Pipeshift AI provides a fast and scalable infrastructure for fine-tuning and inferencing open-source LLMs. We abstract away the training + inferencing infrastructure and the tooling/CI-CD around it, enabling engineering teams to get to production with all the optimizations and one-click deployments.
CEO @ Pipeshift AI. Helping developers fine-tune and deploy LLMs 10x faster.
CTO @ Pipeshift AI. Focused on squeezing out max LLM performance from GPUs
Making LLMs go brrrr at Pipeshift AI
TL;DR: Pipeshift is the cloud platform for finetuning and inferencing open-source LLMs, helping teams get to production with their LLMs faster than ever. With Pipeshift, companies making >1000 calls/day on frontier LLMs can use their data and logs to replace GPT/Claude with specialized LLMs that offer higher accuracy, lower latencies, and model ownership. Connect with us.
The open-source AI stack is missing, forcing most teams to experiment by duct-taping things like TGI/vLLM but having nothing ready for production. As you scale, it requires expensive ML talent, long build cycles, and constant optimizations.
The gap between open-source and closed-source models is shrinking (Meta's Llama 3.1 405B is a testament to that)! And open-source LLMs offer multiple benefits over their closed-source counterparts:
🔏 Model ownership and IP control
🎯 Verticalization and customizability
🏎️ Improved inference speeds and latency
💰 Reduction of API costs at scale
Pipeshift is the cloud platform for fine-tuning and inferencing open-source LLMs, helping developers get to production with their LLMs faster than ever.
🎯 Fine-tune Specialized LLMs
Run multiple LoRA-based fine-tuning jobs to build specialized LLMs.
⚡️ Serverless APIs of Base and Fine-tuned LLMs
Run inference for your fine-tuned LLMs and pay as per your token usage.
🏎️ Dedicated Instances for High Speed and Low Latency
Use our optimised inference stack to get max throughputs and utilisation on GPUs.
Product Demo: https://youtu.be/z8z5ILyXxCI
Our inference stack is one of the best globally, hitting 150+ tokens/sec on 70B parameter LLMs without any model quantization. And, since our private beta access was opened (<2 weeks back), we have already seen 25+ LLMs being fine-tuned with over 1.8B tokens in training data across 15+ companies.
If you’re building an AI co-pilot/agent/SaaS product and are looking to move to open-source LLMs or know someone who’s looking to do that same, then book a call or mail us at founders@pipeshift.ai - whichever you’d like!