How to Reduce Code Completion Latency by 40% with Request Batching
Latency is the silent killer of AI coding assistant adoption. A completion tool that takes 800ms to respond will be disabled by developers within a week — no matter how accurate the suggestions are. At TryAICode, we obsess over P90 latency as our primary product health metric.
The Baseline Problem
When TryAICode launched in beta, our P50 completion latency was 210ms — acceptable. But our P90 was 480ms — painful. The 90th percentile matters more than the median because developers experience the bad cases disproportionately. A tool that is fast 80% of the time but slow 20% of the time feels slow.
The root cause was straightforward: we were processing each completion request sequentially through the same model serving infrastructure. During peak usage hours (9 AM–11 AM and 2 PM–4 PM ET), queue depth would spike and tail latencies would balloon.
Request Batching Architecture
Our first major optimization was dynamic request batching. Instead of processing completions as discrete single requests, we implemented a batching window: incoming requests within a 15ms window are grouped and processed together as a single forward pass through the model.
Transformer models are highly efficient at batch inference due to their parallelizable attention mechanisms. A single forward pass of batch size 8 takes roughly 1.4x the compute of a single-item pass, delivering 8x throughput at 1.4x cost. At scale, this fundamentally changes your serving economics.
Speculative Decoding
The second major optimization was speculative decoding. The technique uses a small draft model (7B parameters) to speculatively generate N tokens ahead, then verifies those tokens in parallel using the primary model (70B parameters). When the draft is correct (which happens roughly 78% of the time in our benchmarks), you receive multi-token completions at draft model speed.
Speculative decoding reduced our mean token generation latency by 32% while maintaining the accuracy profile of the primary model. Combined with request batching, our P90 latency dropped from 480ms to 285ms — a 41% improvement.
Results and Next Steps
After deploying both optimizations in August 2025, we saw immediate impact on developer retention metrics. Extension disable rates fell by 23% in the first two weeks. Completion acceptance rate increased from 91% to 95%, which we attribute to developers staying in flow state rather than dismissing suggestions due to wait time.
Our next latency initiative targets P99 reduction using adaptive model selection: routing simple completions (single-line, low context entropy) to the smaller draft model permanently, reserving the primary model for complex multi-line generations. We expect another 20% P90 reduction by Q1 2026.