Chunked Prefill: Hitting Sub-300ms TTFT on Android

Meta description: How chunked prefill and Vulkan dispatch tuning keep Android LLM first-token latency under 300ms on Snapdragon 8 Gen 3 without killing decode throughput.

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TL;DR

Chunked prefill splits long system prompts into fixed-size token blocks processed across multiple UI frames, interleaving decode steps so your app stays responsive while the KV cache populates. On Snapdragon 8 Gen 3, tuning Vulkan compute dispatch granularity and work queue depth gets Time-To-First-Token (TTFT) consistently under 300ms without sacrificing decode throughput.

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Why prefill kills mobile TTFT

The difference between 820ms and 265ms TTFT on the same hardware comes down to one scheduling decision: whether you process your prompt as a monolithic GPU dispatch or chunk it across frames.

Before generating a single output token, the model must process your entire prompt — system context, conversation history, persona instructions — and populate the KV cache. On a long prompt this is a sequential, compute-heavy operation that blocks your decode pipeline and stalls the UI thread long enough to trigger dropped frames.

Most teams get this wrong: they optimize decode throughput aggressively, ignore prefill scheduling entirely, then ship an app where the first token takes 800ms+ on a cold context. Users read this as the model “thinking forever.”

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The architecture: chunked prefill

Instead of processing 2,048 prompt tokens in one monolithic GPU dispatch, partition the sequence into fixed-size chunks — typically 128-256 tokens — and process each chunk on a separate Vulkan compute dispatch submitted across consecutive frames.

data class PrefillConfig(
    val chunkSize: Int = 128,
    val interleaveDecodeAfter: Int = 4, // chunks before decode step
    val maxQueueDepth: Int = 2
)

fun scheduleChunkedPrefill(
    tokens: IntArray,
    config: PrefillConfig,
    onChunkComplete: (chunkIndex: Int, kvCacheReady: Boolean) -> Unit
) {
    val chunks = tokens.asList().chunked(config.chunkSize)
    chunks.forEachIndexed { index, chunk ->
        vulkanDispatcher.submitPrefillChunk(chunk)
        if ((index + 1) % config.interleaveDecodeAfter == 0) {
            onChunkComplete(index, index == chunks.lastIndex)
        }
    }
}

The interleave cadence matters more than chunk size. Yielding to a decode step every 4 chunks lets you show streaming output — or at minimum an animated indicator — while prefill is still in progress. Users perceive TTFT as the moment something appears, not when the KV cache is fully populated.

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Vulkan dispatch granularity and queue depth

On Snapdragon 8 Gen 3, the Adreno 750 GPU handles Vulkan compute workgroups efficiently at 64-128 threads. Oversized dispatches saturate the command queue and starve the render pipeline. The numbers:

Chunk Size (tokens)	Avg TTFT (ms)	Decode Throughput (tok/s)	Frame Drops (per session)
2048 (monolithic)	820	18.2	11
512	460	17.8	4
256	310	17.1	1
128	265	16.4	0

Test conditions: Llama 3.2 3B, Q4_K_M quantization, 512-token system prompt, device at sustained thermal state (~42°C skin temp), Snapdragon 8 Gen 3 reference device.

128-token chunks hit sub-300ms TTFT with zero perceptible frame drops. The throughput penalty (~10%) is a deliberate trade-off — fine for interactive sessions where responsiveness outweighs raw speed.

Queue depth matters too. Keeping maxQueueDepth at 2 prevents the GPU driver from buffering multiple chunks ahead of the CPU, which would reintroduce latency spikes when the render thread competes for command buffer submission slots.

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Android thread scheduling

Schedule Vulkan submissions on a dedicated HandlerThread pinned to performance cores. One gotcha: set thread priority from within the target thread using Process.setThreadPriority() without arguments — this targets the calling thread’s TID, not the spawning thread’s:

val inferenceThread = object : HandlerThread("InferenceWorker") {
    override fun onLooperPrepared() {
        Process.setThreadPriority(Process.THREAD_PRIORITY_URGENT_DISPLAY)
    }
}.apply { start() }

Without explicit priority, the scheduler migrates compute work to efficiency cores under thermal pressure — exactly when TTFT matters most. Pair this with Window.setSustainedPerformanceMode(true) during active inference sessions to prevent thermal throttling from collapsing clock speeds mid-prefill.

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What to ship this week

Sub-300ms TTFT on Android is an engineering problem, not a hardware lottery. Three changes that actually move the number:

1. Chunk your prefill at 128-256 tokens per dispatch. Submit each chunk as a separate Vulkan compute command. Interleave a decode step every 4 chunks — this drives perceived latency below actual KV cache completion time.

2. Pin your inference thread to performance cores and call Process.setThreadPriority(THREAD_PRIORITY_URGENT_DISPLAY) from within the thread. Thermal migration to efficiency cores is the single largest source of latency variance in production. Sustained performance mode is not optional.

3. Profile queue depth, not just chunk size. A maxQueueDepth of 2 prevents driver-side buffering from reintroducing the scheduling contention you eliminated with chunking. Instrument with Android GPU Inspector before shipping.

The benchmark above shows what’s achievable at Q4_K_M on a 3B model. The teams shipping the most responsive on-device AI aren’t running lighter models — they’re running better schedulers.

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#android #mobile #kotlin #architecture