Steer LLM Inference Serving in Real Time — Temperature, Routing, Limits (Kiponos Python SDK)

LLM serving is never "set and forget." Costs spike, a model degrades, you need to route 80% traffic to the backup, lower max tokens, or raise temperature for a creative endpoint — while GPUs are hot.

Kiponos.io lets Python inference workers read serving parameters from memory on every request.

Request path

def build_request(prompt: str, kiponos, route: str) -> dict:
    cfg = kiponos.path("serving", route)
    return {
        "model": cfg.get("model"),
        "temperature": cfg.get_float("temperature"),
        "max_tokens": cfg.get_int("max_tokens"),
        "top_p": cfg.get_float("top_p"),
    }

No object-store fetch. No Redis per request. Local cache read.

Serving config tree

serving/
  chat/
    model: gpt-4o-mini
    temperature: 0.3
    max_tokens: 2048
    top_p: 0.95
  creative/
    model: claude-sonnet
    temperature: 0.9
    max_tokens: 4096
  routing/
    primary_weight: 70
    fallback_weight: 30
    fallback_model: gpt-4o-mini
  limits/
    rpm_per_user: 60
    max_concurrent: 200

Live ops scenarios

Situation	Kiponos tweak
Primary model outage	Shift primary_weight → 0, fallback → 100
Cost spike	Lower max_tokens, switch to cheaper model
Quality regression	Drop temperature, tighten top_p
Launch traffic	Raise max_concurrent and rpm_per_user

Multi-worker fleet

Every inference worker holds the same Kiponos connection profile. One dashboard change → delta broadcast → all workers update locally. No rolling restart across the GPU pool.

Related patterns: ML training tuning, supervisor orchestration.

Try kiponos.io. Repo: github.com/kiponos-io/kiponos-io

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Kiponos.io — real-time config for Python. Steer inference while GPUs are running.