IBM Research reduces AI chip design costs using open-source models and llm-d distributed inference
IBM Research demonstrates building an agent factory by combining open-source frontier models with llm-d distributed inference, performing HLS hardware optimization on the quantum error correction decoding algorithm Relay-BP, achieving approximately 52x acceleration and inference costs of about $26 under a budget of 100 assumptions; under the same budget, the full proprietary model configuration costs $299 and achieves 204x acceleration.
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