AI Application Inference
Production-grade inference infrastructure for RAG, agents, and multimodal apps.
Serving large models in production is a different problem than training them. Our inference solution stitches together the right hardware tier, the right scheduler, and the right observability for predictable latency at any scale.
Background & challenges
Architecture components
Distributed inference network
Regional PoPs with anycast routing and fail-over.
Tuned servers
vLLM, TensorRT-LLM, SGLang — continuously benchmarked on every SKU.
Dynamic scheduling
Token-aware routing, KV-cache prefill pools, speculative decoding.
Implementation steps
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1
Workload audit
Characterize your traffic, model mix, and latency SLOs.
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2
Solution design
Choose the right GPU tier (H20 / L40 / H200) and serving stack.
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3
Integration & optimization
Deploy serving, apply quantization and speculative decoding, validate quality.
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4
Training & inference hand-off
Automate model promotion from lab to production.
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5
Monitoring & maintenance
Real-time p99, token/s, and cost-per-request dashboards.
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6
Continuous improvement
Quarterly model/SKU review — swap in new hardware as it lands.
Advantages & value
- Improved inference throughput — up to 3× via quantization + speculative decoding.
- Reduced cost per million tokens by matching model size to the right SKU.
- Elastic capacity that scales with your traffic curve.
- Higher availability: multi-region serving with automatic fail-over.
- Accelerated time-to-production for new AI features.
Let's architect your deployment
Our solutions team will scope, price, and stand up the infrastructure for you.
Talk to a solutions architect