Inference Systems
Inference designed for production reality
How FlexAI inference behaves under load. No abstractions. No feature lists.
Inference Execution
Choose the deployment model that fits your workload
Run serverless for simplicity, dedicated for isolation, or shared for cost efficiency.
Token as a service
Pay per token
- Token based execution with no infrastructure management
- Automatic scaling for bursty or unpredictable traffic
- Multi tenant GPU scheduling for high efficiency
- Ideal for APIs, applications, and developer experimentation
Tip: Start with serverless for speed. Move to dedicated for isolation. Scale shared for cost efficiency.
Real Time Inference
Latency bound. User visible.
Sub 100ms time to first token
Streaming responses with bounded p95
Dynamic concurrency with internal micro batching
Elastic scale without pre provisioned capacity
Batch Inference
Throughput bound. Cost optimized.
95%+ GPU utilization under load
Lowest cost per token execution
Multi node parallel processing
Often asynchronous or scheduled
Asynchronous Execution
A batch behavior, not a separate mode. Jobs are queued and results are delivered via callbacks or polling.
Observed Behavior
90–180ms
Sub-100ms P95 Latency
Latency remains within declared bounds as request volume increases.
2–5×
Throughput Under Load
Dynamic batching increases utilization compared to single-request execution.
0 → 100s
Burst Concurrency
Scale to hundreds of GPUs without pre-provisioning or warm pools.
$0 idle
Zero Idle Cost
Billing follows active inference only. Idle capacity does not accumulate cost.
Pipelines
Optimized inference pipelines
Optimized pipelines for higher bandwidth and faster execution.
Dynamic auto-scale aligned to real request demand.
Multi-modal support across LLMs, MoE, NLP, vision, and RAG.
Compatible with vLLM, TensorRT-LLM, PyTorch, and custom runtimes.
Inference Sizer
Reason about scale before deploying
Estimate latency, throughput, and GPU requirements based on real workload characteristics. This is not pricing. It is physics.
4
GPUs needed
~120ms
Est. p95 latency
1.2k
Req/s capacity
Underlying Stack
vLLMTensorRT-LLMPyTorchHugging FaceFlashAttention-3
Ready to deploy inference that scales?
Get to production in minutes, not weeks.