OpenAI just rollicked the AI world yet again yesterday — while releasing the long awaited ChatGPT API, they also priced it at $2 per million tokens generated, which is 90% cheaper than the text-davinci-003 pricing of the “GPT3.5” family. Their blogpost on how they did it is vague: Through a series of system-wide optimizations, we’ve achieved 90% cost reduction for ChatGPT since December; we’re now passing through those savings to API users.We were fortunate enough to record Episode 2 of our podcast with someone who routinely creates 90%+ improvements for their customers, and in fact have started productizing their own infra skills with Codeium, the rapidly growing free-forever Copilot alternative (see What Building “Copilot for X” Really Takes). Varun Mohan is CEO of Exafunction/Codeium, and he indulged us in diving deep into AI infrastructure, compute-optimal training vs inference tradeoffs, and why he loves suffering.Recorded in-person at the beautiful StudioPod studios in San Francisco.Full transcript is below the fold. Timestamps* 00:00: Intro to Varun and Exafunction* 03:06: GPU Efficiency, Model Flop Utilization, Dynamic Multiplexing* 05:30: Should companies own their ML infrastructure?* 07:00: The two kinds of LLM Applications* 08:30: Codeium* 14:50: “Our growth is 4-5% day over day”* 16:30: Latency, Quality, and Correctability* 20:30: Acceleration mode vs Exploration mode* 22:00: Copilot for X - Harvey AI’s deal with Allen & Overy* 25:00: Scaling Laws (Chinchilla)* 28:45: “The compute-optimal model might not be easy to serve”* 30:00: Smaller models* 32:30: Deepmind Retro can retrieve external infromation* 34:30: Implications for embedding databases* 37:10: LLMOps - Eval, Data Cleaning* 39:45: Testing/User feedback* 41:00: “Users Is All You Need”* 42:45: General Intelligence + Domain Specific Dataset* 43:15: The God Nvidia computer* 46:00: Lightning roundShow notes* Varun Mohan Linkedin* Exafunction* Blogpost: Are GPUs Worth it for ML* Codeium* Copilot statistics* Eleuther’s The Pile and The Stack* What Building “Copilot for X” Really Takes* Copilot for X* Harvey, Copilot for Law - deal with Allen & Overy* Scaling Laws* Training Compute-Optimal Large Language Models - arXiv (Chinchilla paper)* chinchilla's wild implications (LessWrong)* UL2 20B: An Open Source Unified Language Learner (20B)* Paper - Deepmind Retro* “Does it make your beer taste better”* HumanEval benchmark/dataset* Reverse Engineering Copilot internals* Quora Poe* Prasanna Sankar notes on FLOPs and Bandwidth* NVIDIA H100 specs - 3TB/s GPU memory, 900GB/s NVLink Interconnect* Optimizer state is 14x size of model - 175B params => 2.5TB to store state → needs at least 30 H100 machines with 80GB each* Connor Leahy on The Gradient PodcastLightning Rounds* Favorite AI Product: Midjourney* Favorite AI Community: Eleuther and GPT-J* One year prediction: Better models, more creative usecases* Request for Startup: Superathlete Fitness Assistant* Takeaway: Continue to tinker!Transcript[00:00:00] Alessio Fanelli: Hey everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO in residence at Decibel Partners. I'm joined by my cohost, swyx, writer, editor of L Space Diaries.[00:00:20] swyx: Hey, and today we have Varun Mohan from Codeium / Exafunction on. I should introduce you a little bit because I like to get the LinkedIn background out of the way.[00:00:30] So you did CS at MIT and then you spent a few years at Nuro where you were ultimately tech lead manager for autonomy. And that's an interesting dive. Self-driving cars in AI and then you went straight into Exafunction with a few of your coworkers and that's where I met some of them and started knowing about Exafunction.[00:00:51] And then from out of nowhere you cloned GitHub Copilot. That's a lot of progress in a very short amount of time. So anyway, welcome .[00:00:59] Varun Mohan: That's high praise.[00:01:00] swyx: What's one thing about you that doesn't appear on LinkedIn that is a bi