operates with a 72-watt power profile. The hardware supports the deployment of small language models ranging from 3B to 13B parameters.

The players

Hivelocity

Founded in 2002, this company is a global provider of data center services and bare metal infrastructure headquartered in Tampa.

NVIDIA

This technology company designs the L4 Tensor Core GPU hardware used to accelerate artificial intelligence and machine learning tasks.

The details

Customers can deploy quantized models on dedicated hardware to keep data within their own controlled environments. The low power draw of the L4 card allows Hivelocity to provide GPU compute across a wide variety of server configurations.

Hivelocity was founded in 2002.
The availability of GPU acceleration was announced on September 29, 2026.

Roadmap

The integration of the NVIDIA L4 Tensor Core GPU reflects a broader industry movement toward energy-efficient hardware for edge and inference tasks. By prioritizing lower power consumption, the company is positioning its bare metal infrastructure to better support the evolving needs of AI developers.

Clients using these servers will benefit from predictable monthly costs for their AI workloads compared to variable-rate cloud services. Deployment is managed on a first-come, first-served basis, meaning early interest could impact immediate hardware availability.

The takeaway

Businesses looking to host AI models locally should consider the power-to-performance ratio of their chosen server hardware. Transitioning to dedicated single-tenant infrastructure can help maintain strict data control while stabilizing operational expenses.