OSCAR KV Cache Compression

OSCAR KV Cache Compression: Book Excerpts and Blog Articles

Free online book excerpts with full text chapters online and free PDF downloads, and the Aussie AI blog, including related articles:

• KV cache quantization is the application of quantization optimizations to the stored caches of KV data, which are used in the QKV attention modules of LLMs. The KV cache grows linearly with the number of input tokens, which means that long prompts or outputs can result in large caches, chewing up memory. Reducing the KV cache to fewer bits allows it to be more easily stored in memory (or disk), and to be processed faster in attention kernels. It is reasonably common to use 4-bit KV caches (KV4) for quantization. ... more about KV cache quantization »

Research on OSCAR KV Cache Compression

Research papers include:

• Zhongzhu Zhou, Donglin Zhuang, Jisen Li, Ziyan Chen, Shuaiwen Leon Song, Ben Athiwaratkun, Xiaoxia Wu, 18 May 2026, OSCAR: Offline Spectral Covariance-Aware Rotation for 2-bit KV Cache Quantization, https://arxiv.org/abs/2605.17757 https://oscar-quantize.github.io/index.html https://github.com/ZunhaiSu/OScaR-KV-Quant (INT2 KV cache compression.)
• KiaDev, May 2026, OSCAR: Attention-Aware 2‑Bit KV Cache for LLMs, https://www.kiadev.net/news/2026-05-25-oscar-2bit-kv-cache
• Editorial Team, 25 May 2026, Together AI Open-Sources OSCAR for 2-Bit KV Cache, https://news.skrew.ai/together-ai-oscar-2bit-kv-cache-quantization/
• Chat Forest, May 26, 2026 Together AI Open-Sources OSCAR: 5× Less KV Cache Memory, Near-Zero Accuracy Loss, https://chatforest.com/builders-log/together-ai-oscar-2bit-kv-cache-quantization-llm-serving/

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