Custom Deployment
Your model. Your deployment.
From model evaluation, quantization and fine-tuning to capacity, region and infrastructure planning.
From requirements to production
Describe your workload
Model, use case, context length and expected traffic.
Evaluate the plan
Model version, quantization, GPU and capacity planning.
Validate the workload
Benchmarks, quality checks and latency / throughput tests.
Deploy to production
Region, private endpoints, API, monitoring and SLA.
Customize around your workload
Model and precision
Choose a model version and evaluate FP8 / NVFP4 quantization.
Fine-tuning and adaptation
Fine-tune and adapt to your data, with quality validation.
Long context
Size inference resources around context window and KV cache needs.
Private deployment
Isolated resources, private endpoints and dedicated capacity.
Regions and data requirements
Choose regions around infrastructure and data-processing requirements.
SLA and support
Define capacity, availability and response targets in your plan.
Engineered for production inference.
We take open models from adaptation, quantization and kernel optimization through caching, parallelism strategies and multi-node deployment, keeping them stable under real production load.
Inference engines
Optimization
Deployment profile
API
OpenAI-compatible
Streaming · SSE
Serving
Dedicated endpoint
Reserved capacity
Scale
Scale vertically or across nodes as workload grows.
Get a custom deployment plan
Tell us your model, workload and deployment needs — we’ll evaluate capacity, regions and performance targets and get back with a plan.