Unlocking LLMs: How Intel, Lamini, and AMD are driving efficiency and customization
Faced with costly and scarce Nvidia GPUs, AI teams are embracing alternatives that expand access to LLM capabilities. BigDL-LLM, an open source library, optimizes model performance on Intel hardware to unlock new AI applications. Lamini collaborated with AMD to build the LLM Superstation, leveraging AMD’s ROCm software and Instinct GPUs to efficiently fine-tune massive models. As LLMs grow more powerful, optimizations that increase efficiency and access will be key to realizing their potential.
BigDL-LLM is an open-source library from Intel that optimizes LLMs on Intel XPUs, ranging from laptops to GPUs to the cloud. It uses low-bit optimizations to achieve low-latency performance of models like LLaMA2. BigDL-LLM was recently expanded to support Intel Arc graphics and data center GPUs, facilitating seamless functionality of models based on PyTorch APIs across Intel platforms. Accelerated examples help developers get started quickly. BigDL-LLM empowers wider access to performant LLMs on Intel hardware.
Demand for enterprise LLMs is surging, with over 5,000 companies joining Lamini’s waitlist. Customers like iFit and AMD highlight the need for customizable models trained on proprietary data. Lamini’s LLM Superstation combines easy infrastructure with AMD Instinct GPUs, optimized for private enterprise LLMs. Lamini exclusively runs on AMD for production LLMs. With software parity to CUDA achieved, the Instinct MI250’s large HBM capacity efficiently runs massive models. Benchmarking up to 166 TFLOP/s, ROCm demonstrates powerful performance for training. The Superstation’s attractive pricing lowers barriers to custom LLMs.
As LLMs grow more powerful, companies are optimizing software and hardware for efficiency, customization, and access. Intel’s BigDL-LLM unlocks model potential across devices. AMD GPUs enable Lamini to scale enterprise LLMs. Together, these advancements expand LLM capabilities to more organizations, accelerating the realization of AI’s transformative potential. Though challenges remain, commitment to optimization points towards a future where LLMs’ capabilities are open and available to all.
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