Speculative decoding is a prominent technique for accelerating LLM inference by leveraging an auxiliary draft model, but its effectiveness is limited by the autoregressive nature of draft generation, where acceptance rates depend on the draft model’s size. Scaling the draft model improves acceptance but also increases speculation latency, limiting overall speedup. Furthermore, fine-tuning both the draft and target models is often necessary to achieve high acceptance rates, adding complexity to inference systems as the number of downstream tasks grows. Single-model approaches like Medusa generate speculative tokens non-autoregressively but lack token dependencies, limiting effectiveness. Alternatives like Hydra and Eagle incorporate token dependencies but rely on dedicated heads, making speculation independent of the base model and limiting the extent to which stronger base models can improve speculation.We introduce a novel speculative decoding method that integrates speculative draft generation directly within the target model using multi-stream attention. This improves acceptance rates by introducing interdependencies between speculative tokens while ensuring non-autoregressive draft generation with minimal overhead. As target models scale in size and quality, speculative generation improves naturally with our method, unlike prior approaches. Furthermore, our approach is both parameter- and FLOP-efficient, requiring over 1000X fewer additional parameters than Medusa, making it highly suitable for resource-constrained devices. We design our method to operate in two modes: (1) Lossless mode, a plug-and-play method that preserves the output of any pre-trained model; and (2) Shared mode, optimizing both speedup and downstream performance. We demonstrate a 2–3.5X speedup across diverse tasks, including summarization, translation, question answering, mathematical reasoning, SQL generation, and retrieval-augmented generation (RAG).

With increasingly more powerful compute capabilities and resources in today’s devices, traditionally compute-intensive automatic speech recognition (ASR) has been moving from the cloud to devices to better protect user privacy. However, it is still challenging to implement on-device ASR on resource-constrained devices, such as smartphones, smart wearables, and other small home automation devices. In this paper, we propose a series of model architecture adaptions, neural network graph transformations, and numerical optimizations to fit an advanced Conformer based end-to-end streaming ASR system on resource-constrained devices without accuracy degradation. We achieve over 5.26 times faster than realtime (0.19 RTF) speech recognition on small wearables while minimizing energy consumption and achieving state-of-the-art accuracy. The proposed methods are widely applicable to other transformer-based server-free AI applications. In addition, we provide a complete theory on optimal pre-normalizers that numerically stabilize layer normalization in any L_p-norm using any floating point precision.