Executive Summary
- Google Research announce TurboQuant, a compression algorithm that could solve the RAMpocalypse and AI’s memory demand.
- The algorithm compresses vectors and reduces the memory size of AI workloads by at least six times, with zero accuracy loss.
- If this new innovation greatly reduces the need for memory within AI inference models, this could have a huge impact on the data centre industry.
The RAM shortages have affected numerous industries as well as the general public, with supply chains in crisis, a rise in demand thanks to AI workloads and price hikes; we reported on this a few months ago and we examined the reasons why we have a global memory shortage.
Now, it seems Google has the answer to the problem. On March 24th 2026, Google Research published an article on TurboQuant, a compression algorithm that addresses the challenge of memory overhead for AI models and with zero accuracy loss. Research Scientist Amir Zandieh and VP and Google Fellow, Vahab Mirrokni, introduce TurboQuant as the solution to the RAMpocalypse, stating it shows great promise for reducing key-value bottlenecks without sacrificing AI model performance. They write that it reduced the key-value memory size by a factor of at least six times.
What does this mean?
If this compression algorithm does what they say it does, it will reduce memory requirements in AI inference workloads, dismantling the global memory shortage and making memory and storage even more useful while increasing AI workflows.
How does it work?
AI models need vectors for understanding and processing information, but they require a lot of memory to function. This reduces the demand for RAM and greatly increases the opportunities with memory and drastically changes the AI server market, which would affect the data centre industry.
The details of it are quite technical, but TurboQuant, through rigorous testing, proved it can quantise the key-value cache to just 3 bits without requiring training or fine-tuning and causing any compromise with model accuracy, and has a faster runtime to boot.
Impacting the data centre industry
The demand for AI will skyrocket once more should this compression algorithm deliver on its promises, and make new projects a lot more viable, especially edge data centres. It would mean a huge shift in hardware demand adn data centres could use the inference that is 6x cheaper and faster to run more complicated agentic AI workflows.
Furthermore, reducing the amount of data between the GPU and memory lowers the PUE for inference workloads, making operations much more efficient. There are more ways this could drastically impact the industry, but we will wait for more details and analyse then.
The RAM stock market has since plummeted in the days following the announcement and whilst Google have put the new innovation through its paces, it’s not a done deal just yet, and we will need to wait for more information, which will be presented at ICLR this month in Brazil.
