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Hugging Face implemented dynamic LoRA loading on its Inference API, where a single base model remains active while different LoRA adapters are swapped in and out for each user request instead of maintaining separate deployments for each adapter. The warm-up time for serving a LoRA decreased from 25 seconds to 3 seconds, and overall response time dropped from 35 seconds to 13 seconds, while the system now serves hundreds of distinct LoRAs on fewer than 5 A10G GPUs. This approach reduced compute resource requirements since the vast majority of the 2,500 public LoRAs share only a few base models, allowing efficient reuse of GPU capacity across different adapter requests.
Hugging Face released Optimum-NVIDIA, an inference library that accelerates large language model performance on NVIDIA GPUs through a modified single line of code. The library achieves up to 28 times faster throughput and generates 1,200 tokens per second using FP8 quantization on Ada Lovelace and Hopper architectures. Currently supporting LLaMA-based models, the library will expand to other architectures while adding techniques like In-Flight Batching and INT4 quantization.
AMD and Hugging Face expanded their partnership to enable large language models to run on AMD Instinct server GPUs without requiring code changes compared to NVIDIA equivalents. An AMD Instinct MI250 GPU with 128GB of memory delivers 2.33x more decode throughput and half the prefill latency of an NVIDIA A100, while also fitting larger workloads that exceed the A100's 80GB capacity. Text Generation Inference is now available in production on AMD Instinct GPUs, with future work planned for AMD Radeon consumer GPUs and Ryzen AI laptop processors.
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