Omni Model Inference: How We Move Tensors Between Stages | LinkedIn

A friend once asked me: what's the fundamental difference between serving Omni multimodal models and serving plain LLMs? I thought about it and the simplest way to put it is this — a regular LLM handles a request with a single model in a single process; an Omni model handles a request by relaying it

A friend once asked me: what's the fundamental difference between serving Omni multimodal models and serving plain LLMs? I thought about it and the simplest way to put it is this — a regular LLM handles a request with a single model in a single process; an Omni model handles a request by relaying it across multiple models. Take Qwen3 Omni as an example. The lifecycle of a voice conversation request looks roughly like this: the user sends an audio clip, which first passes through an audio encoder that converts the waveform into embeddings, then feeds into the Thinker (a large language model) fo

saved by

• surya

related reading

• Transformer Inference Arithmetic | kipply's blogkipp.ly
• M*: A Modular, Extensible, Serving System for Multimodal Modelsai.stanford.edu
• LLM Inference Performance Engineering: Best Practices | Databricks Blogdatabricks.com
• Together AI | The AI Native Cloudtogether.ai
• Transformer Math 101 | EleutherAI Blogblog.eleuther.ai
• Reiner Pope – The math behind how LLMs are trained and serveddwarkesh.com
• All About Transformer Inferencejax-ml.github.io
• M*: One Serving System for Any-to-Any Multimodal Modelsmstar.stanford.edu
• ⭐️ Fast LLM Inference From Scratchandrewkchan.dev
• Real-time LLM Inference on Standard Datacenter GPUs (3,000 tokens/s per request)blog.kog.ai
• How LLM Inference Worksarpitbhayani.me
• Keep the Tokens Flowing: Lessons from 16 Open-Source RL Librarieshuggingface.co