AI Infrastructure
Own your
intelligence.
Lunvex Labs builds bespoke AI infrastructure: fine-tuned open models, custom server and rack design, and hardware tuned to your workload. Own your models and your compute.
Designed, built, racked, and tuned to the workload.
What we build
End-to-end AI infrastructure, yours to own.
From model selection through racking hardware and running inference in production. Every layer designed for the team that cannot afford a black box.
In-house AI and Llama Builds
We design and build model stacks from the ground up. Llama-family and other open-weight models, configured and deployed on infrastructure you control.
Fine-tuning and Self-hosting
Task-specific fine-tuning on your data. Weights stay yours. Models run in your environment: on-prem, co-located, or air-gapped.
Server and Rack Design
Custom server architecture designed to the compute profile of your workloads. Rack layout, thermal planning, and component selection handled end to end.
Custom Hardware for Efficiency
Compute matched to inference and training patterns. Hardware selected and configured for performance-per-watt. Cost efficiency improves over time at scale.
Deployment and Ops
We stand up your stack, configure inference endpoints, monitoring, and failover. Ongoing support to keep models sharp and hardware healthy.
Why owned compute
Renting is a tax on every token.
API access trades control for convenience. For teams running meaningful inference volume, owning the stack pays back quickly and compounds from there.
On-prem or co-located
Deployment flexibility
Run inside your own data center or in a trusted colocation facility.
Open models, owned weights
No black box
Llama and open-weight models you can inspect, fork, and retrain.
Hardware tuned to workload
Built for the job
GPU, CPU, and memory configurations matched to your inference profile.
Cost improves with scale
Economics compound
Fixed infrastructure amortizes over time. Per-inference cost falls as volume grows.
Model ownership
Weights belong to the provider. No insight into what runs.
Full model weights, on your storage, under your control.
Cost curve
Fees scale with every token. Costs rise with usage.
Fixed hardware investment. Unit cost drops as scale increases.
Data privacy
Prompts and completions cross third-party infrastructure.
Data never leaves your environment. Air-gap possible.
Latency
Shared network paths and rate limits affect throughput.
Local inference on dedicated hardware. Predictable performance.
Vendor dependency
Pricing, availability, and model changes are out of your hands.
No lock-in. Switch models, adjust hardware, evolve freely.
The process
Five steps from brief to live.
A build is only as good as the thinking behind it. We go deep on requirements before touching a rack.
Scope the workload
We start with a deep conversation about your use case: inference volume, latency targets, data sensitivity, team size, and budget. The workload defines the build.
Design the stack
Model selection, hardware configuration, and software architecture designed together. Every component justified against your specific requirements.
Build and rack
Servers assembled, racked, cabled, and tested. Thermal and power validated before anything goes live. Built in-house, not drop-shipped.
Tune for efficiency
Model quantization, inference optimization, and hardware tuning to hit performance targets. We measure before and after, not just after.
Deploy and support
Stack goes live with monitoring, alerting, and runbooks in place. Ongoing support to evolve models as your needs change.
Hardware showcase
Built for the job. Tuned for economics.
Every rack we build starts with the inference profile. We design to the workload: frequency, batch size, context length, latency budget. Generic provisioning wastes money and performance.
- Hardware selected for the inference workload, not generic provisioning.
- Model quantization to reduce memory bandwidth and improve throughput.
- Batch scheduling tuned for latency vs throughput trade-offs.
- Power capping to prevent thermal throttle under sustained load.
Design principle
Cost efficiency improves as scale grows. Fixed infrastructure pays back over time, not per token.
Compute density
High-density GPU nodes
Maximizing FLOPS per rack unit
Networking
High-bandwidth fabric
Low-latency node interconnect
Storage tier
NVMe all-flash
Model weights and KV cache close to silicon
Power design
Redundant PSU
No single point of failure
Cooling
Active thermal management
Sustained throughput at full load
Form factor
Standard rack-mount
Fits any 42U data center cabinet
Let's build your stack
Intelligence designed to your spec.
Tell us about your workload. We will scope the model stack, design the hardware, and build the infrastructure you need. No vendor lock-in. No rented intelligence.
hello@lunvexlabs.com