Creeksea sources, configures, and manages enterprise-grade NVIDIA GPU infrastructure. Hardware is quoted separately from the management retainer and can be financed, leased, or purchased outright. We recommend hardware based on your workload — not our margin.
Ideal for Tier 1 deployments. High-performance consumer and workstation GPUs that offer excellent inference throughput at accessible price points.
The highest-performance consumer GPU available. Exceptional for running 7B–34B parameter models at full precision, or 70B+ models with quantization. Ideal for teams with moderate concurrent user loads.
Professional workstation GPU with double the VRAM of the RTX 4090 and ECC memory support. Runs full-precision 34B models comfortably, and 70B with light quantization. Better suited for compliance-sensitive environments.
Our top recommendation for small businesses. The unified memory architecture lets the CPU and GPU share the same high-bandwidth memory pool, enabling surprisingly large models on compact, whisper-quiet hardware — with zero GPU driver management. Plug in, install Ollama, run models up to 40B at full precision.
The most capable single-box option for teams that need large models at full precision without building a GPU workstation. Up to 192 GB of unified memory handles 70B+ models comfortably, with enough headroom for multiple concurrent users and several models loaded simultaneously.
Recommended for Tier 2 deployments. Purpose-built for inference at scale with ECC memory, higher VRAM, and enterprise-grade reliability.
Enterprise workstation GPU built for sustained 24/7 workloads. ECC memory ensures data integrity in long-running inference processes. Excellent performance-per-watt for always-on deployments.
The flagship professional workstation GPU. 48GB VRAM enables simultaneous loading of multiple large models. Ideal for organizations running parallel inference requests across several model variants.
Required for Tier 3 and Tier 4 deployments. True data center accelerators with NVLink, massive VRAM, and support for full-precision large models and training workloads.
The industry benchmark for enterprise AI inference. HBM2e memory provides 1.6× the bandwidth of GDDR6. Multi-GPU configurations with NVLink enable running 180B+ parameter models without quantization.
Double the VRAM of the A100 40GB. A single node can run full-precision 70B models; 4-node configurations handle 180B+ without compromise. The standard for organizations requiring maximum inference quality.
The most advanced GPU accelerator commercially available. 3× the AI inference throughput of the A100 with Transformer Engine optimizations. The definitive choice for organizations at the frontier of private AI deployment.
For organizations requiring complete network isolation. Creeksea designs and deploys fully air-gapped GPU servers with manual update procedures, hardware security modules, and physical access controls. No external connectivity whatsoever.
GPU VRAM is the primary constraint for local deployments — larger models require more VRAM, or must be quantized to run on smaller hardware. For small businesses, the Apple Mac Studio is our top recommendation: its unified memory architecture allows surprisingly large models to run on compact, quiet, power-efficient hardware with no GPU driver management required.
Cloud deployments use a production inference server (e.g., vLLM with continuous batching) which significantly increases concurrent throughput versus local. Reserved or spot instances can reduce on-demand costs by 30–70%. All estimates reflect approximate pricing as of early 2026.
Local deployments ensure all data stays on-premises, never leaving your network. The only option for sensitive, regulated, or confidential workloads that cannot tolerate any external data exposure.
Local hardware is a one-time capital expense; cloud compute is an ongoing operating expense. Hybrid approaches are possible. At scale, local hardware typically becomes more cost-efficient after 18–24 months of operation.
Cloud deployments scale on demand and handle variable workloads without over-provisioning. Local hardware is fixed capacity. High-throughput or bursty workloads may favor cloud or a carefully designed hybrid model.
On-premises deployments typically deliver lower latency for users on the same network. Cloud latency depends on geographic proximity to the instance and network conditions.
Local hardware requires physical maintenance, driver management, and occasional upgrades. Cloud reduces operational overhead but increases vendor dependency and ongoing spend.
For most clients with data privacy requirements, we recommend starting with on-premises hardware — particularly the Apple Mac Studio for small teams. Cloud is introduced only when local capacity is genuinely insufficient.
Hardware pricing varies with market conditions and availability. Creeksea sources hardware through authorized enterprise channels with full warranty coverage. Hardware can be purchased outright, financed over 24–36 months, or leased. In all cases, the hardware is owned or leased directly by your organization — Creeksea does not retain ownership. Hardware costs are quoted separately from the management retainer at the time of contract.
Contact activate@creeksea.ai for a current hardware quote tailored to your workload and budget.
Hardware selection depends on model size, concurrent users, and budget. Tell us your requirements and we'll build a recommendation — at no cost.