Nicky
613 posts

Nicky
@n10nft
Boomer #lgbt 🌈 artist and #web3 analyst ✍️ from 🇲🇾

DO you want to serve models at scale? do you want to do it locally and not be dependent upon a company for good throughput? do you just generally like really cool things? well @AlpinDale does too! - so he's built out Aphrodite as the official backend engine for PygmalionAI(pygmalion.chat) - in the open source for all of our collective benefit, heres what it does for those of you who dont know 👀 its an inference and serving engine which outputs an api endpoint compatible with anything built on Openai's api backend as well as Kobold among the general host of outrageous features it offers you can find: Continuous Batching Efficient K/V management with PagedAttention Optimized CUDA kernels for improved inference Quantization support via GPTQ, GGUF, AWQ, QuIP#, and SqueezeLLM. Distributed inference Variety of sampling methods (Mirostat, Locally Typical Sampling, Tail-Free Sampling, etc) 8-bit KV Cache for higher context lengths and throughput. a recently or soon to merge pr containing an open source implementation of function calling for any model served github.com/PygmalionAI/ap… nccl backend to improve throughput for tensor parallelism by nearly double compatibility with throughput optimized quants like Marlin and way more just wait until we really get going here this is just the beginning. github.com/PygmalionAI/ap…






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❤️🔥Ready for a $5,000 #Easter giveaway?🐣Explore with @AlchemyPay, co-host @fusionistio, partners @PolyhedraZK, @Casper_Network, @kroma_network! 🏆Part 1: Task Completion- $3,000 for 200 winners galxe.com/AlchemyPay/cam… 📸 Part 2: Eggcellence Hunt- $2,000 for 100 winners 🌟Snapshot the project's easter egg 🌟QT& add project tag& tag 3 friends 📅March 31- April 13






What makes @ionet_official different from other GPU aggregators on the market? @ionet_official's ability to form decentralized clusters by aggregating GPUs across multiple locations. Without clustering, it would be virtually impossible to train today's machine learning models, which are highly complex. This creates the opportunity for a 'structural arbitrage', allowing @ionet_official to offer unmatched cost efficiency and low latency GPU compute up to 90% cheaper than the competition.






