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Just used Akash Chat from for the first time. It's actually really impressive. Open source LLM model run on permissionless Akash compute. -Super fast and responsive. -No account or sign in needed. One of the few actually useful instances of crypto + AI.

Jesse Eckel

40,305 subscribers

33,890 просмотров • 2 лет назад •via X (Twitter)

Anya Rossi• Live Now

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Qwen3-235B-A22B-2507, the latest state-of-the-art open-source AI model from Alibaba, is now available to deploy on Akash using a template to deploy in under 60 seconds. Non-custodial. Opensource. Permissionless.

Qwen3-235B-A22B-2507, the latest state-of-the-art open-source AI model from Alibaba, is now available to deploy on Akash using a template to deploy in under 60 seconds. Non-custodial. Opensource. Permissionless.

Akash Network

16,220 просмотров • 1 год назад

📢 NEURALAI X 🤩 NeuralAI is extraordinarily excited to reveal that we have started working closely with Akash Network, the world's premier decentralized compute marketplace! ✔️ The offerings of align perfectly with the vision and roadmap of NeuralAI, making this collaboration extremely significant for NeuralAI. For a start, NeuralAI will run inference on Akash based on our 3D #AI model and prepare TAO miners for reward-yielding subnets. NeuralAI will also leverage Akash's powerful GPUs to enhance our #AI #model training. Currently, provides us with 56 powerful A6000 GPUs, scalable on demand. ✔️ NeuralAI also gets highly competitive pricing from Akash Network, significantly lower than any other compute provider in the world. The lower pricing is possible partially thanks to the crypto-driven incentives within Akash Network, making it an excellent opportunity for NeuralAI to leverage. The synergy with Akash Network is a big leap forward, fueling our mission of becoming the #1 AI-driven 3D asset generator subnet on Bittensor. ✔️ We look forward to our close work with , we are certain with their support we will be able to secure a unique edge in the market! Stay tuned for exciting updates and developments.

📢 NEURALAI X 🤩 NeuralAI is extraordinarily excited to reveal that we have started working closely with Akash Network, the world's premier decentralized compute marketplace! ✔️ The offerings of align perfectly with the vision and roadmap of NeuralAI, making this collaboration extremely significant for NeuralAI. For a start, NeuralAI will run inference on Akash based on our 3D #AI model and prepare TAO miners for reward-yielding subnets. NeuralAI will also leverage Akash's powerful GPUs to enhance our #AI #model training. Currently, provides us with 56 powerful A6000 GPUs, scalable on demand. ✔️ NeuralAI also gets highly competitive pricing from Akash Network, significantly lower than any other compute provider in the world. The lower pricing is possible partially thanks to the crypto-driven incentives within Akash Network, making it an excellent opportunity for NeuralAI to leverage. The synergy with Akash Network is a big leap forward, fueling our mission of becoming the #1 AI-driven 3D asset generator subnet on Bittensor. ✔️ We look forward to our close work with , we are certain with their support we will be able to secure a unique edge in the market! Stay tuned for exciting updates and developments.

NeuralAI

241,287 просмотров • 2 лет назад

Introducing `gpt-llm-trainer` ✍️ The world's simplest way to train a task-specific LLM. **Just write a sentence describing the model you want.** A chain of AI systems will generate a dataset and train a model for you. And it's open-source:

Introducing `gpt-llm-trainer` ✍️ The world's simplest way to train a task-specific LLM. Just write a sentence describing the model you want. A chain of AI systems will generate a dataset and train a model for you. And it's open-source:

Matt Shumer

337,464 просмотров • 3 лет назад

Introducing `claude-llm-trainer` ✍️ The world's simplest way to train a task-specific LLM. Just write a sentence describing the model you want. A chain of AI systems will generate a dataset and train a model for you. And it's open-source.

Introducing `claude-llm-trainer` ✍️ The world's simplest way to train a task-specific LLM. Just write a sentence describing the model you want. A chain of AI systems will generate a dataset and train a model for you. And it's open-source.

Matt Shumer

383,208 просмотров • 2 лет назад

AkashChat has a brand new look. · An updated, modern interface. · Ultra-fast and responsive. · Access to 8 of the leading open-source AI models. Still completely free to use, with no sign-in required.

Akash Network

33,712 просмотров • 1 год назад

ADAM CURRY: I'M NOT A BIG BELIEVER IN AI "I run these models at home on my own computer. I run the llama model, which is, Meta. They've open sourced it. I've, I've run the French one, and then DeepSeek, and it's not very impressive. It's just not. The the error rate is too high, so I'm I'm skeptical of it." Source: Joe Rogan

ADAM CURRY: I'M NOT A BIG BELIEVER IN AI "I run these models at home on my own computer. I run the llama model, which is, Meta. They've open sourced it. I've, I've run the French one, and then DeepSeek, and it's not very impressive. It's just not. The the error rate is too high, so I'm I'm skeptical of it." Source: Joe Rogan

Mario Nawfal

202,113 просмотров • 1 год назад

ELON: CHINA HAS THE BEST OPEN SOURCE AI MODELS RIGHT NOW “The best open source models are generally from China, which is bizarre. I think the second best one, or maybe it’s better than second best, is Grok 2.5. The open source model is actually very good, and we will continue to open source our models.” Source: The All-In Podcast

ELON: CHINA HAS THE BEST OPEN SOURCE AI MODELS RIGHT NOW “The best open source models are generally from China, which is bizarre. I think the second best one, or maybe it’s better than second best, is Grok 2.5. The open source model is actually very good, and we will continue to open source our models.” Source: The All-In Podcast

Mario Nawfal

1,163,582 просмотров • 9 месяцев назад

Elon Musk: In the future, every country will have its own AI clusters. “It's currently very difficult to actually build an AI cluster and have it run. It's not like just pulling a computer out of a box. They are currently very difficult to run. And are you going to be training a frontier model? Because if you're training a frontier model, then you need a massive amount of compute and a level of technical skill that only a few companies possess. But, over time, I think every country will have AI compute clusters. It's just going to be a normal thing.” Future Investment Initiative, October 29, 2024

Elon Musk: In the future, every country will have its own AI clusters. “It's currently very difficult to actually build an AI cluster and have it run. It's not like just pulling a computer out of a box. They are currently very difficult to run. And are you going to be training a frontier model? Because if you're training a frontier model, then you need a massive amount of compute and a level of technical skill that only a few companies possess. But, over time, I think every country will have AI compute clusters. It's just going to be a normal thing.” Future Investment Initiative, October 29, 2024

ELON CLIPS

95,313 просмотров • 8 месяцев назад

Is compute scarcity masking the real economics of AI? interesting point from Peter Fenton: open-weight models are still complex to run well. For now, the big labs have the bundle: model + compute + reliability + access... and thus pricing power. But if compute supply loosens and open model tooling gets easier, routine AI work can more easily move to cheaper/open models.

Is compute scarcity masking the real economics of AI? interesting point from Peter Fenton: open-weight models are still complex to run well. For now, the big labs have the bundle: model + compute + reliability + access... and thus pricing power. But if compute supply loosens and open model tooling gets easier, routine AI work can more easily move to cheaper/open models.

Deirdre Bosa

50,558 просмотров • 1 месяц назад

$A crypto project actually trained a 72B parameter AI model from scratch using decentralized GPU compute. Not fine-tuned, not a wrapper: trained from zero. The model benchmarks competitively against Meta's LLaMA 3 on reasoning tasks, and the entire training run cost a fraction of what centralized labs spend. If decentralized compute can produce frontier-class models, the moat around OpenAI and Anthropic is thinner than people think.$

A crypto project actually trained a 72B parameter AI model from scratch using decentralized GPU compute. Not fine-tuned, not a wrapper: trained from zero. The model benchmarks competitively against Meta's LLaMA 3 on reasoning tasks, and the entire training run cost a fraction of what centralized labs spend. If decentralized compute can produce frontier-class models, the moat around OpenAI and Anthropic is thinner than people think.

VirtualBacon

40,647 просмотров • 4 месяцев назад

We deployed a fully private AI agent on NuNet in under 5 minutes 🚀 OpenClaw🦞 running Qwen through ollama , one of the hottest open source model families right now, entirely on decentralized compute. No cloud. No API keys. No data leaving the machine. This is what private AI looks like when you actually build it instead of just talking about it. Your model. Your hardware. Your rules. Full walkthrough showing exactly how it works: What should we deploy next?

We deployed a fully private AI agent on NuNet in under 5 minutes 🚀 OpenClaw🦞 running Qwen through ollama , one of the hottest open source model families right now, entirely on decentralized compute. No cloud. No API keys. No data leaving the machine. This is what private AI looks like when you actually build it instead of just talking about it. Your model. Your hardware. Your rules. Full walkthrough showing exactly how it works: What should we deploy next?

NuNet 🌐

87,598 просмотров • 4 месяцев назад

.Bridgit Mendler on the founding thesis of Northwood. "We really were recognizing the same problem, that networking for the space economy is really what drives its value. And there's a significant bottleneck, and that bottleneck actually is not in space. It's actually on Earth. The way that you actually solve that networking problem for space is through introducing a model that gets you to the scale that you need, not just for one individual company or a mission, but for the industry overall." Full "Valley of Depth" episode in comments 👇

.Bridgit Mendler on the founding thesis of Northwood. "We really were recognizing the same problem, that networking for the space economy is really what drives its value. And there's a significant bottleneck, and that bottleneck actually is not in space. It's actually on Earth. The way that you actually solve that networking problem for space is through introducing a model that gets you to the scale that you need, not just for one individual company or a mission, but for the industry overall." Full "Valley of Depth" episode in comments 👇

Mo Islam

137,904 просмотров • 1 месяц назад

Build agents that can actually do real-world tasks! Agent Reinforcement Trainer (ART) is a framework to train multi-step LLM agents for real-world tasks using GRPO. Just a few lines of code. No manual rewards needed. vLLM + Unsloth combined 🚀 100% open-source.

Build agents that can actually do real-world tasks! Agent Reinforcement Trainer (ART) is a framework to train multi-step LLM agents for real-world tasks using GRPO. Just a few lines of code. No manual rewards needed. vLLM + Unsloth combined 🚀 100% open-source.

Akshay 🚀

38,297 просмотров • 6 месяцев назад

If OpenAI actually pulls this off and builds billions of GPUs, every single human on Earth would basically have access to their own GPU. That’s the vision of Universal Basic Compute Sam Altman has been hinting at, compute as abundant and accessible as electricity. Not just AI for the few, but AI for everyone.

If OpenAI actually pulls this off and builds billions of GPUs, every single human on Earth would basically have access to their own GPU. That’s the vision of Universal Basic Compute Sam Altman has been hinting at, compute as abundant and accessible as electricity. Not just AI for the few, but AI for everyone.

VraserX e/acc

82,484 просмотров • 10 месяцев назад

Introducing `AutoRL` 📈 The world's simplest way to train a task-specific LLM with RL. *Just write a SENTENCE describing the model you want.* A chain of AI systems will generate data + rubrics and train a model for you. Powered by ART, it's open source. Link in thread:

Introducing `AutoRL` 📈 The world's simplest way to train a task-specific LLM with RL. Just write a SENTENCE describing the model you want. A chain of AI systems will generate data + rubrics and train a model for you. Powered by ART, it's open source. Link in thread:

Matt Shumer

150,107 просмотров • 1 год назад

A decentralized AI compute play just outran 99% of the crypto market this month $AKT, the native token of Akash Network, is up 72% on the month and leading CoinMarketCap's Broad Market category despite a 12% pullback Saturday testing $0.595 support. The DePIN GPU marketplace lets users rent NVIDIA H100s for $1.49/hour vs $43K outright. Founder Greg Osuri presented at the Economist Impact AI Compute Summit in Amsterdam this week. Recent integrations with Venice ai and FLock io target decentralized AI inference, the same vertical NEAR and Sahara AI are also rotating into.

A decentralized AI compute play just outran 99% of the crypto market this month $AKT, the native token of Akash Network, is up 72% on the month and leading CoinMarketCap's Broad Market category despite a 12% pullback Saturday testing $0.595 support. The DePIN GPU marketplace lets users rent NVIDIA H100s for $1.49/hour vs $43K outright. Founder Greg Osuri presented at the Economist Impact AI Compute Summit in Amsterdam this week. Recent integrations with Venice ai and FLock io target decentralized AI inference, the same vertical NEAR and Sahara AI are also rotating into.

BSCN

10,315 просмотров • 2 месяцев назад

The bottleneck in LLM inference isn't compute. It's how fast you can move the weights. Our CTO Mathias Lechner, Mathias Lechner, joins Piotr Mazurek, Piotr Mazurek (in SF 🌉), from our inference team, to discuss what actually limits token throughput and how we're optimizing for it.

The bottleneck in LLM inference isn't compute. It's how fast you can move the weights. Our CTO Mathias Lechner, Mathias Lechner, joins Piotr Mazurek, Piotr Mazurek (in SF 🌉), from our inference team, to discuss what actually limits token throughput and how we're optimizing for it.

Liquid AI

21,393 просмотров • 2 месяцев назад

I got a diffusion model to run really fast, glued it to a LLM, and it blows my mind how much of the interactivity we expect from software (editing text, dragging things around, clicking on "links") just works™️ in pixel space. The future of software is bright

I got a diffusion model to run really fast, glued it to a LLM, and it blows my mind how much of the interactivity we expect from software (editing text, dragging things around, clicking on "links") just works™️ in pixel space. The future of software is bright

Zain Shah

22,455 просмотров • 1 месяц назад

🚨 FRANCOIS CHOLLET SAYS THE CURRENT LLM STACK IS THE WRONG PATH TO AGI Interviewer: “When will we accomplish the first definition of AGI?” Chollet confirms we are already on that exact trajectory, pointing out that: "Current technology can fully automate at human level or beyond any domain where you have verifiable rewards, right? And code... code being the first one." He then brings up the problem with LLMs, stating that: "It's possible in principle to build something that looks a lot like AGI on top of the LLM stack... I do believe, however, this would be the wrong thing to do because it would be very inefficient." Looking ahead to what the final architecture will actually require, he explains that: "AI research will have to trend towards not just efficiency, but in fact optimality over time. And for this reason, future AI in a few decades... it's not going to be this harness on top of a reasoning model on top of a base LLM."

🚨 FRANCOIS CHOLLET SAYS THE CURRENT LLM STACK IS THE WRONG PATH TO AGI Interviewer: “When will we accomplish the first definition of AGI?” Chollet confirms we are already on that exact trajectory, pointing out that: "Current technology can fully automate at human level or beyond any domain where you have verifiable rewards, right? And code... code being the first one." He then brings up the problem with LLMs, stating that: "It's possible in principle to build something that looks a lot like AGI on top of the LLM stack... I do believe, however, this would be the wrong thing to do because it would be very inefficient." Looking ahead to what the final architecture will actually require, he explains that: "AI research will have to trend towards not just efficiency, but in fact optimality over time. And for this reason, future AI in a few decades... it's not going to be this harness on top of a reasoning model on top of a base LLM."

Chris

15,578 просмотров • 4 месяцев назад