Inference Pipeline

Benched.ai Editorial Team

An inference pipeline is the sequence of steps that transform a user request into a model output and back to the client.

Reference Architecture

Step	Runtime	Purpose
Auth & rate limit	Rust gateway	Validate key, throttle abuse
Pre-processing	Python workers	Tokenize text, resize images
Scheduler	C++	Batch & route to GPUs
Model server	Triton / vLLM	Generate tokens
Post-processing	Node.js	JSON schema, profanity filter
CDN / WebSocket	Edge	Stream chunks to UI

Latency Budget Example (Chat, 1k tokens)

Component	Target ms
Gateway	20
Pre-proc	30
Queue	100
Inference	1200
Post-proc	40
Egress	30

Design Trade-offs

Centralized scheduler maximizes GPU utilization but is single point of failure.
Pre-processing on CPU saves GPU cycles yet increases data movement.
Streaming reduces perceived latency but complicates retry logic.

Current Trends (2025)

vLLM KV-cache multiplexing allows 10× concurrent streams per GPU.
gRPC with HTTP/3 reduces tail latency vs REST.
WASM pre-processing modules run inside envoy filters for low overhead.

Implementation Tips

Tag each request with a trace ID that propagates through every stage.
Use batched tokenization to offload CPU pressure.
Monitor queue depth and back-pressure tokens/s in autoscaler.