vLLM Optimizer Future Implementation Roadmap
This roadmap lists capabilities that follow the first MVP described in MVP_SPEC.md. It is organized by dependency and product value, not by promised release dates.
Guiding rules
Future work should preserve these invariants:
- Raw measurements remain separate from ranking and optimization policy.
- A trial is one server configuration; a scenario evaluation is one workload result within that trial.
- Reports remain regenerable from persisted data without GPUs or benchmarks.
- Benchmark and server integrations use adapters.
- Local execution remains the simplest default even after distributed support exists.
- New features must not weaken owned-process cleanup, immutable-run integrity, or secret handling.
- Any result labeled “best†must state the population and scoring policy from which it was selected.
Detailed phases
- Experiment engine and benchmark coverage
- Optimization and rankings
- Faster local execution
- Distributed execution and reporting
- Automation, APIs, cost, and capacity
- Cross-cutting requirements
Suggested release sequence
The roadmap can be grouped into practical releases:
| Release | Theme | Main outcome |
|---|---|---|
| MVP | Reliable local loop | Trustworthy unattended single-host experiments |
| 0.2 | Measurement confidence | Advanced retries, drift detection, statistics, richer comparisons |
| 0.3 | Benchmark breadth | vLLM Bench Serve, datasets, quality gates |
| 0.4 | Optimization depth | Multiple targets, Pareto search, pruning |
| 0.5 | Faster local runs | Cache reuse, controlled reuse, parallel GPU workers |
| 0.6 | Interactive analysis | Local UI, richer rankings and parameter analysis |
| 0.7 | Distributed execution | Remote workers and shared artifacts |
| 1.0 | Stable platform | Versioned APIs, plugins, migrations, compatibility guarantees |
Version numbers are illustrative. Reliability gates should determine release timing rather than the number of accumulated features.
Prioritization test
Before adding a roadmap feature, ask:
- Does it improve measurement trust, failure recovery, or reproducibility?
- Does it help users answer a decision they cannot answer from raw results?
- Can it be implemented without weakening the local-first workflow?
- Does its persisted data model remain useful if the implementation changes?
- Is its complexity justified relative to model startup and benchmark cost?
Features that improve reliability and interpretability should normally precede features that only add optimizer variety or visual polish.