As Hong Kong enterprises adopt AI, one of the earliest decisions CTOs must make is how to select the right AI server. Enterprise AI servers generally fall into three budget tiers – high, middle, and low – using mainstream vendors such as ASUS, Dell, HP (HPE), and Lenovo.
These tiers align well with different levels of LLM training and inference capacity, and map directly to the Parami AI solution stack across documents, voice biometrics, security AI, and chatbots.
Below is a concise guide to what each tier can support, followed by the major AI functions enabled by these servers and real examples of how Hong Kong organizations apply them.
At the high end, Dell’s flagship AI servers (such as the PowerEdge XE9680 or top R-series AI nodes) offer up to 8 data-center GPUs per node. These configurations are designed for large-scale generative AI and LLM training, featuring advanced cooling, large memory bandwidth, and rack-scale deployment capabilities.
Lenovo’s HGX-class ThinkSystem servers (e.g. SR780a/SR675 V3 with up to 8 GPUs) and HPE’s Cray or ProLiant XL/XD GPU-dense platforms similarly target foundation-model training and heavy multi-tenant inference in enterprise data centers.
Typical use cases
These high-budget machines are suitable when you need to handle organization-wide or city-scale workloads and long-context LLMs, for example:
In the mid tier, Dell PowerEdge R760xa/R7725 AI configurations provide 2–4 data-center GPUs per node with strong CPU performance and I/O throughput. These systems provide solid capacity for fine-tuning LLMs, running embedding services, and supporting moderate-to-high QPS inference.
Lenovo ThinkSystem SR670 V2 and HPE ProLiant DL380a Gen11 with 2–4 GPUs represent similar “balanced” options for specialized domain model training and hosting multiple production services on a shared cluster.
Typical use cases
This level is ideal for organizations that want strong in-house AI capability without hyperscale budgets. A mid-range node or small cluster can simultaneously power:
For low-budget or edge-oriented deployments, the ASUS Ascent GX10 (GB10-class) provides a compact “desktop AI supercomputer” built on an NVIDIA Grace Blackwell GB10 superchip with unified CPU-GPU memory. This tier is targeted at AI developers and small teams.
Equivalent entry-grade AI servers from Dell or Lenovo typically use one mid-range data-center GPU in a tower or 2U chassis. They support lighter LLM inference, embeddings, small fine-tuning jobs, and can act as satellite nodes to a central cluster.
Typical use cases
This tier is suitable for SMBs, branch offices, and pilot projects that want to host Parami capabilities locally. A single GB10-class machine can:
Although hardware tiers differ, most enterprise AI workloads fall into four categories.
Parami DAPmi ingests multi-year logs and records, allowing auditors to query incidents in natural language while keeping all data inside government infrastructure.
Parami Natalie provides Cantonese/English support, policy lookups, product guidance, and customer workflows with low latency and regulatory compliance.
Parami Theia detects crowding, loitering, and restricted-zone breaches, with mid-tier servers handling dozens of camera feeds in real time.
Parami BrainBot robots identify hazards such as open doors or abandoned objects. Low-tier servers per building combine with mid-tier central nodes for analytics.
Parami Athena provides password-free identity verification, with all voiceprint processing kept fully on-prem.
Parami Theia uses vision AI technology to inspect hotel room cleanliness and support automated quality control.
AI servers—from high to low budget tiers—allow Hong Kong enterprises to bring critical AI workloads in-house, balancing performance, cost, and data governance.
Across all tiers, organizations gain the ability to deploy:
By matching the right hardware tier with the right AI function, businesses can scale AI adoption efficiently, securely, and in line with Hong Kong’s operational and compliance requirements.
