# Timebill Inference

> Guarantee LLM inference completes within time budgets via response length prediction, execution time estimation, and adaptive KV cache eviction. Three-stage pipeline predicts response length, estimates end-to-end time with 1.22% accuracy, adjusts cache eviction ratio—enabling time-critical deployment in robotics and autonomous systems.

- Skill: `adu2021/timebill-inference` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/timebill-inference`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/timebill-inference/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/timebill-inference

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## Overview

TimeBill solves the problem of unpredictable LLM inference latency in time-critical systems.

## Core Technique

**Three-Stage Pipeline:**

```python
# Stage 1: Response Length Prediction
length_predictor = fine_grained_response_length_model()
predicted_length = length_predictor.predict(input)

# Stage 2: Execution Time Estimation
ete = execution_time_estimator()
worst_case_time = ete.estimate(predicted_length)

# Stage 3: Adaptive KV Cache Eviction
cache_eviction_ratio = calculate_optimal_eviction(worst_case_time, time_budget)
```

## When to Use

Use when: Robotics, autonomous driving, time-critical inference.

## References

- Response length prediction
- FLOPs-based execution time modeling
- Adaptive KV cache management