Google introduces TurboQuant, a compression method that reduces memory usage and increases speed ...

Google Research recently revealed TurboQuant, a compression algorithm that reduces the memory footprint of large language ...

Microsoft once tried to reduce Windows RAM usage by 20 percent but failed. Now Windows 11 may finally fix memory issues in ...

Those memory numbers don't mean what you think.

SEOUL, South Korea, March 5, 2026 /PRNewswire/ -- Nota AI, an AI optimization technology company behind the Nota AI brand, announced that it has developed a next-generation quantization technology ...

NVIDIA shows neural rendering cuts VRAM use, reduces game storage, and improves performance without changing visual quality ...

The biggest memory burden for LLMs is the key-value cache, which stores conversational context as users interact with AI ...

NVIDIA showcases Neural Texture Compression at GTC 2026, cutting VRAM usage by up to 85% with real-time AI reconstruction.

On March 24, 2026, Google Research announced a new suite of compression techniques for large-scale language models and vector search engines: TurboQuant, PolarQuant, and Quantized ...

Fine-tuning large language models in artificial intelligence is a computationally intensive process that typically requires significant resources, especially in terms of GPU power. However, by ...

The compression algorithm works by shrinking the data stored by large language models, with Google’s research finding that it can reduce memory usage by at least six times “with zero accuracy loss.” [ ...

Neural Texture Compression (NTC) optimized memory usage for either neural rendering or high-resolution texture and game data.