Skip to main content Tech Radar Pro Tech Radar Gaming Tech Radar Pro TechRadar the business technology experts Search TechRadar View Profile België (Nederlands) Deutschland North America US (English) Australasia New Zealand Expert Insights Website builders Web hosting Best web hosting Best website builder Best office chairs Expert Insights Artificial Intelligence The race to trillion-parameter model training in AI is on, and this company thinks it can manage it for less than $100,000 Efosa Udinmwen 26 March 2025 Trillion-parameter AI models expected before 2026 as sizes keep growing When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works. (Image credit: Shutterstock / Ryzhi) Phison’s SSD strategy slashes AI training costs from $3 million to $100,000 aiDAPTIV+ software shifts AI workloads from GPUs to SSDs efficiently SSDs could replace costly GPUs in massive AI model training The development of AI models has become increasingly costly as their size and complexity grow, requiring massive computational resources with GPUs playing a central role in handling the workload. Phison, a key player in portable SSDs, has unveiled a new solution that aims to drastically reduce the cost of training a 1 trillion parameter model by shifting some of the processing load from GPUs to SSDs, bringing the estimated $3 million operational expense down to just $100,000. Phison's strategy involves integrating its aiDAPTIV+ software with high-performance SSDs to handle some AI tool processing tasks traditionally managed by GPUs while also incorporating NVIDIA’s GH200 Superchip to enhance performance and keep costs manageable. AI model growth and the trillion-parameter milestone Phison expects the AI industry to reach the 1 trillion parameter milestone before 2026. According to the company, model sizes have expanded rapidly, moving from 69 billion parameters in Llama 2 (2023) to 405 billion with Llama 3.1 (2024), followed by DeepSeek R3’s 671 billion parameters (2025). If this pattern continues, a trillion-parameter model could be unveiled before the end of 2025, marking a significant leap in AI capabilities. In addition, it believes that its solution can significantly reduce the number of GPUs needed to run large-scale AI models by shifting some of the processing tasks away from GPUs to the largest SSDs and this approach could bring down training costs to just 3% of current projections (97% savings), or less than 1/25 of the usual operating expenses. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news and offers from other Future brandsReceive email from us on behalf of our trusted partners or sponsorsBy submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over. Phison has already collaborated with Maingear to launch AI workstations powered by Intel Xeon W7-3455 CPUs, signaling its commitment to reshaping AI hardware. As companies seek cost-effective ways to train massive AI models, innovations in SSD technology could play a crucial role in driving efficiency gains while external HDD options remain relevant for long-term data storage. The push for cheaper AI training solutions gained momentum after DeepSeek made headlines earlier this year when its DeepSeek R1 model demonstrated that cutting-edge AI could be developed at a fraction of the usual cost, with 95% fewer chips and reportedly requiring only $6 million for training. Via Tweaktown You may also like These are the best AI website builders around Take a look at the best firewalls on offer right now Seagate teams with Nvidia to build an NVMe hard drive proof of concept, more than 3 years after its last effort See more Computing News Efosa Udinmwen Social Links Navigation Freelance Journalist Efosa has been writing about technology for over 7 years, initially driven by curiosity but now fueled by a strong passion for the field. He holds both a Master's and a PhD in sciences, which provided him with a solid foundation in analytical thinking. Efosa developed a keen interest in technology policy, specifically exploring the intersection of privacy, security, and politics. His research delves into how technological advancements influence regulatory frameworks and societal norms, particularly concerning data protection and cybersecurity. Upon joining TechRadar Pro, in addition to privacy and technology policy, he is also focused on B2B security products. Efosa can be contacted at this email: udinmwenefosa@gmail.com You must confirm your public display name before commenting Please logout and then login again, you will then be prompted to enter your display name. 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