with all the sparsity-aware context based memory loading papers coming out, (PowerInfer getting 11x and Apple getting 25x speedup on GPU) ReLU's dead zone is turning out to be important llama-class models (SwiGLU) might not have much longevity afterall once all the Metal work

⚡PowerInfer It is a CPU/GPU LLM inference engine leveraging activation locality for your device. The underlying design of PowerInfer exploits the high locality inherent in LLM inference, characterized by a power-law distribution in neuron activation. This distribution

A one-plus 24GB mobile running a Mixtral 8x7B at 11 tokens/second with PowerInfer-2🤯 Much faster inference speed vs llama.cpp and MLC-LLM. Using swap and caching to run the model even if it doesn't fit the available RAM. 📌 Between Apple’s LLM in a flash and PowerInfer-2,

PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU paper page: https://t.co/GfwfNHOidp This paper introduces PowerInfer, a high-speed Large Language Model (LLM) inference engine on a personal computer (PC) equipped with a single consumer-grade GPU. The key

This is why quality quantization or other methods like PowerInfer matter

PowerInfer - a high-speed inference engine for deploying LLMs locally. Just came across this super interesting project on speeding up inference. It's not MoE but it's a simple approach that exploits the high locality in LLM inference to design a GPU-CPU hybrid inference engine.

Wonder how Tiiny runs a 120B LLM locally? The key is PowerInfer — a heterogeneous inference architecture that assigns "hot neurons" and "cold neurons" across NPU/CPU for unprecedented 120B decoding speed on consumer hardware. Code & details on GitHub👇 #AI #LLM #OpenAI

🚀🖥️ PowerInfer: Fast LLM Serving with a Consumer-grade GPU • A new tool to make large LLMs run fast on regular computers with a consumer graphics card (GPU). • It works by identifying which parts of the AI (neurons) are used a lot and which aren't. • The parts used a lot are

This is the paper which made possible to run Mixtral 8x7B at 11 tokens/s on a mobile phone.🤯 📌 PowerInfer-2 tackles the challenge of enabling high-speed inference of LLMs on smartphones, especially for models that exceed the device's memory capacity. 🔥 📌 The key innovation

PowerInfer-2 vs LLM in a Flash vs llama.cpp

やっとPowerInferが4090でLlama2-70B-4qを動かしてくれた。桃太郎を書かせたけど途中からストーリーがずれててまあLlama2くんの安定運転... 最終スピードとしてWSL2環境において13900K 64G RAM 16 threads許可で3.89 tokens/sまで出していて、プリプリントで主張された~4 tokens/sの結果を再現できた

专为智能手机设计的高性能大模型推理框架：PowerInfer-2 支持 Mixtral 47B MoE模型，推理速度快达每秒11.68个token，比其他框架快22倍 即使在7B模型上，只需在手机上放置50%的FFN权重，仍然能保持最先进的速度 特点 1、PowerInfer-2 是一个高速推理引擎，专为智能手机部署激活稀疏型 LLMs 设计

比llama.cpp快12倍！ PwerInfer推理引擎实测速度比llama.cpp快了11.69倍，日新月异啊 https://t.co/duCyCZy5Ja

PowerInfer is making rounds in AI inference/local llama community. It's a fork of llama.cpp. I created a PR merging it and wrote a review on the diff. Will update it as I go through it more. Feel free to contribute with comments. https://t.co/Zp2LvtLge0

キタコレ。ポケットにGPTー4時代 【Apple の LLM in a flash と PowerInfer】 24GB のモバイルで、Mixtral 8x7B を 11 トークン/秒で実行 https://t.co/xvckxQOH5T llama.cpp および MLC-LLM と比較して推論速度が大幅に高速化 使用可能な RAM

Kind of.. I started writing this in Nov.. Just started prompting tho… This and Powerinfer

🚀 Excited to introduce PowerInfer-2: A game-changing LLM inference engine for mobile devices by the #PowerInfer team. It smoothly runs a 47B model with a staggering 29x speedup on smartphones! Watch our demo to see it in action! 🎥 Technical details at: https://t.co/7bx5EnzWCs

折角NVIDAとCUDAを使える様になったので、高速LLM本命のPowerInferを使う 果たして6GBでまともに動くのだろうか インストールはCUDAを入れてから git clone https://t.co/49Dn3GnEDB cd PowerInfer pip install -r requirements.txt 続く

This is how GPT-4 scores its own responses to tech & startup prompts compared to: * LocalMentor * Mixtral-8x7b * Falcon-40B (128 token response) Sampled responses using the mixtral-offloading notebook: https://t.co/45zJekZ5lo And the PowerInfer demo: https://t.co/IXMqYiRjQd