InterDigital
48.1336951,-1.6190308
Summary
Multi-Modal Large Language Models (MLLMs) are increasingly capable of processing and reasoning over diverse input modalities such as text, images, audio, and video. However, running such models in real-time or resource-constrained environments poses significant challenges in terms of bandwidth and compute requirements.
Distributing the processing of models between client and server (a "distributed computing" approach) is a promising solution. While traditional distributed inferencing has been applied successfully to DNN models, extending this paradigm to MLLMs is a novel and impactful use case.
This internship aims to demonstrate the feasibility of distributed MLLMs inference approach, where MLLM components are distributed across two endpoints, coordinated through an agentic orchestration.
Responsibilities
The internship will be involved in the following tasks:
Survey the recent advances in MLLMs,
Select representative models, and a representative agentic...
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Qualifications
Education: Master's student in Computer Science, Artificial Intelligence, Data Science, or related field.
Skills:
Background in AI/ML, particularly large language models.
Knowledge of multi-modal systems (text, vision, speech).
Proficiency in Python programming and ML frameworks (PyTorch).
Ability to conduct research and prototype efficiently.
Nice to have: familiarity with distributed systems, networking, bandwidth concepts, ONNX framework
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| Durée (Mois): |
6
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