Inference Runtime
03 / Adaptive AI Inference Runtime
C++20 · AI Infrastructure · Scheduling
Adaptive AI
Inference Runtime
A C++ multi-model inference runtime designed around workload-aware scheduling, dynamic batching, worker routing, and memory-constrained model residency.
Demo coming soon
Conceptual runtime flow
Worker A
Model X
Queue depth · med
Worker B
Model Y
Queue depth · low
Runtime problem
When inference becomes a systems problem
Serving multiple models under concurrent load turns inference into scheduling, routing, and memory management — not just model execution.
Runtime pressure
- Multiple models
- Concurrent requests
- Limited compute / memory
Decisions that follow
- What should run next?
- Where should it run?
- Should compatible requests wait briefly to form a batch?
- Which models should remain resident in constrained memory?
- What happens when demand changes?
Conceptual resource example
Worker memory budget
Total demand can exceed what can remain resident at once — residency becomes a runtime decision.
System design
Runtime architecture
Requests enter the runtime; the scheduler decides what should run, the router decides where it should run, and workers execute using resident or loadable models.
Runtime policy ≠ model execution — admission, queueing, scheduling, batching, routing, residency, and overload handling belong to the runtime architecture; the execution backend performs neural computation.
Execution is isolated behind a backend boundary, with llama.cpp as the intended real-model backend.
Adaptive runtime
Four decision surfaces
Adaptive means runtime decisions respond to workload and resource state — not automatic model switching or learned schedulers.
01
Scheduling
Workload-aware scheduling can consider queue state, waiting time, model locality, estimated request cost, batch opportunities, and deadlines.
02
Dynamic batching
Compatible requests can wait inside a bounded window to form a larger batch — trading throughput against latency and time-to-first-token.
03
Routing
Worker selection can balance current load against whether the requested model is already resident — the central model locality vs worker load trade-off.
04
Model residency
When multiple models compete for finite memory, the runtime decides which models remain loaded and which are evicted or reloaded as demand changes.
Adaptive decisions are designed to be evaluated against simple baselines — FIFO vs workload-aware scheduling, batch size 1 vs dynamic batching, round-robin vs residency-aware routing, LRU vs cost-aware residency.
Performance trade-offs
Systems judgment over invented metrics
The runtime design centers on explicit trade-offs — not benchmark claims.
Throughput ↔ Latency
Longer batching windows may improve efficiency but increase waiting and time-to-first-token.
Model locality ↔ Worker load
A worker with the model already resident may have a longer queue, while an idle worker may require an expensive model load.
Efficiency ↔ Starvation
Reordering and grouping requests may improve batching and locality, but unpopular models must not wait indefinitely.
Engineering highlights
Architecture principles
Policy, bounded execution, and evaluable baselines.
Highlight 01
Policy ≠ mechanism
Scheduler, router, model management, and execution have explicit responsibilities — runtime policy stays separate from model computation.
Highlight 02
Synthetic + real execution
The architecture separates deterministic infrastructure evaluation from eventual real-model execution behind a backend boundary.
Highlight 03
Bounded systems
Queues and backpressure are part of the runtime design so overload is handled explicitly rather than by allowing unbounded backlog.
Demo coming soon