Baseten

Intelligence should be defined by the people closest to the work. Intelligence should be owned by all of us. Let’s build a many model future!

Great work at @baseten running vLLM-Omni in production — open-source, production-grade, cost-efficient omni-modal serving 🎙️ Multi-stage audio, streaming multi-modal, real-time TTS — workloads where closed-source APIs have been the default. → github.com/vllm-project/v…

@EvidenceOpen @oneill_c 💚

The work to make a clinical answer trustworthy includes figuring out which source applies, when the evidence is weak, and what the doctor is trying to decide. Charlie @oneill_c at @baseten on why AI companies train their own models. Must read.

Partnering with @baseten is a joy! We love working alongside them to support the teams on the frontier.

We serve Qwen3-TTS on vLLM-Omni at $3 per 1M characters. That's 90% lower in cost than comparable closed-source TTS APIs. Our engineers optimized a single-replica serving stack to get there. Details on the optimized stack and cost per concurrent stream here.

This is for good cause!

Thanks @tuhinone for the mention!

This is a great piece from @baseten's @oneill_c: The question every app layer company is now asking is: "how do we resist commodification to deliver better results for customers?" The answer is specialized models based on your unique understanding of who you serve every day.

Now this is a message we can get behind: Let's build a many model future. #TeamScience

when I first heard of @baseten they were basically a competitor but then I met @tuhinone and I liked him, and when I heard they were pivoting to inference, I was relieved because I didn't want to compete against him and then he hired @DannieHerz and I was angry because I wish I had thought of it and now they're a critical partner for us as we embrace our own many model future at @_hex_tech I'm so happy for all their success and very excited to share what we've been working on with them!

The starting premise @Conviction was that AI (general models at scale) were a broad shift in computing. This has come to pass But the way AI benefits many users more powerfully is going to be more distributed product/research work, in partnership with humans who do the work

Honored to be included, rooting for @tuhinone, @DannieHerz and everyone at @baseten

Love that @baseten is sending this message right now. For a while, it’s felt like the future is finite. Many models, many futures!

“The question every app layer company is now asking is no longer ‘how do we use AI?’ It is ‘how do we resist commodification to deliver better results for customers?’ The answer is specialized models based on your unique understanding of who you serve every day. The big labs can’t do it, but you can.” Baseten’s Head of Model Training, @oneill_c, on the wave of AI companies using post-training to deliver better results for customers via specialized models.

@tereza_tizkova @smallest_AI @philipkiely 🩷💚

@baseten @smallest_AI @philipkiely I am really big fan of your brand! (and people) I like that someone uses pink color haha

Visited my first-ever conference as a sponsor, and it was a wakeup call! 🫥So I made a "guide to not wasting money on conference booths." Below are some learnings, checklist, & things I really loved online and irl. Im also giving credits there to my and my friends' favorite conference booths. Check it out!🔗👇

Every day we're seeing more companies emerge with specialized models that push the SOTA forward. Congrats to the Speechify team on the launch of SIMBA 3.0! Very happy to partner with you. More here: baseten.co/resources/cust…

@franceszhao_ @IKorovinsky 💚

@IKorovinsky @baseten im a big fan of this company’s branding

just wrapped up my first week @baseten on the post-training team can’t wait to make some models go brrr 💚

We're proud to share our partnership story with @SpeechifyAI. Speechify just announced SIMBA 3.0, ranked top 10 globally on the @ArtificialAnlys TTS leaderboard and the most cost-efficient model by far in that tier. We’re honored to serve the full SIMBA TTS family and other core workloads for Speechify, achieving: → 44% lower cost per 1M characters → 30-50% lower p99 latency → 4.5x faster cold starts Read the full case study here: