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How flash storage can cut GPU waste from KV-cache recomputation: LLM inference can waste up to 60% of GPU compute recomputing context. WEKA explains how KV-cache ...
venturebeat.com/ai/up-to-60-of-gpu-compute-goes-to-recomputing-context-flash-storage-can-put-that-capacity-back-to-work

GitHub - RedToasty/llama.cpp_qts: Fixing --split-mode tensor, with different KV cache quantization types.
github.com/RedToasty/llama.cpp_qts

TurboQuant - Extreme KV Cache Quantization · ggml-org/llama.cpp · Discussion #20969
github.com/ggml-org/llama.cpp/discussions/20969
GitHub - jhammant/Turbo1bit: Turbo1Bit: Combining 1-bit LLM weights (Bonsai) with TurboQuant KV cache compression for maximum inference efficiency. 4 ...
github.com/jhammant/Turbo1bit

GitHub - joshuagamboa/turboquant-apple-silicon: High-performance Rust integration for aggressive KV cache quantization on Apple Silicon GPUs (Metal) ...
github.com/joshuagamboa/turboquant-apple-silicon

GitHub - quantumaikr/TurboQuant.cpp: turboquant.cpp: turboquant.cpp. Contribute to quantumaikr/TurboQuant.cpp development by creating an account on GitHub.
github.com/quantumaikr/TurboQuant.cpp

What Google's TurboQuant can and can't do for AI's spiraling cost: Google's real-time quantization could be important for running local AI. Here's why.
zdnet.com/article/what-googles-turboquant-can-and-cant-do-for-ais-spiraling-cost/

GitHub - Memoriant/dgx-spark-kv-cache-benchmark: KV cache quantization benchmarks on NVIDIA DGX Spark GB10 — three novel findings including the dequantization ...
github.com/Memoriant/dgx-spark-kv-cache-benchmark

GitHub - cenconq25/delta-compress-llm: Exploiting temporal coherence in LLM inference-- delta encoding for KV cache compression and weight-skip prediction ...
github.com/cenconq25/delta-compress-llm
GitHub - samfurr/foveated_kv: Importance-adaptive mixed-precision KV cache compression for LLM inference on Apple Silicon.
github.com/samfurr/foveated_kv

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