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🚀 Our "technical" marketer might not be looped in, but today is our biggest launch day yet. We're introducing two new products to serve the inference lifecycle: Model APIs and Training. Model APIs are frontier models running on the Baseten Inference Stack, purpose-built for production. Baseten Training (Beta) provides... show more

Baseten

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35,207 次观看 • 1 年前 •via X (Twitter)

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Baseten 的头像

Baseten1 年前

We've had great partners in building Model APIs, @Retool, @OpenRouterAI, and @poe_platform. Today we’re launching DeepSeek R1, V3, Llama Scout and Maverick, with more rolling out soon—stay tuned. You can get started here:

Baseten 的头像

Baseten1 年前

We’re working with @oxen_ai (moo!) to power model training with robust data management tooling. Learn more here: We're also partnering with @mixedbreadai to support their frontier embedding models, and @Eliasfiz and @akv_13 at Canopy Labs to deliver fine-tuning for text-to-speech generation with Orpheus.

Baseten 的头像

Baseten1 年前

Learn more about Baseten Model APIs and Training in @tuhinone's blog:

Mike Bilodeau 的头像

Mike Bilodeau1 年前

see the Model APIs running now on @OpenRouterAI - not bad!

Greg Schoeninger 的头像

Greg Schoeninger1 年前

@oxen_ai 🤝 @basetenco coming soon

Baseten 的头像

Baseten1 年前

@oxen_ai 💚

VCs Congratulating Themselves 👏👏👏 的头像

VCs Congratulating Themselves 👏👏👏1 年前

Big if true

Baseten 的头像

Baseten1 年前

the marketing doesn't always work, but the product does

Baseten 的头像

Baseten1 年前

we have all been there tbh

相关视频

Our approach to AI infra is simple: build the most fungible and flexible fleet to meet the real world's needs across inference and training as Scott Guthrie shared with Alex Kantrowitz. And we are already doing it at scale today, as we power the biggest AI workloads like Copilot and ChatGPT, APIs that power 3P products & enterprise workloads and high scale training.

Our approach to AI infra is simple: build the most fungible and flexible fleet to meet the real world's needs across inference and training as Scott Guthrie shared with Alex Kantrowitz. And we are already doing it at scale today, as we power the biggest AI workloads like Copilot and ChatGPT, APIs that power 3P products & enterprise workloads and high scale training.

Satya Nadella

114,478 次观看 • 9 个月前

DeepSeek-R1 is blowing up right now, but we're not surprised. And not just because we’ve been working closely with DeepSeek to bring these models to production. We've been betting on powerful, open-source models like DeepSeek-R1 from day one. 1/n 🧵

DeepSeek-R1 is blowing up right now, but we're not surprised. And not just because we’ve been working closely with DeepSeek to bring these models to production. We've been betting on powerful, open-source models like DeepSeek-R1 from day one. 1/n 🧵

Baseten

59,332 次观看 • 1 年前

IN NEWS: Baseten raises a $150M series D round. Tuhin Srivastava (Founder & CEO, Baseten) on the future of inference: “I think the token price goes down and inference should get cheaper over time. And that really just means there is going to be more inference.” “Every time we lower prices or optimize models to make it cheaper, four months later customers are spending more anyway.” “Inference prices will go down, but if the world is run by AI in 10 years, there is going to be a lot of inference. It better be cheap.”

IN NEWS: Baseten raises a $150M series D round. Tuhin Srivastava (Founder & CEO, Baseten) on the future of inference: “I think the token price goes down and inference should get cheaper over time. And that really just means there is going to be more inference.” “Every time we lower prices or optimize models to make it cheaper, four months later customers are spending more anyway.” “Inference prices will go down, but if the world is run by AI in 10 years, there is going to be a lot of inference. It better be cheap.”

TBPN

17,057 次观看 • 10 个月前

"Inference, if you look at it as a market, will be much, much bigger than cloud computing was pre-ChatGPT." Lightspeed’s Bucky Moore says inference is an underrated investment category in AI, and expects the market to break up into large, specialized platforms for each modality: "The GPU supply crunch that we're seeing right now is largely, as Dylan Patel has said on the show before, due to the fact that not only these consumer products, but also B2B products like Claude Code and Codex are just really taking off and creating insane demand for inference." "We're talking hundreds of billions in spend every year. And if that's true, I think there will be very, very large inference platforms built in each modality." "So there will be an inference platform for real-time video models, there will be an inference platform for open-source and custom language models, there will be an inference platform built specifically for long-running agents." "So I think we're just going to see that industry, which today looks like one industry, break up into many because of how big it is and how much room for specialization there is."

"Inference, if you look at it as a market, will be much, much bigger than cloud computing was pre-ChatGPT." Lightspeed’s Bucky Moore says inference is an underrated investment category in AI, and expects the market to break up into large, specialized platforms for each modality: "The GPU supply crunch that we're seeing right now is largely, as Dylan Patel has said on the show before, due to the fact that not only these consumer products, but also B2B products like Claude Code and Codex are just really taking off and creating insane demand for inference." "We're talking hundreds of billions in spend every year. And if that's true, I think there will be very, very large inference platforms built in each modality." "So there will be an inference platform for real-time video models, there will be an inference platform for open-source and custom language models, there will be an inference platform built specifically for long-running agents." "So I think we're just going to see that industry, which today looks like one industry, break up into many because of how big it is and how much room for specialization there is."

TBPN

33,278 次观看 • 4 个月前

🚀 New Generally Available Whisper drop: The fastest, most accurate, and cost-effective transcription with over 1000x real-time factor for production AI workloads. 🚀 Our new Generally Available Whisper implementation delivers: 🏎️ Over 1000x real-time factor ✨ The lowest word error rate 💪 Production-grade reliability 🧩 Custom scaling and hardware per processing step 👉 See how in our blog: Reach out to get record-breaking performance for your mission-critical AI workloads!

🚀 New Generally Available Whisper drop: The fastest, most accurate, and cost-effective transcription with over 1000x real-time factor for production AI workloads. 🚀 Our new Generally Available Whisper implementation delivers: 🏎️ Over 1000x real-time factor ✨ The lowest word error rate 💪 Production-grade reliability 🧩 Custom scaling and hardware per processing step 👉 See how in our blog: Reach out to get record-breaking performance for your mission-critical AI workloads!

Baseten

15,489 次观看 • 1 年前

Excited to share that we’ve raised $300M in our Series B round, led by Radical Ventures, bringing our total funding to more than $450M, with leading technology companies joining as both customers and investors. We continue accelerating the path to AGI through our two core pillars: ultra-optimized infrastructure for AI workloads, and realtime world models built on top of it. Today, we’re also launching DOS (Decart Optimization Stack) 2.0 – our next-generation inference and training platform, delivering over 1,600 tokens per second for agentic inference and over 100 FPS for world models across major hardware platforms. Alongside DOS, we will launch new versions of our world models in the coming weeks: Lucy, for immersive realtime experiences, and Oasis, for physical AI. Grateful to our partners, customers, and backers across media & entertainment, Physical AI, chips, hyperscalers, and the broader AI ecosystem. Radical Ventures, Adobe, Alpha Partners, Amazon and Amazon Web Services, Atreides Management, LP, Benchmark, eBay, NVIDIA, Sequoia Capital, Toyota USA, @valorep, oren zeev; Andrej Karpathy, Michael Eisner, Yamauchi-No.10 Family Office, Moritz Baier-Lentz and more. We made a film for this moment with Decart CEO Dean Leitersdorf and Moritz Baier-Lentz - a look at the company, the technology, and what comes next.

Excited to share that we’ve raised $300M in our Series B round, led by Radical Ventures, bringing our total funding to more than $450M, with leading technology companies joining as both customers and investors. We continue accelerating the path to AGI through our two core pillars: ultra-optimized infrastructure for AI workloads, and realtime world models built on top of it. Today, we’re also launching DOS (Decart Optimization Stack) 2.0 – our next-generation inference and training platform, delivering over 1,600 tokens per second for agentic inference and over 100 FPS for world models across major hardware platforms. Alongside DOS, we will launch new versions of our world models in the coming weeks: Lucy, for immersive realtime experiences, and Oasis, for physical AI. Grateful to our partners, customers, and backers across media & entertainment, Physical AI, chips, hyperscalers, and the broader AI ecosystem. Radical Ventures, Adobe, Alpha Partners, Amazon and Amazon Web Services, Atreides Management, LP, Benchmark, eBay, NVIDIA, Sequoia Capital, Toyota USA, @valorep, oren zeev; Andrej Karpathy, Michael Eisner, Yamauchi-No.10 Family Office, Moritz Baier-Lentz and more. We made a film for this moment with Decart CEO Dean Leitersdorf and Moritz Baier-Lentz - a look at the company, the technology, and what comes next.

Decart

96,116 次观看 • 2 个月前

What is AI inference engineering, why is it such an in-demand skill, and how do you break into the field? With author of Inference Engineering Philip Kiely and head of training at Baseten Charlie O'Neill 0:00: What is inference? 2:47: History of inference 4:59: Downstream effects of AI research on inference 13:54: What you'll learn from Inference Engineering 16:14: Advice for engineers transitioning into AI 19:00: Open source models driving inference growth 20:55: Specialization vs. frontier closed models 23:51: "Big Token" and the importance of open source AI 27:18: Where to get Inference Engineering

What is AI inference engineering, why is it such an in-demand skill, and how do you break into the field? With author of Inference Engineering Philip Kiely and head of training at Baseten Charlie O'Neill 0:00: What is inference? 2:47: History of inference 4:59: Downstream effects of AI research on inference 13:54: What you'll learn from Inference Engineering 16:14: Advice for engineers transitioning into AI 19:00: Open source models driving inference growth 20:55: Specialization vs. frontier closed models 23:51: "Big Token" and the importance of open source AI 27:18: Where to get Inference Engineering

Madison Kanna

119,268 次观看 • 4 个月前

NVIDIA Vera Rubin is in full production and arrives just in time for the next frontier of AI. The #NVIDIARubin platform uses extreme co-design across six new chips to accelerate agentic AI, advanced reasoning and massive-scale MoE model inference. NVIDIA Vera Rubin NVL72: Six new chips, one AI supercomputer — see how we built it.

NVIDIA Vera Rubin is in full production and arrives just in time for the next frontier of AI. The #NVIDIARubin platform uses extreme co-design across six new chips to accelerate agentic AI, advanced reasoning and massive-scale MoE model inference. NVIDIA Vera Rubin NVL72: Six new chips, one AI supercomputer — see how we built it.

NVIDIA Data Center

27,643 次观看 • 6 个月前

let there be inference

let there be inference

Baseten

332,307 次观看 • 1 年前

Tired of guessing which AI model is best? 🤔 We've got you covered! 👀 Here's a sneak peek of our new model comparison feature in action! Run the same prompt across multiple models and see which one works best for YOU. Which models are you excited to test? #Inferium #AI #Inference #Compare

Tired of guessing which AI model is best? 🤔 We've got you covered! 👀 Here's a sneak peek of our new model comparison feature in action! Run the same prompt across multiple models and see which one works best for YOU. Which models are you excited to test? #Inferium #AI #Inference #Compare

Inferium

12,329 次观看 • 1 年前

Today, we're introducing GLM-5.2 Fast: our GLM-5.2 Model API designed for the most demanding real-time use cases. The Fast tier delivers 2-3x higher TPS than the standard GLM-5.2 MAPI.

Today, we're introducing GLM-5.2 Fast: our GLM-5.2 Model API designed for the most demanding real-time use cases. The Fast tier delivers 2-3x higher TPS than the standard GLM-5.2 MAPI.

Baseten

111,285 次观看 • 2 天前

Today, we're introducing not one but TWO new models, striking the balance between efficiency and quality to enable you to build production AI agents. — Gemini 3.6 Flash: Addresses efficiency feedback we received from Gemini 3.5 Flash with upgrades in coding, knowledge work, and multimodal tasks faster, more accurately, and with substantially fewer tokens per task — Gemini 3.5 Flash-Lite: Our fastest, most cost-effective 3.5-class model yet built for agentic workflows, hitting ~350 output tokens/sec with improved coding and overall quality Start building with these today via the Gemini API in Google AI Studio or try them out in the Google Gemini

Today, we're introducing not one but TWO new models, striking the balance between efficiency and quality to enable you to build production AI agents. — Gemini 3.6 Flash: Addresses efficiency feedback we received from Gemini 3.5 Flash with upgrades in coding, knowledge work, and multimodal tasks faster, more accurately, and with substantially fewer tokens per task — Gemini 3.5 Flash-Lite: Our fastest, most cost-effective 3.5-class model yet built for agentic workflows, hitting ~350 output tokens/sec with improved coding and overall quality Start building with these today via the Gemini API in Google AI Studio or try them out in the Google Gemini

Google AI

418,134 次观看 • 5 天前

Today is a big day at Odyssey. We're sharing Explorer, our first generative world model. We think world models are the next frontier for AI, enabling wonderful new things. To help shape this, Ed Catmull, the iconic founder of Pixar, is joining our board and investing.

Today is a big day at Odyssey. We're sharing Explorer, our first generative world model. We think world models are the next frontier for AI, enabling wonderful new things. To help shape this, Ed Catmull, the iconic founder of Pixar, is joining our board and investing.

Odyssey

75,286 次观看 • 1 年前

Today we’re announcing two new updates in our computer vision work — a new, expanded license for our DINOv2 model and the release of FACET, a comprehensive new benchmark dataset to help evaluate and improve fairness in vision models. More details ➡️ 🧵

Today we’re announcing two new updates in our computer vision work — a new, expanded license for our DINOv2 model and the release of FACET, a comprehensive new benchmark dataset to help evaluate and improve fairness in vision models. More details ➡️ 🧵

AI at Meta

453,971 次观看 • 2 年前

Today, we’re thrilled to unveil the Arcee.ai Foundation Models, a new family of GenAI models designed from the ground up for enterprise reality. The first release—AFM-4.5B—is a 4.5-billion-parameter frontier model that delivers excellent accuracy, strict compliance, and very high cost efficiency. In short: enterprise-grade intelligence that can run anywhere—on a smartphone, at the edge, or in the cloud. For a quick taste, you can test AFM-4.5B in our playground and on For a deeper dive into the model’s training pipeline and benchmarks, details are available in our technical blog post. ➡️ Arcee AI playground: ➡️ Together AI playground: ➡️ Launch blog post: ➡️ Technical blog post: PS: As we’ll now focus on our AFM foundation models, and because we love open-source, we're opening up access to our previously closed-source language models. Details in the tech blog post!

Today, we’re thrilled to unveil the Arcee.ai Foundation Models, a new family of GenAI models designed from the ground up for enterprise reality. The first release—AFM-4.5B—is a 4.5-billion-parameter frontier model that delivers excellent accuracy, strict compliance, and very high cost efficiency. In short: enterprise-grade intelligence that can run anywhere—on a smartphone, at the edge, or in the cloud. For a quick taste, you can test AFM-4.5B in our playground and on For a deeper dive into the model’s training pipeline and benchmarks, details are available in our technical blog post. ➡️ Arcee AI playground: ➡️ Together AI playground: ➡️ Launch blog post: ➡️ Technical blog post: PS: As we’ll now focus on our AFM foundation models, and because we love open-source, we're opening up access to our previously closed-source language models. Details in the tech blog post!

Arcee.ai

21,934 次观看 • 1 年前

Compute is the backbone of the AI-driven future. OptimAI Compute Engine would enable scalable, high-performance workloads across image and video models, supporting: • Text-to-image generation • Image editing and inpainting • 2K / 4K / 8K super-resolution • Brand-aligned visual synthesis • OCR and image intelligence • Video generation and motion synthesis • Frame interpolation and enhancement • Multimodal model inference Distributed compute built for faster inference, shorter training cycles, and production-grade AI execution.

OptimAI Network

18,821 次观看 • 5 个月前

Nobody knows what inference means but it's provocative

Nobody knows what inference means but it's provocative

Baseten

496,940 次观看 • 1 年前

My startup, Positron AI just raised $51.6M Series A to rebuild the infrastructure powering AI inference and bring Superintelligence to everyone. Led by Valor Equity Partners (@valorep) , Atreides Management (Atreides Management, LP) , and DFJ Growth (DFJ Growth) —the same teams behind SpaceX, Tesla, xAI, and other companies pushing compute to its physical and economic limits. Why inference? Because as AI moves from research into production, the real bottleneck isn’t training—it’s deploying transformer models at scale. GPUs were built for flexibility, not predictable workloads. That mismatch means wasted bandwidth, poor density, and massive power costs. So at Positron, we built silicon specifically optimized for inference: •>90% memory bandwidth utilization (GPUs typically ~30%) •Multi-model hosting per card (higher density, lower power) •Zero code changes — drop in compatability with the HuggingFace Transformers ecosystem. U.S.-made silicon, stable supply chain, geopolitically resilient We’re already shipping and running in production environments today. Here’s what we’ve built, and link to our WSJ coverage and press release in the follow up reply ↓

My startup, Positron AI just raised $51.6M Series A to rebuild the infrastructure powering AI inference and bring Superintelligence to everyone. Led by Valor Equity Partners (@valorep) , Atreides Management (Atreides Management, LP) , and DFJ Growth (DFJ Growth) —the same teams behind SpaceX, Tesla, xAI, and other companies pushing compute to its physical and economic limits. Why inference? Because as AI moves from research into production, the real bottleneck isn’t training—it’s deploying transformer models at scale. GPUs were built for flexibility, not predictable workloads. That mismatch means wasted bandwidth, poor density, and massive power costs. So at Positron, we built silicon specifically optimized for inference: •>90% memory bandwidth utilization (GPUs typically ~30%) •Multi-model hosting per card (higher density, lower power) •Zero code changes — drop in compatability with the HuggingFace Transformers ecosystem. U.S.-made silicon, stable supply chain, geopolitically resilient We’re already shipping and running in production environments today. Here’s what we’ve built, and link to our WSJ coverage and press release in the follow up reply ↓

Thomas Sohmers

26,324 次观看 • 1 年前

GLM-5.2 running on Mac Studio Clusters or RTX GPU rigs isn't how Local AI will get mass adopted. For mass adoption we need to focus on two axes: 1. Intelligence/Speed Frontier. This is being tracked on local dot ai. Aggressive co-design is needed across the entire stack to push up the frontier specifically for local hardware. Better kernels, inference tricks like spec decoding, quantizing models, model routing. 2. Usability / UX. To use Local AI today, you need to install CLIs, select a model, a quantization, a harness that works best with that model, and you need to configure your inference engine. It's not very accessible. More work needs to be done on making this seamless, so the user doesn't even know the AI is running locally.

GLM-5.2 running on Mac Studio Clusters or RTX GPU rigs isn't how Local AI will get mass adopted. For mass adoption we need to focus on two axes: 1. Intelligence/Speed Frontier. This is being tracked on local dot ai. Aggressive co-design is needed across the entire stack to push up the frontier specifically for local hardware. Better kernels, inference tricks like spec decoding, quantizing models, model routing. 2. Usability / UX. To use Local AI today, you need to install CLIs, select a model, a quantization, a harness that works best with that model, and you need to configure your inference engine. It's not very accessible. More work needs to be done on making this seamless, so the user doesn't even know the AI is running locally.

Alex Cheema

30,443 次观看 • 17 天前

🔱 Introducing Manta AI 🔱 In collaboration with Celestia, ORA, and Hyperbolic, we are excited to launch a comprehensive AI toolkit on #MantaPacific. Experience seamless training, deployment, and inference with our Universal Circuits, now enhanced with powerful AGI models like Stable Diffusion & Llama. 🌊 Dive into the future of AI on blockchain:

🔱 Introducing Manta AI 🔱 In collaboration with Celestia, ORA, and Hyperbolic, we are excited to launch a comprehensive AI toolkit on #MantaPacific. Experience seamless training, deployment, and inference with our Universal Circuits, now enhanced with powerful AGI models like Stable Diffusion & Llama. 🌊 Dive into the future of AI on blockchain:

Manta Network (🔱,🔱)

174,032 次观看 • 2 年前