ai startup development

Speculative Decoding on Android: Running Draft-and-Verify LLM Inference On-Device with Dual GGUF Models and the Token Acceptance Pipeline That Doubles Generation Speed

Krystian Wiewiór · Apr 24, 2026 · 1 min read

Tags: android, kotlin, mobile, architecture, kmp

Speculative Decoding on Android: Running Draft-and-Verify LLM Inference On-Device with Dual GGUF Models and the Token Acceptance Pipeline That Doubles Generation Speed

Implementing speculative decoding on-device using a small draft model (0.5B) paired with a larger target model (8B), covering the parallel verification algorith

Apr 24, 2026 · 1 min read

ai startup development

Idempotent API Design for Mobile Payment Flows: Request Fingerprinting, Server-Side Deduplication Windows, and the Exactly-Once Architecture That Prevents Double Charges on Flaky Networks

Deep dive into implementing idempotency keys with server-side deduplication using PostgreSQL upserts and TTL-based cleanup, client-side retry strategies with Ok

Apr 23, 2026 · 5 min read

ai startup development

Kotlin/Native GC tuning that cut P99 latency by 60%

Deep dive into Kotlin/Native's modern memory manager internals — how the tracing GC with cycle collection actually works under the hood, practical mimalloc allo

Apr 23, 2026 · 5 min read

Speculative Decoding on Android: Running Draft-and-Verify LLM Inference On-Device with Dual GGUF Models and the Token Acceptance Pipeline That Doubles Generation Speed

Related Posts

Speculative Decoding on Android: Running Draft-and-Verify LLM Inference On-Device with Dual GGUF Models and the Token Acceptance Pipeline That Doubles Generation Speed

Idempotent API Design for Mobile Payment Flows: Request Fingerprinting, Server-Side Deduplication Windows, and the Exactly-Once Architecture That Prevents Double Charges on Flaky Networks

Kotlin/Native GC tuning that cut P99 latency by 60%