Quantization

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UniSVQ: 2-bit Unified Scalar-Vector Quantization

 📊Vector Quantization  Content type: Academic
arxiv.org·

[AINews] not much happened today

 📉Technical Analysis  Content type: News
latent.space
·

mtmd : add video input support by ngxson · Pull Request #24269 · ggml-org/llama.cpp

 🎮Godot  Content type: Code
github.com··r/LocalLLaMA

Trainable Smooth-Rotation Transforms with Learned Channel Scales for LLM Quantization

 🎛️Fine-tuning  Content type: Academic
arxiv.org·

The Edge LLM Offload Story

 🤖AI
semiengineering.com·

LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization

 📊Vector Quantization  Content type: Academic
arxiv.org·

Show HN: Ext-Infer

 💬LLMs

Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression

 💬LLMs  Content type: Academic
arxiv.org·

mtp: support for gemma-4 E2B and E4B assistants by max-krasnyansky · Pull Request #24282 · ggml-org/llama.cpp

 💬LLMs  Content type: Code
github.com··r/LocalLLaMA

Google Gemma 4 12B: Architecture, Benchmarks, Access, and Hands-on Guide for Developers

 💬LLMs  Content type: Blog
analyticsvidhya.com·

FAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language Models

 🎛️Fine-tuning  Content type: Academic
arxiv.org·
Less-relevant results

[AINews] Reve 2 and Ideogram 4: Layouts in Imagegen

 🎮Reinforcement Learning
latent.space
·

On Low-Bit Quantization Errors in Speaker Verification: Diagnostic and Mitigation

 📊Vector Quantization  Content type: Academic
arxiv.org·

defai-digital/ax-engine: Apple Silicon LLM runtime supporting Gemma 4 and Qwen 3.6 MTP modes

 🤖AI  Content type: Code
github.com··Hacker News

not much happened today | AINews

 🔬Anthropic
news.smol.ai·

ScaleSweep: Accurate NVFP4 Post-Training Quantization of LLMs via Block Scale Initialization

 💬LLMs  Content type: Academic
arxiv.org·

bigattichouse/packed-twin-inference: PTI achieves ~2× throughput using a single quantized model (Q5_K_M or better) by running 4 generation streams in one batched decode call. The GPU loads model weights once per step and produces 4 predictions simultaneously. KV cache overhead is ~0.8 GiB total for all 4 streams. No draft model. No quality loss

 💬LLMs  Content type: Code
github.com··r/LocalLLaMA

Sigma-Branch: Hierarchical Single-Path Network Reconstruction for Dynamic Inference with Reduced Active Parameters

 🤖AI  Content type: Academic
arxiv.org·

FADA: Accessible fetal ultrasound interpretation and annotation with a selectively distilled unified vision-language model

 🧠Deep Learning  Content type: Academic
arxiv.org·

harshuljain13/llm-inference-at-scale: A Practitioner handbook for production llm serving.

 🤖AI  Content type: Code
github.com··Hacker News

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