RENT FROM THE 202 COMPUTE NEOCLOUD
Rent hosted NVIDIA GPU capacity directly from 202 Compute. Your team gets a defined remote environment for the agreed workload while our neocloud operation manages the physical system, site, connectivity, monitoring, and maintenance behind it.
01 / Current capacity
Our current fleet includes three dual-GPU pods. Each system includes the supporting CPU, system memory, NVMe storage, cooling, power delivery, remote access, and health monitoring required for sustained workloads.
202 / CURRENT RTX 5090 FLEET
WHAT ONE POD CAN RUN
Each GPU has its own 32 GB of memory; the pod does not create one unified 64 GB memory pool. Actual fit depends on the software, model size, precision, batching, and whether the workload can use more than one GPU.
Run appropriately quantized models across both GPUs when the selected framework supports multi-GPU execution.
Assign one service to each GPU for language, vision, embedding, reranking, or other independent workloads.
Use LoRA, QLoRA, evaluation suites, and development workflows that fit the available GPU memory.
Accelerate image, video, 3D, simulation, and other CUDA-enabled production pipelines.
Commercial terms define the environment, usage window, access model, support scope, and expected utilization.
Host CPU, system memory, and storage are documented in the capacity proposal for each deployment.
202 Compute houses the pod, observes component health, and coordinates infrastructure maintenance.
Add capacity through additional pods or transition into a larger professional GPU configuration.
02 / Workload fit
We evaluate each engagement for technical fit before reserving hardware.
Dedicated accelerators for APIs, agents, vision, language, and other production inference workloads.
Isolated compute for model adaptation, evaluation, and iterative development.
GPU capacity for qualified 3D, video, visualization, and generative-media workloads.
Longer-term infrastructure arrangements for companies that need predictable access.
03 / Capacity roadmap
New configurations are planned around customer demand. Future systems are not represented as currently available; companies can discuss requirements and potential reservations now.
Compact dedicated capacity for inference, adaptation, rendering, and parallel GPU services.
Professional Blackwell capacity for larger models, higher concurrency, scientific computing, simulation, and enterprise workloads.
Higher GPU counts, networking, storage, and deployment design developed against a qualified contract requirement.
04 / Engagement models
PROJECT
Capacity reserved for a specific project, run, or delivery window.
RESERVED
Predictable dedicated access for ongoing workloads and development teams.
CUSTOM
A tailored deployment covering hardware, environment, utilization, support, and term.
CAPACITY REQUEST
Include your preferred GPU count, software stack, utilization pattern, storage needs, start date, and contract term.