Researchers have developed a method to distribute large language models across multiple Intel AI PCs, allowing for faster and more efficient inference. The technique, known as pipeline parallelism, involves splitting the model into smaller shards that can be processed in parallel on different machines. This approach leverages OpenVINO's GPU optimization and speculative decoding to achieve speeds comparable to a single, monolithic model. The method also allows for micro-batching, where multiple users' requests are interleaved across stages, increasing throughput. The researchers demonstrate the effectiveness of their technique using large language models such as Llama 3.1 and Lunar Lake. --- Why it matters: This work matters because it addresses a significant challenge in AI: scaling large language models to handle concurrent inference tasks on distributed hardware. By enabling efficient parallelization of these models, researchers can unlock new applications and use cases that require high throughput and low latency. Source: https://arxiv.org/abs/2608.19147