IBM and Lenovo today announced at LEAP 2025 a planned expansion of their strategic technology partnership designed to help scale the impact of generative AI for clients in the Kingdom of Saudi Arabia. IDC expects annual worldwide spending on AI-centric systems to surpass $300 billion by 2026, with many leading organizations in Saudi Arabia exploring and investing in generative AI use cases as they prepare for the emergence of an "AI everywhere" world.

Building upon their 20-year partnership, IBM and Lenovo will collaborate to deliver AI solutions comprised of technology from the IBM watsonx portfolio of AI products, including the Saudi Data and Artificial Intelligence Authority (SDAIA) open-source Arabic Large Language Model (ALLaM), and Lenovo infrastructure. These solutions are expected to help government and business clients in the Kingdom to accelerate their use of AI to improve public services and make data-driven decisions in areas such as fraud detection, public safety, customer service, code modernization, and IT operations.

At CES 2025, Qualcomm Technologies, Inc., today announced Qualcomm AI On-Prem Appliance Solution, an on-premises desktop or wall-mounted hardware solution, and Qualcomm AI Inference Suite, a set of software and services for AI inferencing spanning from near-edge to cloud. The combination of these new offerings allows for small and medium businesses, enterprises and industrial organizations to run custom and off-the-shelf AI applications on their premises, including generative workloads. Running AI inference on premises can deliver significant savings in operational costs and overall total cost of ownership (TCO), compared to the cost of renting third-party AI infrastructure.

Using the AI On-Prem Appliance Solution in concert with the AI Inference Suite, customers can now use generative AI leveraging their proprietary data, fine-tuned models, and technology infrastructure to automate human and machine processes and applications in virtually any end environment, such as retail stores, quick service restaurants, shopping outlets, dealerships, hospitals, factories and shop floors - where the workflow is well established, repeatable and ready for automation.

IBM and AMD have announced a collaboration to deploy AMD Instinct MI300X accelerators as a service on IBM Cloud. This offering, which is expected to be available in the first half of 2025, aims to enhance performance and power efficiency for Gen AI models such as and high-performance computing (HPC) applications for enterprise clients. This collaboration will also enable support for AMD Instinct MI300X accelerators within IBM's watsonx AI and data platform, as well as Red Hat Enterprise Linux AI inferencing support.

"As enterprises continue adopting larger AI models and datasets, it is critical that the accelerators within the system can process compute-intensive workloads with high performance and flexibility to scale," said Philip Guido, executive vice president and chief commercial officer, AMD. "AMD Instinct accelerators combined with AMD ROCm software offer wide support including IBM watsonx AI, Red Hat Enterprise Linux AI and Red Hat OpenShift AI platforms to build leading frameworks using these powerful open ecosystem tools. Our collaboration with IBM Cloud will aim to allow customers to execute and scale Gen AI inferencing without hindering cost, performance or efficiency."

IBM applauds the EU Parliament's decision to adopt the EU AI Act, a significant milestone in establishing responsible AI regulation in the European Union. The EU AI Act provides a much-needed framework for ensuring transparency, accountability, and human oversight in developing and deploying AI technologies. While important work must be done to ensure the Act is successfully implemented, IBM believes the regulation will foster trust and confidence in AI systems while promoting innovation and competitiveness.

"I commend the EU for its leadership in passing comprehensive, smart AI legislation. The risk-based approach aligns with IBM's commitment to ethical AI practices and will contribute to building open and trustworthy AI ecosystems," said Christina Montgomery, Vice President and Chief Privacy & Trust Officer at IBM. "IBM stands ready to lend our technology and expertise - including our watsonx.governance product - to help our clients and other stakeholders comply with the EU AI Act and upcoming legislation worldwide so we can all unlock the incredible potential of responsible AI." For more information, visit watsonx.governance and ibm.com/consulting/ai-governance.

IBM announced the availability of the popular open-source Mixtral-8x7B large language model (LLM), developed by Mistral AI, on its watsonx AI and data platform, as it continues to expand capabilities to help clients innovate with IBM's own foundation models and those from a range of open-source providers. IBM offers an optimized version of Mixtral-8x7B that, in internal testing, was able to increase throughput—or the amount of data that can be processed in a given time period—by 50 percent when compared to the regular model. This could potentially cut latency by 35-75 percent, depending on batch size—speeding time to insights. This is achieved through a process called quantization, which reduces model size and memory requirements for LLMs and, in turn, can speed up processing to help lower costs and energy consumption.

The addition of Mixtral-8x7B expands IBM's open, multi-model strategy to meet clients where they are and give them choice and flexibility to scale enterprise AI solutions across their businesses. Through decades-long AI research and development, open collaboration with Meta and Hugging Face, and partnerships with model leaders, IBM is expanding its watsonx.ai model catalog and bringing in new capabilities, languages, and modalities. IBM's enterprise-ready foundation model choices and its watsonx AI and data platform can empower clients to use generative AI to gain new insights and efficiencies, and create new business models based on principles of trust. IBM enables clients to select the right model for the right use cases and price-performance goals for targeted business domains like finance.