Architecture consulting
Requirements discovery, platform comparison, deployment models, capability planning, and transparent tradeoffs.
Private compute + hybrid systems
Own the capability your workload actually needs.
BLITS engineers right-sized on-prem AI/ML systems and hybrid architectures for teams that need local performance, data control, a smaller footprint, and a clear operational path.
- NVIDIA
- AMD
- Linux
- On-prem
- Private models
- Hybrid cloud
Workload-first architecture
Start with the model, data, and operating reality.
GPU count alone does not define a useful AI platform. Memory capacity, data movement, storage, thermals, power, software compatibility, concurrency, privacy, and the people operating the system all shape the correct design.
- Workload and model-hosting assessment
- Vendor-neutral NVIDIA and AMD evaluation
- Local, cloud, and hybrid tradeoff analysis
- Capacity, memory, storage, thermal, and power planning
- Operational model and lifecycle considerations
Small footprint, serious capability
On-prem does not have to mean datacenter scale.
Many teams need controlled local inference, development, retrieval, automation, or engineering tools—not a giant training cluster. We design for the useful workload and a manageable operating footprint.
Engineering scope
The platform around the accelerator matters.
Compute is one layer. A dependable AI/ML environment also needs a stable system, good data paths, access controls, observability, and a way to deploy work repeatedly.
GPU system engineering
Component selection, physical design, thermal considerations, host configuration, and accelerator provisioning.
Linux optimization
Driver and runtime integration, system tuning, service design, resource controls, and reliable administration.
Storage & data paths
Dataset movement, local storage, shared access, throughput, capacity, backup, and retention considerations.
Model hosting
Private inference services, access patterns, containerized deployment, resource allocation, and operational handoff.
Hybrid integration
Secure connections between local systems and cloud resources for selected workflows, scale, or collaboration.
Custom technical tooling
Turn compute into a usable business tool.
Where appropriate, BLITS can connect models to internal workflows with custom interfaces, engineering utilities, retrieval systems, deterministic calculations, and task-specific automation.
Control where it matters
Keep sensitive or latency-critical work closer.
Local capability can reduce dependency on an external service, make data boundaries clearer, and provide predictable access. Hybrid architecture remains useful when cloud elasticity or a managed capability fits part of the workload better.
- Private or sensitive data workflows
- Production and engineering assistance
- Internal knowledge retrieval
- Repeatable model services for local applications
- Cloud bursting where it provides real value
A durable decision
Plan beyond the initial benchmark.
Can the workload run well?
Validate model compatibility, memory needs, performance expectations, data flow, and user concurrency.
Can the team keep it useful?
Account for monitoring, updates, security, supportability, power, thermals, and recovery from failures.
Can the platform change?
Choose architecture with realistic expansion, replacement, software, and hybrid options in view.
AI/ML infrastructure inquiry
Bring the workload—not a shopping list.
Share the model or use case, data sensitivity, expected users, and where the system needs to operate.