Interactive LLMs (chat, copilots, agents) with strict latency targets Long‑context reasoning (codebases, research, video) with massive KV (key value) cache footprints Ranking and recommendation models ...
Google researchers have published a new quantization technique called TurboQuant that compresses the key-value (KV) cache in large language models to 3.5 bits per channel, cutting memory consumption ...
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New AI techniques slash LLM memory use and costs
TurboQuant breakthrough: Google's TurboQuant compresses LLM KV-cache up to 6x without quality loss, freeing GPU memory and boosting inference speed. Hybrid attention savings: DeltaNet-style ...
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