Blog
Reshaping 32 query heads into [8, 4] instead of replicating 8 KV heads into 32 removed 60 tile operations and increased throughput from 20.39 to 54.30 tok/s.
Read moreThe same mapping, kv_head = query_head // group_size, takes different forms across backends. We compare pointer arithmetic, graph fan-out, and tensor shapes.
Read moreWhere asynchronous softmax is applied changes HMX allocation and measured TPS. We compare Custom Op and export-stage approaches.
Read moreThe same mapping, kv_head = query_head // group_size, takes different forms across backends. We compare pointer arithmetic, graph fan-out, and tensor shapes.
Read moreWhere asynchronous softmax is applied changes HMX allocation and measured TPS. We compare Custom Op and export-stage approaches.
Read moreValidating a custom kernel costs more than implementing it. We trace HMX allocation, quantization, HVX lane rearrangement, and accumulated error using measurements.
Read moreHow asynchronous softmax removes synchronization between blocks using a unified maximum while maintaining numerical safety through a fallback.
Read moreCore AI selects its compute device through shapes at export time rather than a runtime flag. We compare four execution paths on iPhone 17 Pro.
Read moreA structural comparison of how Apple Core AI redesigns graph capture, the KV cache, and the compiler stack relative to ANEMLL.
Read moreAn analysis of how Fisher-guided layer selection and Stiefel constraints can mitigate rank collapse in LoRA-based parameter-efficient fine-tuning.
Read moreLimited memory, power budgets, latency constraints, and heterogeneous hardware environments make device deployment fundamentally different from cloud inference.
Read moreA practical explanation of general-purpose compute, parallel acceleration, and dedicated neural processing for sustained local inference.
Read moreHow precision mapping, calibration, and mixed precision strategies reduce model size while preserving accuracy.
Read moreWhy dynamic model graphs often fail on edge runtimes, and how static shape transformation improves deployment stability.
Read moreOptHancer converts, compresses, and optimizes AI models for deployment across CPU, GPU, NPU, and embedded AI environments.
Read moreA technical overview of model compression, inference speed, context length, and accuracy validation for mobile sLM deployment.
Read moreHow KV cache design affects memory pressure, latency, and context length in on-device LLM execution.
Read moreLatency, privacy, cloud cost, and network dependency are pushing AI deployment from centralized servers to local devices.
Read moreHow mobile operators can use local inference for call intelligence, summarization, privacy-first assistants, and low-latency AI features.
Read moreHighlights from global exhibitions, partner meetings, and on-device AI demonstrations across telecom, OEM, and semiconductor ecosystems.
Read moreCompany updates, awards, and recognition related to OptAI’s hardware-aware model optimization technology.
Read moreA step-by-step view of model adaptation, quantization, compilation, and runtime optimization for real-device deployment.
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