Inference

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Scoured 262 posts in 11.6 ms

Google Shrank Gemma 4 by 72% and Unsloth Fixed the 4-Bit Bug Nobody Else Caught on One 4090, and 4-Bit Shouldn’t Be This Good

 🧠LLMs  Content type: Blog
towardsai.net·

146th airhacks tv: Rust, Java 25, AI Agents, BCE, Web Components, zunit, zb

 🧠LLMs  Content type: Blog
adambien.blog·

DeskDash - a free Windows tool to easily manage your GGUF files

 🧠LLMs

Here's a llama.cpp CLI Command builder.

 ⚙️Systems Programming

Speculators v0.5.0: DFlash support and online training

 🧠LLMs
developers.redhat.com·

Alignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation

 🧠LLMs  Content type: Academic
arxiv.org·

TFLite Edge Model Quantizer Snippet

 🤖AI

Show HN: Run Llama.cpp In-Process from Java with Project Panama FFM

 🧠LLMs

Train Models Faster with JAX and MaxText Using NVFP4 on NVIDIA Blackwell

 🧠LLMs  Content type: News  Content type: Blog

Google DeepMind releases Gemma 4 QAT, but Unsloth developer Daniel Han warns naive llama.cpp conversions suffer accuracy loss

 🏗️MLSys  Content type: News
digg.com·

google/gemma-4-31B-it · fix: chat template — null handling, reasoning preservation, turn-tag balance, input validation

 🧠LLMs

The latest Gemma 4 models use a training trick to slash their on-device memory footprint

 🧠LLMs
androidauthority.com·

MiMo-v2.5-Pro-UltraSpeed: 1T model with 1000 TPS

 🛠️Compilers  Content type: Blog

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.

 🧠LLMs  Content type: Code
github.com··Hacker News

PagedAttention vs Traditional KV Cache: How vLLM Reinvented GPU Memory for LLM Inference

 ⚙️Systems Programming  Content type: Blog
medium.com
·

Apples to Apples: MLX vs. Llama.cpp for Gemma 4 12B on an M1 16GB

 🔧Hardware  Content type: Blog
ziraph.com··Hacker News

Token4Token — pay-per-token inference on Gnosis + Swarm

 🧠LLMs

Running Qwen 35B MoE at 450k Context on a Single 32GB GPU

 🛠️Compilers

Pruned YOLOv8 ONNX INT8 Fails: 3 Fixes That Work

 🤖AI  Content type: Blog  Content type: Discussion
tildalice.io·

Optimal Post-Training Quantization Scales and Where to Find Them

 🧠LLMs  Content type: Academic
arxiv.org·

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