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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

9,517 subscribers

35,207 Aufrufe • vor 1 Jahr •via X (Twitter)

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Profilbild von Baseten

Basetenvor 1 Jahr

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:

Profilbild von Baseten

Basetenvor 1 Jahr

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.

Profilbild von Baseten

Basetenvor 1 Jahr

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

Profilbild von Mike Bilodeau

Mike Bilodeauvor 1 Jahr

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

Profilbild von Greg Schoeninger

Greg Schoeningervor 1 Jahr

@oxen_ai 🤝 @basetenco coming soon

Profilbild von Baseten

Basetenvor 1 Jahr

@oxen_ai 💚

Profilbild von VCs Congratulating Themselves 👏👏👏

VCs Congratulating Themselves 👏👏👏vor 1 Jahr

Big if true

Profilbild von Baseten

Basetenvor 1 Jahr

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

Profilbild von Baseten

Basetenvor 1 Jahr

we have all been there tbh

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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.

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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 Aufrufe • vor 1 Jahr

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 Aufrufe • vor 10 Monaten

"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."

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TBPN

33,278 Aufrufe • vor 4 Monaten

🚀 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 Aufrufe • vor 1 Jahr

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 Aufrufe • vor 4 Monaten

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let there be inference

let there be inference

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Baseten

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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

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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 ↓

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26,324 Aufrufe • vor 1 Jahr

Nobody knows what inference means but it's provocative

Nobody knows what inference means but it's provocative

Baseten

496,940 Aufrufe • vor 1 Jahr

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

30,607 Aufrufe • vor 21 Tagen

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Nebius Token Factory

260,215 Aufrufe • vor 8 Monaten