Tweet
[ul]
[li]Phison’s SSD strategy slashes AI training costs from $3 million to $100,000[/li][li]aiDAPTIV+ software shifts AI workloads from GPUs to SSDs efficiently[/li][li]SSDs could replace costly GPUs in massive AI model training[/li][/ul]
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.
[HEADING=1]AI model growth and the trillion-parameter milestone[/HEADING]
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.
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
[HEADING=2]You may also like[/HEADING]
[ul]
[li]These are the best AI website builders around[/li][li]Take a look at the best firewalls on offer right now[/li][li]Seagate teams with Nvidia to build an NVMe hard drive proof of concept, more than 3 years after its last effort[/li][/ul]
Continue reading…