Infrastructure
Dedicated inference capacity
GPU capacity reserved for your models, with a defined resource boundary. Your team runs the serving stack.
- Best for
- Teams with their own serving runtime and steady traffic
Products / Production Inference
From trained model to production service.
Inference capacity, managed model serving, and application-facing APIs, tuned to your latency targets, traffic patterns, and cost per token.
- Options
- Dedicated capacity, managed serving, inference APIs
- Tuned for
- Time to first token, throughput, cost per token
- Integrations
- Multiple model providers’ APIs
- Before release
- Fine-tuning, post-training, evaluation
The serving problem
The whole stack sets the cost of inference.
Request shape, context length, GPU memory, batching, and concurrency determine how fast responses return and what each token costs. We tune them together.
Serving options
Choose how much of the stack ARC runs.
From raw serving capacity to a managed runtime to an API your application calls.
Managed service
Managed model serving
ARC operates the serving environment: runtime health, monitoring, release workflow, and scaling policies you approve.
- Best for
- Production serving without an in-house serving team
Application layer
Inference APIs
Application-facing endpoints for models deployed on ARC, plus integration with multiple model providers’ APIs.
- Best for
- Products that combine several models
Runtime optimization
The levers that decide serving performance.
We measure them on your traffic before we change anything.
Request routing and concurrency
Distribute requests across replicas and queues based on real traffic patterns.
Batching and caching
Raise GPU utilization without hurting response times.
Latency and throughput
Balance time to first token, generation speed, and sustained throughput.
Cost per token
Connect model size, runtime behavior, and utilization to what each token costs.
Model services
Get the model right before you serve it.
ARC supports fine-tuning, broader post-training, and evaluation, with a clear release path into production.
- Fine-tuning on domain datasets for a specific task
- Post-training to shape model behavior beyond a single task
- Evaluation against task-specific quality criteria before release
- Model versioning, licensing, and data handling agreed up front
Plan for the traffic, not just the model.
Share your models, latency targets, expected concurrency, and request volume. We’ll help you size and tune the serving environment.
or email contact@arccloudai.com