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Covers 7 stories See all stories this covers including Gemma 4 QAT models: Optimizing model compression for mobile and laptop efficiency

Covers 7 related stories

blog.google·

Gemma 4 QAT models: Optimizing model compression for mobile and laptop efficiency

Discussed on Hacker News
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GitHub·

ggerganov/llama.cpp

Discussed on r/LocalLLaMA and DEV
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arxiv.org·

KVarN: Variance-Normalized KV-Cache Quantization Mitigates Error Accumulation in Reasoning Tasks

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GitHub·

huawei-csl/KVarN: KVarN is a native vLLM KV-cache quantization backend for your agents: 3-5x more context, throughput above FP16, and FP16-level accuracy. Calibration-free, one flag.

Discussed on Hacker News
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huggingface.co·

nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16

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unsloth.ai·

Unsloth Gemma 4 QAT

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vllm.ai·

A First Comprehensive Study of TurboQuant: Accuracy and Performance

Discussed on r/LocalLLaMA
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