Size & source
Map your models, users, budget, and site requirements. Compare supplier configurations and confirm the exact bill of materials.
GPU accelerators / Hopper
NVIDIA H200.
Evaluate H200 systems for memory-intensive inference and larger model-serving requirements.
Configuration, availability, and commercial terms confirmed by quotation.
START WITH THE WORKLOAD
The right fit
starts with the details.
Workloads where model size, context length, or concurrency makes GPU memory a key constraint.
Deployment approach
Compatible enterprise servers or integrated systems.
What we assess with you
- Model weights and KV-cache memory budget
- Supported server and accelerator configuration
- Throughput under representative request loads
Capacity and performance depend on the quoted hardware, model, precision, context, and serving configuration.
FROM REQUIREMENT TO RUNNING SYSTEM
A clear scope, from the start.
- 01
Estimate model and runtime memory requirements together.
- 02
Select a supported system and networking configuration.
- 03
Benchmark the agreed serving setup and document capacity limits.
THE SERVICES AROUND YOUR HARDWARE
More than the compute.
Supply & install
Coordinate hardware supply, delivery, rack or workstation installation, networking, and power and cooling prerequisites.
Configure local inference
Set up supported drivers, CUDA, containers, and serving engines such as vLLM, Ollama, or NVIDIA NIM where suitable.
Validate & support
Check the agreed workload, access controls, monitoring, and recovery procedures. Hand over documentation and scope ongoing support.
From ambition to action
Let’s size the possibilities.
Share your models, expected usage, budget, location, and target deployment window. We’ll confirm the right configuration and sourcing options.