Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

Great to be back at another stellar Modular developer conference: #ModCon2026! Thanks to tim for leading a deeply engaging discussion on AI infrastructure, the economics of training and inference, and the looming massive impact of open models. Takeaway: Efficient open models can achieve 90% of frontier performance at 10%... show more

Dave Munichiello 🇺🇸

3,863 subscribers

11,749 просмотров • 2 месяцев назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

The most effective AI systems don't rely on a single model. Frontier models provide state-of-the-art performance for complex tasks, while routers automatically select lightweight, open-source models for simpler jobs to optimize accuracy, latency, and cost. Learn more:

The most effective AI systems don't rely on a single model. Frontier models provide state-of-the-art performance for complex tasks, while routers automatically select lightweight, open-source models for simpler jobs to optimize accuracy, latency, and cost. Learn more:

NVIDIA AI

12,152 просмотров • 7 месяцев назад

Congrats to our partners Together AI on their $800M Series C fundraising round. Few teams have done more to make open models fast, accessible, and production-ready. We’re excited to keep working with Vipul Ved Prakash and the team on the next frontier of inference: lower costs, better performance, and open models at real scale.

Congrats to our partners Together AI on their $800M Series C fundraising round. Few teams have done more to make open models fast, accessible, and production-ready. We’re excited to keep working with Vipul Ved Prakash and the team on the next frontier of inference: lower costs, better performance, and open models at real scale.

Pearl Research Labs

16,613 просмотров • 3 месяцев назад

Gavin Baker Gavin Baker says open source is "awesome" for AI infrastructure names because it shifts the economic value from the frontier labs to the infrastructure, and it is already 80% plus of all tokens processed. "A misconception that a lot of people have is that open source models are somehow bad for AI. They're awesome for the AI infrastructure providers. They just shift economic value from the margins of the frontier labs to the infrastructure." "But I do think there's still a role for these frontier models. And it may be true, to date, frontier tokens are capturing 90% of the economic value, and open source tokens are probably 80% plus of tokens processed, and those ratios may be here to stay." _______ Follow Fireside Alpha for more highlights of the best business and technology conversations.

Gavin Baker Gavin Baker says open source is "awesome" for AI infrastructure names because it shifts the economic value from the frontier labs to the infrastructure, and it is already 80% plus of all tokens processed. "A misconception that a lot of people have is that open source models are somehow bad for AI. They're awesome for the AI infrastructure providers. They just shift economic value from the margins of the frontier labs to the infrastructure." "But I do think there's still a role for these frontier models. And it may be true, to date, frontier tokens are capturing 90% of the economic value, and open source tokens are probably 80% plus of tokens processed, and those ratios may be here to stay." _______ Follow Fireside Alpha for more highlights of the best business and technology conversations.

Fireside Alpha

162,832 просмотров • 3 месяцев назад

"Most enterprise workflows don't require frontier AI models. Outside of coding, tasks like summarization, document generation, and briefing can often be handled effectively by lower-cost open-source models. The real value is knowing when you need frontier performance and when cheaper models are more than good enough." Michael Mignano What percent of enterprise workflows requires frontier models today Nikesh Arora Matan Grinberg Aaron Katz Lin Qiao

"Most enterprise workflows don't require frontier AI models. Outside of coding, tasks like summarization, document generation, and briefing can often be handled effectively by lower-cost open-source models. The real value is knowing when you need frontier performance and when cheaper models are more than good enough." Michael Mignano What percent of enterprise workflows requires frontier models today Nikesh Arora Matan Grinberg Aaron Katz Lin Qiao

Harry Stebbings

36,428 просмотров • 3 месяцев назад

“AI MODELS COULD BECOME 10× SMALLER WITHOUT LOSING MOST OF THEIR INTELLIGENCE.” Mark Zuckerberg says one of the most powerful developments in AI is model distillation: taking the capabilities of a much larger model and transferring them into a much smaller one. - Meta's huge frontier models can serve as "teachers" for smaller models. - Zuckerberg says a model could potentially retain around 90–95% of the larger model's intelligence while being only ~10% of its size. - That means dramatically lower inference costs, memory requirements and computing power. - Developers can distill and customize these capabilities for their own applications. And that's where open-source models come into the picture.

“AI MODELS COULD BECOME 10× SMALLER WITHOUT LOSING MOST OF THEIR INTELLIGENCE.” Mark Zuckerberg says one of the most powerful developments in AI is model distillation: taking the capabilities of a much larger model and transferring them into a much smaller one. - Meta's huge frontier models can serve as "teachers" for smaller models. - Zuckerberg says a model could potentially retain around 90–95% of the larger model's intelligence while being only ~10% of its size. - That means dramatically lower inference costs, memory requirements and computing power. - Developers can distill and customize these capabilities for their own applications. And that's where open-source models come into the picture.

SciTech Era

77,374 просмотров • 2 месяцев назад

BREAKING: Only ~1% of Bending Spoons’ AI requests & tokens goes to frontier models. 99% runs on self-hosted open-weight models. CEO Luca Ferrari on the shift to Sovereign AI: “We end up using open-weight models we self-host, so these are basically free. I mean, there is a little bit of cloud cost, but for 99% of the requests and tokens.” “And the frontier models, generally closed weight, through APIs for maybe 1% of the requests, only for the most complex tasks.” “Sometimes we use them automatically to check the work that got done by the slightly less intelligent models, as a more senior engineer would with a more junior engineer.”

BREAKING: Only ~1% of Bending Spoons’ AI requests & tokens goes to frontier models. 99% runs on self-hosted open-weight models. CEO Luca Ferrari on the shift to Sovereign AI: “We end up using open-weight models we self-host, so these are basically free. I mean, there is a little bit of cloud cost, but for 99% of the requests and tokens.” “And the frontier models, generally closed weight, through APIs for maybe 1% of the requests, only for the most complex tasks.” “Sometimes we use them automatically to check the work that got done by the slightly less intelligent models, as a more senior engineer would with a more junior engineer.”

Molly O’Shea

243,526 просмотров • 1 месяц назад

learned a lot from this conversation with Simon Mo and Matt Bornstein. biggest takeaways for me: -there are a lot of reasons why we should like open-weight models. a lot of these arguments stop at handwavy things like "what if the labs stop releasing frontier models to the public" or "it's lower cost." but simon's position as lead maintainer of vLLM and CEO of Inferact give him authority to talk about some of the other, more interesting and concrete reasons to pay attention to open-weight models, namely that they allow end-users to calibrate latency / other performance metrics with way more customizability than what any of the frontier closed-source labs offer (and without the fear that your job might be met with a refusal at some random point where you're deep in a 2 hour job) -re: the above point...for this reason, a lot of US companies (inferact included!) choose to use open-weight models over their closed-source alternatives. this also isn't limited to internal workloads / research - on a recent a16z podcast the team at Decagon spoke about how something like 90% of their customer service ai agents run on open-weight models that they've fine-tuned. -we should really appreciate how many companies/teams came out researchers fascinated by the wave of very small open-weight models that were being distilled from e.g. gpt-3.5 and earlier models in 2022/2023 (prior to the release of chatGPT!). these small models motivated the development of pagedattention, which then led to vlmm/inferact (at other layers of the stack with similar origin stories, you can look at teams like openrouter or ollama). in other words, we have open-weight models to thank for a bunch of the orchestration infra we now rely on. i think yet another, indirect, way we can point to open-source/weight infra pushing the frontier forward. anyway, a lot more in this convo, it was a lot of fun!

learned a lot from this conversation with Simon Mo and Matt Bornstein. biggest takeaways for me: -there are a lot of reasons why we should like open-weight models. a lot of these arguments stop at handwavy things like "what if the labs stop releasing frontier models to the public" or "it's lower cost." but simon's position as lead maintainer of vLLM and CEO of Inferact give him authority to talk about some of the other, more interesting and concrete reasons to pay attention to open-weight models, namely that they allow end-users to calibrate latency / other performance metrics with way more customizability than what any of the frontier closed-source labs offer (and without the fear that your job might be met with a refusal at some random point where you're deep in a 2 hour job) -re: the above point...for this reason, a lot of US companies (inferact included!) choose to use open-weight models over their closed-source alternatives. this also isn't limited to internal workloads / research - on a recent a16z podcast the team at Decagon spoke about how something like 90% of their customer service ai agents run on open-weight models that they've fine-tuned. -we should really appreciate how many companies/teams came out researchers fascinated by the wave of very small open-weight models that were being distilled from e.g. gpt-3.5 and earlier models in 2022/2023 (prior to the release of chatGPT!). these small models motivated the development of pagedattention, which then led to vlmm/inferact (at other layers of the stack with similar origin stories, you can look at teams like openrouter or ollama). in other words, we have open-weight models to thank for a bunch of the orchestration infra we now rely on. i think yet another, indirect, way we can point to open-source/weight infra pushing the frontier forward. anyway, a lot more in this convo, it was a lot of fun!

Elena

12,922 просмотров • 2 месяцев назад

Bending Spoons self-hosts open-weight models for ~99% of AI requests and tokens, using frontier models for only ~1% of the most complex work or to check the open models, staying fully vendor-neutral while keeping token costs extremely low. - Luca Ferrari, CEO and co-founder Bending Spoons, a Milan-based Italian tech company

Bending Spoons self-hosts open-weight models for ~99% of AI requests and tokens, using frontier models for only ~1% of the most complex work or to check the open models, staying fully vendor-neutral while keeping token costs extremely low. - Luca Ferrari, CEO and co-founder Bending Spoons, a Milan-based Italian tech company

Rohan Paul

27,417 просмотров • 1 месяц назад

Osmo is rewriting the story of fragrance. They are digitizing scent with AI, and the economics now make sense. Osmo builds tools that generate fragrance formulas from creative briefs, but evaluating results requires physically producing each scent. After moving from proprietary frontier model APIs to Meta Llama on AWS, Osmo gained full control of its models and infrastructure, cutting training costs by 20x and inference costs by 200x. In evaluations by trained perfumers, its open-model system matched the performance of leading frontier models. That's nothing to sneeze at.

Osmo is rewriting the story of fragrance. They are digitizing scent with AI, and the economics now make sense. Osmo builds tools that generate fragrance formulas from creative briefs, but evaluating results requires physically producing each scent. After moving from proprietary frontier model APIs to Meta Llama on AWS, Osmo gained full control of its models and infrastructure, cutting training costs by 20x and inference costs by 200x. In evaluations by trained perfumers, its open-model system matched the performance of leading frontier models. That's nothing to sneeze at.

AWS Startups

9,003,742 просмотров • 5 месяцев назад

Today we're shipping Nemotron 3 Ultra. A 550B MoE frontier-intelligence open model built for long-running agents. It delivers 5x faster inference and lowers the cost of complex agentic tasks by up to 30% versus other open frontier models.

Today we're shipping Nemotron 3 Ultra. A 550B MoE frontier-intelligence open model built for long-running agents. It delivers 5x faster inference and lowers the cost of complex agentic tasks by up to 30% versus other open frontier models.

NVIDIA AI

1,331,256 просмотров • 4 месяцев назад

"You need frontier products in order to have frontier models and for people to feel the magic of frontier models." Full conversation with Dianne Penn (Head of Product for Research and Labs Anthropic):

"You need frontier products in order to have frontier models and for people to feel the magic of frontier models." Full conversation with Dianne Penn (Head of Product for Research and Labs Anthropic):

Lenny Rachitsky

17,966 просмотров • 3 месяцев назад

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."

Fireside Alpha

14,114 просмотров • 2 месяцев назад

New chips are shipping faster than ever before, but the ecosystem is being held back by having to rewrite and then re-debug the same code over & over again. Chris Lattner CEO and Co-founder at Modular and EVP of Advanced AI Software & Platforms at Qualcomm, will deliver a keynote at PyTorch Conference North America about an open software platform for heterogeneous compute powered by Mojo and MAX. PyTorch has always been the place where the best models come together, and now there's a way to get those models onto all kinds of hardware. If you're interested in Al and compute, join us at the PyTorch Conference in San Jose, CA. Register now: #PyTorchCon

New chips are shipping faster than ever before, but the ecosystem is being held back by having to rewrite and then re-debug the same code over & over again. Chris Lattner CEO and Co-founder at Modular and EVP of Advanced AI Software & Platforms at Qualcomm, will deliver a keynote at PyTorch Conference North America about an open software platform for heterogeneous compute powered by Mojo and MAX. PyTorch has always been the place where the best models come together, and now there's a way to get those models onto all kinds of hardware. If you're interested in Al and compute, join us at the PyTorch Conference in San Jose, CA. Register now: #PyTorchCon

PyTorch

20,838 просмотров • 1 месяц назад

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.

Base Labs

245,571 просмотров • 1 месяц назад

We’ve been building Boundless AI, the inference partner for AI-native startups. Boundless helps teams run open-weight models tuned to their product, for better economics on frontier intelligence.

We’ve been building Boundless AI, the inference partner for AI-native startups. Boundless helps teams run open-weight models tuned to their product, for better economics on frontier intelligence.

Boundless

23,354 просмотров • 1 месяц назад

It's been just over a year since we started Meta Superintelligence Labs. Muse Spark was our first family of models, and now Muse Wonder is a much larger and more advanced class of models that brings us to the frontier. We're releasing two models today. Muse Wonder for fast frontier performance and Muse Wonder Full for the full version of the model.

It's been just over a year since we started Meta Superintelligence Labs. Muse Spark was our first family of models, and now Muse Wonder is a much larger and more advanced class of models that brings us to the frontier. We're releasing two models today. Muse Wonder for fast frontier performance and Muse Wonder Full for the full version of the model.

Mark Zuckerberg

626,048 просмотров • 15 дней назад

Different voices. Same answer: open models. NVIDIA Founder and CEO Jensen Huang sat down with the leaders from Mistral AI, BlackForestLabsAI - Unofficial, Cursor, LangChain, Perplexity, Reflection, Thinking Machines, Ai2, , and AMP PBC to discuss the rapid rise of open frontier models. Get the top takeaways from the frontier of AI:

Different voices. Same answer: open models. NVIDIA Founder and CEO Jensen Huang sat down with the leaders from Mistral AI, BlackForestLabsAI - Unofficial, Cursor, LangChain, Perplexity, Reflection, Thinking Machines, Ai2, , and AMP PBC to discuss the rapid rise of open frontier models. Get the top takeaways from the frontier of AI:

NVIDIA

61,869 просмотров • 7 месяцев назад

Introducing Kardashev-0.7, the world’s first trained swarm made of 32 distinct models Trained with RL for Population Scaling (RLPS), 32 models organically develop specialization & complementary capabilities, delivering frontier performance at: - 0.007x ~ 0.02x of the inference cost - 0.03x of the required memory Civilization advances through different minds specializing and working together. We’re bringing that principle into AI Banbury Road, we’re scaling intelligence by model count, toward civilizations of models that learn to build on one another

Introducing Kardashev-0.7, the world’s first trained swarm made of 32 distinct models Trained with RL for Population Scaling (RLPS), 32 models organically develop specialization & complementary capabilities, delivering frontier performance at: - 0.007x ~ 0.02x of the inference cost - 0.03x of the required memory Civilization advances through different minds specializing and working together. We’re bringing that principle into AI Banbury Road, we’re scaling intelligence by model count, toward civilizations of models that learn to build on one another

engineer cat 🐈

427,171 просмотров • 22 дней назад

Chamath is making one of the most important business arguments of 2026. Half of large US companies right now cannot generate returns that exceed their cost of capital, which has normalized back to its long run average of 8 to 11%. Another one in seven companies globally is stuck generating persistent returns between 1 and 5% and most businesses don't have room for error and in this environment walks every frontier AI lab saying the same thing, give us your data, your workflows, your processes and our model will make everything better. And companies by the millions said yes. What they didn't fully account for is what happens on the other side of that door. Every time an employee runs a query through a frontier model API, the prompt goes through external servers, workflows, customer data, pricing logic, internal processes, all of it transmitted through a third party. As Alex Karp said companies are spending on tokens while handing over the exact proprietary advantages that make their business worth owning. Microsoft blocked internal use of Anthropic's Claude Fable 5 but over its 30-day data retention policy and the largest software company in the world decided a frontier model's data handling was too risky for its own employees. A US government action revoked access to another frontier model for foreign nationals overnight. Now here's where the cost math becomes impossible to ignore. Deutsche Bank calculated a roughly 65x cost gap between frontier models like Claude Fable 5 at ~$3.25 per task and open-source alternatives at ~$0.05. For 90% of everyday enterprise tasks, performance is comparable. Open-weight models now match closed frontier systems on core agent tasks at roughly one-tenth the cost, a high-volume deployment that costs $250/day on Claude runs at $12/day on an open-source equivalent. Chamath Palihapitiya tested this directly by running a standard enterprise code migration task through an orchestration layer wrapping an open-source model came in 16.4x cheaper than using a frontier model directly.

Chamath is making one of the most important business arguments of 2026. Half of large US companies right now cannot generate returns that exceed their cost of capital, which has normalized back to its long run average of 8 to 11%. Another one in seven companies globally is stuck generating persistent returns between 1 and 5% and most businesses don't have room for error and in this environment walks every frontier AI lab saying the same thing, give us your data, your workflows, your processes and our model will make everything better. And companies by the millions said yes. What they didn't fully account for is what happens on the other side of that door. Every time an employee runs a query through a frontier model API, the prompt goes through external servers, workflows, customer data, pricing logic, internal processes, all of it transmitted through a third party. As Alex Karp said companies are spending on tokens while handing over the exact proprietary advantages that make their business worth owning. Microsoft blocked internal use of Anthropic's Claude Fable 5 but over its 30-day data retention policy and the largest software company in the world decided a frontier model's data handling was too risky for its own employees. A US government action revoked access to another frontier model for foreign nationals overnight. Now here's where the cost math becomes impossible to ignore. Deutsche Bank calculated a roughly 65x cost gap between frontier models like Claude Fable 5 at ~$3.25 per task and open-source alternatives at ~$0.05. For 90% of everyday enterprise tasks, performance is comparable. Open-weight models now match closed frontier systems on core agent tasks at roughly one-tenth the cost, a high-volume deployment that costs $250/day on Claude runs at $12/day on an open-source equivalent. Chamath Palihapitiya tested this directly by running a standard enterprise code migration task through an orchestration layer wrapping an open-source model came in 16.4x cheaper than using a frontier model directly.

Milk Road AI

282,224 просмотров • 3 месяцев назад

Aligned with Matan Grinberg's intuition that in the next 12 months, 90% of tokens could be going to open models. We're seeing more and more companies using frontier APIs for experimenting and open-source or private models for production and larger workloads!

Aligned with Matan Grinberg's intuition that in the next 12 months, 90% of tokens could be going to open models. We're seeing more and more companies using frontier APIs for experimenting and open-source or private models for production and larger workloads!

clem 🤗

24,138 просмотров • 3 месяцев назад