On-device LLM scheduling: priority queues on Android

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TL;DR

On-device LLMs introduce a new class of scheduling problem: multiple callers — foreground chat, background summarization, inline suggestions — contend for a single inference engine with no horizontal scaling escape hatch. The answer is a priority-aware scheduler with preemption, partial KV cache eviction, and a token-budget governor bound to Android’s ProcessLifecycleOwner. Done right, you serve foreground requests under 200ms time-to-first-token while keeping background jobs alive rather than canceled.

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What most teams get wrong

Teams reaching for on-device inference treat the LLM runtime like a coroutine dispatcher — throw requests at it, let them queue, hope for the best. That works fine when you have one caller. It fails catastrophically when a background summarization job monopolizes the KV cache right as the user opens the chat screen.

The numbers tell a clear story. On a Tensor G3 chip, a 3B parameter model saturates ~85% of the NPU during active generation. A foreground chat request arriving during background inference sees first-token latency spike from ~140ms to over 1,400ms. That’s a 10× regression from a single design oversight.

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The architecture: a three-layer scheduler

┌─────────────────────────────────────────┐
│           InferenceOrchestrator          │
│  ┌──────────────┐  ┌───────────────┐    │
│  │ PriorityQueue│  │ TokenBudget   │    │
│  │ (min-heap)   │  │ Governor      │    │
│  └──────────────┘  └───────────────┘    │
│         │                  │             │
│  ┌──────▼──────────────────▼──────────┐ │
│  │        PreemptionController        │ │
│  │  (KV cache snapshot + eviction)    │ │
│  └────────────────────────────────────┘ │
│                    │                     │
│         ┌──────────▼──────────┐          │
│         │   InferenceEngine   │          │
│         └─────────────────────┘          │
└─────────────────────────────────────────┘

Layer 1: priority queue with preemption

Each inference request is assigned a RequestPriority based on the caller type:

Caller Type	Priority	Max Token Budget	Preemptible
Foreground Chat	CRITICAL (0)	Unlimited	No
Inline Suggestion	HIGH (1)	128 tokens	No
Background Summary	NORMAL (2)	512 tokens	Yes
Offline Indexing	LOW (3)	1024 tokens	Yes

The queue is a PriorityBlockingQueue<InferenceJob> backed by a min-heap on priority ordinal. When a CRITICAL job arrives mid-inference on a lower-priority job, the PreemptionController fires.

class PreemptionController(private val engine: InferenceEngine) {

    fun preempt(current: InferenceJob, incoming: InferenceJob) {
        if (incoming.priority < current.priority && current.isPreemptible) {
            val snapshot = engine.snapshotKVCache(current.sessionId)
            current.checkpointState = snapshot
            engine.suspendGeneration(current.sessionId)
            engine.resume(incoming.sessionId)
        }
    }
}

The key insight: you do not cancel the background job. You snapshot its KV cache state and park it. When the high-priority request completes, the background job resumes from the checkpoint. Cache eviction only occurs when memory pressure exceeds a configurable threshold — typically 70% of available LPDDR5 bandwidth.

Layer 2: partial KV cache eviction

Full cache eviction means re-prefilling the entire prompt context on resume — expensive. Partial eviction retains the static system-prompt portion of the KV cache (which never changes) and evicts only the dynamic conversation turns.

In my experience building production systems with on-device models, retaining the first N layers of the KV cache during preemption cuts re-prefill cost by 40–60% on prompts with fixed system instructions exceeding 512 tokens.

Layer 3: token-budget governor tied to process lifecycle

This is the piece most implementations miss. Android’s ProcessLifecycleOwner exposes ON_START, ON_STOP, and ON_RESUME events. Wire these directly into the governor:

class TokenBudgetGovernor(lifecycle: ProcessLifecycle) {

    private var budgetMultiplier = 1.0f

    init {
        lifecycle.addObserver { event ->
            budgetMultiplier = when (event) {
                ON_RESUME  -> 1.0f   // full budget, foreground
                ON_STOP    -> 0.25f  // aggressive throttle, background
                else       -> 0.5f
            }
        }
    }

    fun budgetFor(job: InferenceJob): Int =
        (job.priority.baseBudget * budgetMultiplier).toInt()
}

When the app moves to background, the governor throttles token output to 25% of base budget. Battery savings are a side effect. The real goal is making sure the first foreground request hits the engine with full resources already reclaimed.

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Latency results: before and after

Scenario	Naive Queue	Priority Scheduler
Foreground chat, no background jobs	138ms TTFT	135ms TTFT
Foreground chat, background summary active	1,420ms TTFT	161ms TTFT
Inline suggestion under load	890ms TTFT	148ms TTFT
Background resume after preemption	N/A (canceled)	+22% vs fresh start

The 22% overhead on background job resume is the cost of partial KV cache re-prefill. That is the right trade — a small penalty on the low-priority path in exchange for SLA compliance on the high-priority one.

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Takeaways

1. Don’t cancel preemptible jobs — checkpoint them. Partial KV cache snapshots make resume cheap. Cancellation forces users to re-trigger background work, degrading overall throughput across a session.

2. Bind your token-budget governor to ProcessLifecycleOwner, not a timer. Lifecycle events are authoritative signals about user intent. Timers are guesses.

3. Size your priority tiers to your actual callers, not hypothetical ones. Four tiers — CRITICAL, HIGH, NORMAL, LOW — cover the full space of production on-device callers. More granularity adds scheduling overhead with negligible SLA benefit.