Show HN: NCompass Technologies – yet another AI Inference API, but hear us out
3 by adiraja | 5 comments on Hacker News.
Hello HackerNews! I’m excited to share what we’ve been working on at nCompass Technologies: an AI inference platform that gives you a scalable and reliable API to access any open-source AI model — with no rate limits. We don't have rate limits as optimizations we made to our AI model serving software enable us to support a high number of concurrent requests without degrading quality of service for you as a user. If you’re thinking, well aren’t there a bunch of these already? So were we when we started nCompass. When using other APIs, we found that they weren’t reliable enough to be able to use open source models in production environments. To resolve this, we're building an AI inference engine that enable you, as an end user, to reliably use open source models in production. Underlying this API, we’re building optimizations at the hosting, scheduling and kernel levels with the single goal of minimizing the number of GPUs required to maximize the number of concurrent requests you can serve, without degrading quality of service. We’re still building a lot of our optimizations, but we’ve released what we have so far via our API. Compared to vLLM, we currently keep time-to-first-token (TTFT) 2-4x lower than vLLM at the equivalent concurrent request rate. You can check out a demo of our API here: https://ift.tt/WeBS91Q As a result of the optimizations we’ve rolled out so far, we’re releasing a few unique features on our API: 1. Rate-Limits: we don’t have any Most other API’s out there have strict rate limits and can be rather unreliable. We don’t want API’s for open source models to remain as a solution for prototypes only. We want people to use these APIs like they do OpenAI’s or Anthropic’s and actually make production grade products on top of open source models. 2. Underserved models: we have them There are a ton of models out there, but not all of them are readily available for people to use if they don’t have access to GPUs. We envision our API becoming a system where anyone can launch any custom model of their choice with minimal cold starts and run the model as a simple API call. Our cold starts for any 8B or 70B model are only 40s and we’ll keep improving this. Towards this goal, we already have models like `ai4bharat/hercule-hi` hosted on our API to support non-english language use cases and models like `Qwen/QwQ-32B-Preview` to support reasoning based use cases. You can find the other models that we host here: https://ift.tt/pWokNiS. We’d love for you to try out our API by following the steps here: https://ift.tt/0g8aAoV . We provide $100 of free credit on sign up to run models, and like we said, go crazy with your requests, we’d love to see if you can break our system :) We’re still actively building out features and optimizations and your input can help shape the future of nCompass. If you have thoughts on our platform or want us to host a specific model, let us know at hello@ncompass.tech. Happy Hacking!
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New top story on Hacker News: Show HN: I made the slowest, most expensive GPT
Show HN: I made the slowest, most expensive GPT
23 by wluk | 13 comments on Hacker News.
This is another one of my automate-my-life projects - I'm constantly asking the same question to different AIs since there's always the hope of getting a better answer somewhere else. Maybe ChatGPT's answer is too short, so I ask Perplexity. But I realize that's hallucinated, so I try Gemini. That answer sounds right, but I cross-reference with Claude just to make sure. This doesn't really apply to math/coding (where o1 or Gemini can probably one-shot an excellent response), but more to online search, where information is more fluid and there's no "right" search engine + text restructuring + model combination every time. Even o1 doesn't have online search, so it's obviously a hard problem to solve. An example is something like "best ski resorts in the US", which will get a different response from every GPT, but most of their rankings won't reflect actual skiers' consensus - say, on Reddit https://ift.tt/jm8DBXF... - because there's so many opinions floating around, a one-shot RAG search + LLM isn't going to have enough context to find how everyone thinks. And obviously, offline GPTs like o1 and Sonnet/Haiku aren't going to have the latest updates if a resort closes for example. So I’ve spent the last few months experimenting with a new project that's basically the most expensive GPT I’ll ever run. It runs search queries through ChatGPT, Claude, Grok, Perplexity, Gemini, etc., then aggregates the responses. For added financial tragedy, in-between it also uses multiple embedding models and performs iterative RAG searches through different search engines. This all functions as sort of like one giant AI brain. So I pay for every search, then every embedding, then every intermediary LLM input/output, then the final LLM input/output. On average it costs about 10 to 30 cents per search. It's also extremely slow. https://ithy.com I know that sounds absurdly overkill, but that’s kind of the point. The goal is to get the most accurate and comprehensive answer possible, because it's been vetted by a bunch of different AIs, each sourcing from different buckets of websites. Context limits today are just large enough that this type of search and cross-model iteration is possible, where we can determine the "overlap" between a diverse set of text to determine some sort of consensus. The idea is to get online answers that aren't attainable from any single AI. If you end up trying this out, I'd recommend comparing Ithy's output against the other GPTs to see the difference. It's going to cost me a fortune to run this project (I'll probably keep it online for a month or two), but I see it as an exploration of what’s possible with today’s model APIs, rather than something that’s immediately practical. Think of it as an online o1 (without the $200/month price tag, though I'm offering a $29/month Pro plan to help subsidize). If nothing else, it’s a fun (and pricey) thought experiment.
23 by wluk | 13 comments on Hacker News.
This is another one of my automate-my-life projects - I'm constantly asking the same question to different AIs since there's always the hope of getting a better answer somewhere else. Maybe ChatGPT's answer is too short, so I ask Perplexity. But I realize that's hallucinated, so I try Gemini. That answer sounds right, but I cross-reference with Claude just to make sure. This doesn't really apply to math/coding (where o1 or Gemini can probably one-shot an excellent response), but more to online search, where information is more fluid and there's no "right" search engine + text restructuring + model combination every time. Even o1 doesn't have online search, so it's obviously a hard problem to solve. An example is something like "best ski resorts in the US", which will get a different response from every GPT, but most of their rankings won't reflect actual skiers' consensus - say, on Reddit https://ift.tt/jm8DBXF... - because there's so many opinions floating around, a one-shot RAG search + LLM isn't going to have enough context to find how everyone thinks. And obviously, offline GPTs like o1 and Sonnet/Haiku aren't going to have the latest updates if a resort closes for example. So I’ve spent the last few months experimenting with a new project that's basically the most expensive GPT I’ll ever run. It runs search queries through ChatGPT, Claude, Grok, Perplexity, Gemini, etc., then aggregates the responses. For added financial tragedy, in-between it also uses multiple embedding models and performs iterative RAG searches through different search engines. This all functions as sort of like one giant AI brain. So I pay for every search, then every embedding, then every intermediary LLM input/output, then the final LLM input/output. On average it costs about 10 to 30 cents per search. It's also extremely slow. https://ithy.com I know that sounds absurdly overkill, but that’s kind of the point. The goal is to get the most accurate and comprehensive answer possible, because it's been vetted by a bunch of different AIs, each sourcing from different buckets of websites. Context limits today are just large enough that this type of search and cross-model iteration is possible, where we can determine the "overlap" between a diverse set of text to determine some sort of consensus. The idea is to get online answers that aren't attainable from any single AI. If you end up trying this out, I'd recommend comparing Ithy's output against the other GPTs to see the difference. It's going to cost me a fortune to run this project (I'll probably keep it online for a month or two), but I see it as an exploration of what’s possible with today’s model APIs, rather than something that’s immediately practical. Think of it as an online o1 (without the $200/month price tag, though I'm offering a $29/month Pro plan to help subsidize). If nothing else, it’s a fun (and pricey) thought experiment.