Google Introduces TurboQuant Algorithm Reducing LLM Memory Usage by 6x

Mar 28, 2026

Google released TurboQuant in March 2026, a compression algorithm that reduces large language model memory usage during inference by at least 6x while maintaining quality at 3-bit precision without requiring model retraining. The algorithm combines PolarQuant coordinate conversion with Quantized Johnson-Lindenstrauss error-correction and achieved 8x speedup on Nvidia H100 accelerators, with developers porting it to local frameworks like MLX within 24 hours. The announcement triggered significant stock declines among memory chip manufacturers, with SK Hynix falling 6.4% and Samsung dropping nearly 5%, though analysts disagreed on long-term impact—Bloomberg Intelligence and Morgan Stanley argued HBM demand would remain unaffected while JPMorgan cited Jevons Paradox to suggest efficiency gains would ultimately increase total memory consumption. Community benchmarks demonstrated the Qwen3.5-35B model running at 2.5-bit precision across 64,000 tokens with perfect accuracy, with NAND flash manufacturers like Kioxia and Sandisk absorbing the worst damage as TurboQuant directly threatens their market segment.

Google's TurboQuant Algorithm and Technical Innovation

▪A community benchmark ran the Qwen3.5-35B model at 2.5-bit TurboQuant across context lengths up to 64,000 tokens with perfect accuracy.
▪Google released TurboQuant publicly with no licensing restrictions and no retraining requirement.
▪Google released the TurboQuant compression algorithm in March 2026.
▪TurboQuant achieved an 8x speedup in computing attention logits on Nvidia H100 accelerators.
▪Developers ported TurboQuant to local AI frameworks including MLX for Apple Silicon within 24 hours of release.
▪TurboQuant enables AI models to run at 3-bit precision with no quality loss and no retraining.
▪TurboQuant uses Quantized Johnson-Lindenstrauss technique to apply 1-bit error-correction to clean up inaccuracies from PolarQuant.

Market Impact on Memory Chip Manufacturers

▪Samsung shares dropped nearly 5% following Google's TurboQuant announcement.
▪SK Hynix shares fell as much as 6.4% on the Korea Exchange following Google's TurboQuant announcement.
▪Kioxia stock had surged over 700% since August on AI-fuelled optimism before declining after TurboQuant announcement.

Analyst Perspectives on Long-term Demand Implications

▪Bloomberg Intelligence analyst Jake Silverman stated that HBM demand and DRAM made by Micron would likely be unaffected by TurboQuant.
▪Quilter Cheviot analyst Ben Barringer characterized TurboQuant as evolutionary rather than revolutionary.

Practical Implementation and Developer Adoption

▪Developers ported TurboQuant to local AI frameworks within 24 hours of Google's public release.

Perspective of NAND flash memory manufacturers (Kioxia, Sandisk)

▪Kioxia's stock decline following TurboQuant announcement erased gains from an over 700% surge since August driven by AI optimism.
▪TurboQuant's 6x memory reduction directly threatens the NAND flash memory market segment used for AI model storage.

Perspective of HBM and DRAM manufacturers (SK Hynix, Samsung, Micron)

▪Bloomberg Intelligence analyst Jake Silverman assessed that Micron's DRAM and HBM product lines face no material threat from TurboQuant.

Perspective of Market analysts citing Jevons Paradox

▪SemiAnalysis analyst Ray Wang argued to CNBC that removing AI bottlenecks through TurboQuant will enable more capable models that eventually consume more memory.
▪Quilter Cheviot analyst Ben Barringer downplayed TurboQuant's market impact by characterizing it as evolutionary rather than revolutionary technology.

Perspective of Enterprises with data privacy requirements

▪The rapid port of TurboQuant to MLX framework for Apple Silicon within 24 hours enables privacy-focused organizations to deploy compressed models on local hardware immediately.