正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

🚨Today we’re rolling out Prompt Caching on GroqCloud. Keep hot prompts in memory, cut cached token costs by 50% and slash latency. Faster response, smarter inference. Learn more 👇

Groq Inc

89,828 subscribers

20,383 次观看 • 10 个月前 •via X (Twitter)

健康养生科学技术教育

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

PSA: Prompt caching is now live for moonshotai/kimi-k2-instruct on Groq Inc: → 50% discount for cached tokens → Lower latency → Automatic prefix matching Excited to roll this out starting with Kimi K2 and even more excited for inexpensive, fast vibe coding for all. ⚡️

PSA: Prompt caching is now live for moonshotai/kimi-k2-instruct on Groq Inc: → 50% discount for cached tokens → Lower latency → Automatic prefix matching Excited to roll this out starting with Kimi K2 and even more excited for inexpensive, fast vibe coding for all. ⚡️

Hatice Ozen

67,965 次观看 • 10 个月前

🚨 New model alert! Dialog by vibx — a leading text-to-speech model — now runs on GroqCloud™. That means natural-sounding speech with ultra-low latency, making real-time voice applications smoother and more responsive. Learn more & build fast — links in the comments!

🚨 New model alert! Dialog by vibx — a leading text-to-speech model — now runs on GroqCloud™. That means natural-sounding speech with ultra-low latency, making real-time voice applications smoother and more responsive. Learn more & build fast — links in the comments!

Groq Inc

47,183 次观看 • 1 年前

I added KV caching and INT8 KV quantization to our transformer inference, improving throughput by 35x. All of this was done from scratch in Rust + CUDA, on top of a homemade ML framework. On a 4-token prompt with 252 generated tokens: - Original: 0.76 tok/s - KV cache fp32: 27.21 tok/s - KV cache int8 (quantized): 27.29 tok/s Try it out yourself here: In practice: - KV caching gave us about a 35x end-to-end speedup - INT8 KV cache kept roughly the same speed as fp32 but cut KV cache memory by 3.78x FP32 cache used 4.5 MB in this run while the INT8 cache used only 1.19 MB This simple change to inference created a huge impact on performance. To learn more about the KV cache and other optimizations like this, check out the blog at

I added KV caching and INT8 KV quantization to our transformer inference, improving throughput by 35x. All of this was done from scratch in Rust + CUDA, on top of a homemade ML framework. On a 4-token prompt with 252 generated tokens: - Original: 0.76 tok/s - KV cache fp32: 27.21 tok/s - KV cache int8 (quantized): 27.29 tok/s Try it out yourself here: In practice: - KV caching gave us about a 35x end-to-end speedup - INT8 KV cache kept roughly the same speed as fp32 but cut KV cache memory by 3.78x FP32 cache used 4.5 MB in this run while the INT8 cache used only 1.19 MB This simple change to inference created a huge impact on performance. To learn more about the KV cache and other optimizations like this, check out the blog at

Reese Chong

52,588 次观看 • 2 个月前

New course announcement: Semantic Caching for AI Agents, taught by Tyler Hutcherson and Iliya Zhechev from Redis. Semantic caching can significantly reduce your AI application's inference costs and latency. If someone asks "How do I get a refund?" and another later asks "I want my money back," semantic caching recognizes these mean the same thing so it can use a cached response instead of making another model call. This short course takes you from building your first semantic cache from scratch to implementing production-ready systems using Redis' open-source tools. Skills you'll gain: - Build semantic caches from scratch, then implement them using Redis' SDK with production features - Measure cache performance using hit rate, precision, recall, and latency - Enhance accuracy with threshold tuning, cross-encoders, LLM validation, and fuzzy matching Join and learn to reduce your agentic AI's costs and improve speed!

New course announcement: Semantic Caching for AI Agents, taught by Tyler Hutcherson and Iliya Zhechev from Redis. Semantic caching can significantly reduce your AI application's inference costs and latency. If someone asks "How do I get a refund?" and another later asks "I want my money back," semantic caching recognizes these mean the same thing so it can use a cached response instead of making another model call. This short course takes you from building your first semantic cache from scratch to implementing production-ready systems using Redis' open-source tools. Skills you'll gain: - Build semantic caches from scratch, then implement them using Redis' SDK with production features - Measure cache performance using hit rate, precision, recall, and latency - Enhance accuracy with threshold tuning, cross-encoders, LLM validation, and fuzzy matching Join and learn to reduce your agentic AI's costs and improve speed!

Andrew Ng

61,939 次观看 • 7 个月前

Today we’re rolling out Zipline's March software update—1,100 changes to make deliveries faster, smarter, and more reliable. Key improvements include: • Deliveries in higher wind tolerance • Brighter droids that emit a cool blue glow at night • Customers can now order when stores are closed (Thread 🧵👇)

Today we’re rolling out Zipline's March software update—1,100 changes to make deliveries faster, smarter, and more reliable. Key improvements include: • Deliveries in higher wind tolerance • Brighter droids that emit a cool blue glow at night • Customers can now order when stores are closed (Thread 🧵👇)

Keller Rinaudo Cliffton

28,037 次观看 • 1 年前

🚨 GPT 5.6 on medium reasoning no skill used , straight out of api and one more point prompt was also short not lengthy one Prompted by ASI in Dev Mode > keep following us we will provide info before everyone and drop your hardest prompt we will run few prompts

🚨 GPT 5.6 on medium reasoning no skill used , straight out of api and one more point prompt was also short not lengthy one Prompted by ASI in Dev Mode > keep following us we will provide info before everyone and drop your hardest prompt we will run few prompts

Chetaslua

120,138 次观看 • 1 个月前

Today, we’re unveiling a new stablecoin, PayPal USD (PYUSD). It’s designed for payments and is backed by highly liquid and secure assets. Starting today and rolling out in the next few weeks, you’ll be able to buy, sell, hold and transfer PYUSD. Learn more

Today, we’re unveiling a new stablecoin, PayPal USD (PYUSD). It’s designed for payments and is backed by highly liquid and secure assets. Starting today and rolling out in the next few weeks, you’ll be able to buy, sell, hold and transfer PYUSD. Learn more

PayPal

3,739,271 次观看 • 2 年前

In this week's Stability Seconds, we’re getting you up to speed on how to use simple prompting techniques to create more precise images faster by not having to rewrite your prompt. Here’s how: 1️⃣ Use positive and negative prompts: Combine positive prompts to describe what should appear in the image, like “an editorial close-up,” and negative prompts to guide the model away from unwanted elements, such as “low resolution.” 2️⃣ Create prompt journals: When using Stable Image Ultra, try the prompt journal node to store a list of prompts directly into your workflow. This keeps your best prompts in one place, so you can easily reuse them or build on them later. You can find more prompting techniques and start creating with Stable Image Ultra here 👉

In this week's Stability Seconds, we’re getting you up to speed on how to use simple prompting techniques to create more precise images faster by not having to rewrite your prompt. Here’s how: 1️⃣ Use positive and negative prompts: Combine positive prompts to describe what should appear in the image, like “an editorial close-up,” and negative prompts to guide the model away from unwanted elements, such as “low resolution.” 2️⃣ Create prompt journals: When using Stable Image Ultra, try the prompt journal node to store a list of prompts directly into your workflow. This keeps your best prompts in one place, so you can easily reuse them or build on them later. You can find more prompting techniques and start creating with Stable Image Ultra here 👉

Stability AI

14,109 次观看 • 1 年前

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Milk Road AI

128,079 次观看 • 13 天前

🚀 Design Voice Prompts on the Rime Dashboard TODAY! Now you can spin up, test, and refine voice prompts faster than ever. + Try out any prompt in our sandbox + Swap between personality styles instantly + Hear how each voice delivers your script + Test everything before you deploy Whether it’s “drive-through pancakes” or your production flow, the Design Voice Prompts page helps you design richer, more natural voice interactions, without the guesswork. Try it out below!

🚀 Design Voice Prompts on the Rime Dashboard TODAY! Now you can spin up, test, and refine voice prompts faster than ever. + Try out any prompt in our sandbox + Swap between personality styles instantly + Hear how each voice delivers your script + Test everything before you deploy Whether it’s “drive-through pancakes” or your production flow, the Design Voice Prompts page helps you design richer, more natural voice interactions, without the guesswork. Try it out below!

lily clifford

268,490 次观看 • 7 个月前

The Working Families Tax Cut is rolling out now — and we are already starting to see the positive results in the economy and Americans getting to keep MORE of their own hard-earned money. After years of being hurt by failed Democrat policies, working families will finally keep more of what they earn: NO TAX on tips, NO TAX on overtime, and $10,000 MORE staying in the average American household. Republicans promised to put working families first — and we’re delivering on that promise.

The Working Families Tax Cut is rolling out now — and we are already starting to see the positive results in the economy and Americans getting to keep MORE of their own hard-earned money. After years of being hurt by failed Democrat policies, working families will finally keep more of what they earn: NO TAX on tips, NO TAX on overtime, and $10,000 MORE staying in the average American household. Republicans promised to put working families first — and we’re delivering on that promise.

Speaker Mike Johnson

83,326 次观看 • 5 个月前

🚂 Introducing Agent Prompt Train 🚂 An open-sourced high-performance proxy that works with Claude Code to help turn it from a single-developer tool into a collaborative development platform. Claude Code changed the game. Now, Agent Prompt Train is here to help teams: ✅ Log every conversation for review ✅ Analyze & optimize prompts with AI ✅ Turn best practices into reusable team commands ✨ The payoff: Smarter prompts, faster learning, and shared knowledge across your whole team. 📖 Read more on the blog & test it out 👇

🚂 Introducing Agent Prompt Train 🚂 An open-sourced high-performance proxy that works with Claude Code to help turn it from a single-developer tool into a collaborative development platform. Claude Code changed the game. Now, Agent Prompt Train is here to help teams: ✅ Log every conversation for review ✅ Analyze & optimize prompts with AI ✅ Turn best practices into reusable team commands ✨ The payoff: Smarter prompts, faster learning, and shared knowledge across your whole team. 📖 Read more on the blog & test it out 👇

Moonsong Labs

21,439 次观看 • 10 个月前

The team’s been heads-down all weekend pushing major upgrades to make Sympson AI smarter, faster, and more intuitive to trade with 🎶 Here’s the updates: • Multi-threaded intent analysis: Sympson can now process multiple signals from your prompts simultaneously, providing multiple insights from a single prompt. • Enhanced conversational context: Your past prompts and actions are now more deeply remembered and interpreted, so Sympson feels more like a co-pilot that actually knows your flow. • Improved asset detection: Better parsing of token names, pairs, and market context. • Advanced site analytics: We’ve added real-time tracking to see where users hit any potential friction to further improve the UX. Bonus: Stop Loss and Take Profit orders are in the testing phase — and should be rolling out shortly to give you even more control over your trades.

The team’s been heads-down all weekend pushing major upgrades to make Sympson AI smarter, faster, and more intuitive to trade with 🎶 Here’s the updates: • Multi-threaded intent analysis: Sympson can now process multiple signals from your prompts simultaneously, providing multiple insights from a single prompt. • Enhanced conversational context: Your past prompts and actions are now more deeply remembered and interpreted, so Sympson feels more like a co-pilot that actually knows your flow. • Improved asset detection: Better parsing of token names, pairs, and market context. • Advanced site analytics: We’ve added real-time tracking to see where users hit any potential friction to further improve the UX. Bonus: Stop Loss and Take Profit orders are in the testing phase — and should be rolling out shortly to give you even more control over your trades.

Symphony

10,834 次观看 • 1 年前

New course: Efficient Inference with SGLang: Text and Image Generation, built in partnership with LMSys LMSYS Org and RadixArk RadixArk, and taught by Richard Chen Richard Chen, a Member of Technical Staff at RadixArk. Running LLMs in production is expensive, and much of that cost comes from redundant computation. This short course teaches you to eliminate that waste using SGLang, an open-source inference framework that caches computation already done and reuses it across future requests. When ten users share the same system prompt, SGLang processes it once, not ten times. The speedups compound quickly, especially when there's a lot of shared context across requests. Skills you'll gain: - Implement a KV cache from scratch to eliminate redundant computation within a single request - Scale caching across users and requests with RadixAttention, so shared context is only processed once - Accelerate image generation with diffusion models using SGLang's caching and multi-GPU parallelism Join and learn to make LLM inference faster and more cost-efficient at scale!

New course: Efficient Inference with SGLang: Text and Image Generation, built in partnership with LMSys LMSYS Org and RadixArk RadixArk, and taught by Richard Chen Richard Chen, a Member of Technical Staff at RadixArk. Running LLMs in production is expensive, and much of that cost comes from redundant computation. This short course teaches you to eliminate that waste using SGLang, an open-source inference framework that caches computation already done and reuses it across future requests. When ten users share the same system prompt, SGLang processes it once, not ten times. The speedups compound quickly, especially when there's a lot of shared context across requests. Skills you'll gain: - Implement a KV cache from scratch to eliminate redundant computation within a single request - Scale caching across users and requests with RadixAttention, so shared context is only processed once - Accelerate image generation with diffusion models using SGLang's caching and multi-GPU parallelism Join and learn to make LLM inference faster and more cost-efficient at scale!

Andrew Ng

99,125 次观看 • 3 个月前

Context memory essentially unlocks Agentic AI Much needed for Opus 4.6's "multi-agent swarms" In this SemiDoped pod, Vikram Sekar talks to Val Bercovici from Weka about context storage. - How token warehouses save inference costs - A new networking tier? Context Storage Network! - High Bandwidth Flash for context? - Weka's Augmented Memory Grid for context storage - Where this is all headed The convo is info packed. Don't miss out on it! b/acc, context platform engineer Chapters (00:00) Introduction to Weka and AI Storage Solutions (05:18) The Evolution of Context Memory in AI (09:30) Understanding Memory Hierarchies and Their Impact (16:24) Latency Challenges in Modern Storage Solutions (21:32) The Role of Networking in AI Storage Efficiency (29:42) Dynamic Resource Utilization in AI Networks (30:04) Introducing the Context Memory Network (31:13) High Bandwidth Flash: A Game Changer (32:54) Weka’s Neural Mesh and Storage Solutions (35:01) Axon: Transforming GPU Storage into Memory (39:00) Augmented Memory Grid Explained (42:00) Pooling DRAM and CXL Innovations (46:02) Token Warehouses and Inference Economics (52:10) The Future of Storage Innovations

Context memory essentially unlocks Agentic AI Much needed for Opus 4.6's "multi-agent swarms" In this SemiDoped pod, Vikram Sekar talks to Val Bercovici from Weka about context storage. - How token warehouses save inference costs - A new networking tier? Context Storage Network! - High Bandwidth Flash for context? - Weka's Augmented Memory Grid for context storage - Where this is all headed The convo is info packed. Don't miss out on it! b/acc, context platform engineer Chapters (00:00) Introduction to Weka and AI Storage Solutions (05:18) The Evolution of Context Memory in AI (09:30) Understanding Memory Hierarchies and Their Impact (16:24) Latency Challenges in Modern Storage Solutions (21:32) The Role of Networking in AI Storage Efficiency (29:42) Dynamic Resource Utilization in AI Networks (30:04) Introducing the Context Memory Network (31:13) High Bandwidth Flash: A Game Changer (32:54) Weka’s Neural Mesh and Storage Solutions (35:01) Axon: Transforming GPU Storage into Memory (39:00) Augmented Memory Grid Explained (42:00) Pooling DRAM and CXL Innovations (46:02) Token Warehouses and Inference Economics (52:10) The Future of Storage Innovations

Semi Doped

12,796 次观看 • 5 个月前

Major $IOTAI Update Incoming 🧠 We’re excited to share that a powerful new update is rolling out next week, packed with features designed to make your experience faster, smarter, and smoother: What’s New: ⬇️ • All-New Swap Dashboard – Sleek, intuitive, and optimized for real-time swap analytics. • Favorites Section – Pin your most-loved tokens for instant access. • Revamped Token Search – Cleaner, smarter token views to help you find what you need, faster. • Community-Driven UI Enhancements – Built with your feedback at the core. • Swap Reliability Boost – Key bugs eliminated, with ongoing improvements for long-term stability. We’re building relentlessly behind the scenes to keep $IOTAI the fastest-growing and #1 AI-powered DEX Aggregator in crypto. With savings of up to 70% in gas fees, trading is about to get even more effortless. 🧬 And that’s not all—$SOL integration is coming soon, unlocking access for millions of new users. 🌍

Major $IOTAI Update Incoming 🧠 We’re excited to share that a powerful new update is rolling out next week, packed with features designed to make your experience faster, smarter, and smoother: What’s New: ⬇️ • All-New Swap Dashboard – Sleek, intuitive, and optimized for real-time swap analytics. • Favorites Section – Pin your most-loved tokens for instant access. • Revamped Token Search – Cleaner, smarter token views to help you find what you need, faster. • Community-Driven UI Enhancements – Built with your feedback at the core. • Swap Reliability Boost – Key bugs eliminated, with ongoing improvements for long-term stability. We’re building relentlessly behind the scenes to keep $IOTAI the fastest-growing and #1 AI-powered DEX Aggregator in crypto. With savings of up to 70% in gas fees, trading is about to get even more effortless. 🧬 And that’s not all—$SOL integration is coming soon, unlocking access for millions of new users. 🌍

IoTAI

46,814 次观看 • 10 个月前

OpenLora is the designated deployment engine for specialized models in the OpenLedger ecosystem. By leveraging just-in-time adapter switching, OpenLora enables the efficient serving of thousands of fine-tuned LoRA adapters on a single GPU, drastically reducing deployment costs. Unlike generic models, OpenLora-powered specialized models require fewer input tokens and produce more optimized, precise outputs - minimizing both input and output token size while delivering task-specific performance at scale. Faster, Smarter, and Specialized.

OpenLora is the designated deployment engine for specialized models in the OpenLedger ecosystem. By leveraging just-in-time adapter switching, OpenLora enables the efficient serving of thousands of fine-tuned LoRA adapters on a single GPU, drastically reducing deployment costs. Unlike generic models, OpenLora-powered specialized models require fewer input tokens and produce more optimized, precise outputs - minimizing both input and output token size while delivering task-specific performance at scale. Faster, Smarter, and Specialized.

OpenLedger

76,396 次观看 • 1 年前

Congratulations to the people of Karnataka on the flagging off of the Namma Metro Yellow Line and the Vande Bharat Express train in Bengaluru by PM Shri Narendra Modi Ji today. Three Vande Bharat trains connecting Bengaluru-Belagavi, Amritsar-Shri Mata Vaishno Devi Katra and Nagpur-Pune will fast-track our development journey by making travelling smarter, faster, and more efficient, while the new metro line in Bengaluru will further cut down commuting time by enhancing seamless travel.

Congratulations to the people of Karnataka on the flagging off of the Namma Metro Yellow Line and the Vande Bharat Express train in Bengaluru by PM Shri Narendra Modi Ji today. Three Vande Bharat trains connecting Bengaluru-Belagavi, Amritsar-Shri Mata Vaishno Devi Katra and Nagpur-Pune will fast-track our development journey by making travelling smarter, faster, and more efficient, while the new metro line in Bengaluru will further cut down commuting time by enhancing seamless travel.

Amit Shah

596,308 次观看 • 11 个月前

TOKENFI TOKEN LAUNCHER IS LIVE ON MAINNET IT’S OFFICIAL! TokenFi Token Launcher is live on the mainnet of five top EVM blockchains: Ethereum, BNB Chain, Base, Arbitrum, and opBNB. With TokenFi Token Launcher, ANYONE can create a fungible crypto token (ERC-20), NFT (ERC-721), or Multi-Token (ERC-1155) in just a few clicks, regardless of their crypto experience. The TokenFi platform and framework has been thoroughly audited and is being monitored in real-time by industry-leading blockchain security auditor Certik. This means that the tokens you create with TokenFi are safe and audited. TokenFi Token Launcher is powered by $TOKEN as its main utility token, and every successful transaction on the Ethereum and BNB blockchains buys and burns $TOKEN, enhancing its utility and making it perpetually deflationary as adoption of the platform grows. TokenFi Token Launcher aims to be the dominant token creation platform on EVM blockchains, and to this effect, we will be rolling out the platform on dozens of additional EVM blockchains in the coming weeks and months. You can learn more about TokenFi Token Launcher here:

TOKENFI TOKEN LAUNCHER IS LIVE ON MAINNET IT’S OFFICIAL! TokenFi Token Launcher is live on the mainnet of five top EVM blockchains: Ethereum, BNB Chain, Base, Arbitrum, and opBNB. With TokenFi Token Launcher, ANYONE can create a fungible crypto token (ERC-20), NFT (ERC-721), or Multi-Token (ERC-1155) in just a few clicks, regardless of their crypto experience. The TokenFi platform and framework has been thoroughly audited and is being monitored in real-time by industry-leading blockchain security auditor Certik. This means that the tokens you create with TokenFi are safe and audited. TokenFi Token Launcher is powered by $TOKEN as its main utility token, and every successful transaction on the Ethereum and BNB blockchains buys and burns $TOKEN, enhancing its utility and making it perpetually deflationary as adoption of the platform grows. TokenFi Token Launcher aims to be the dominant token creation platform on EVM blockchains, and to this effect, we will be rolling out the platform on dozens of additional EVM blockchains in the coming weeks and months. You can learn more about TokenFi Token Launcher here:

TokenFi

1,288,859 次观看 • 2 年前

Today we’re announcing our $50M Series A! Over the last year, we’ve grown from a small group with a big idea into a platform powering AI for hundreds of millions of end users across image, video, audio, and more. Since Q3, we’ve grown revenue by 10x, placing Runware comfortably among the fastest growing companies in the world. 🚀 When Runware started, the mission was simple: build the fastest and most reliable multimodal inference platform on the planet and make it accessible to everyone. We built our own Sonic Inference Engine, our own GPU infrastructure, our own inference pods, and a set of optimizations that push the limits of what an AI provider can do at scale. 2026 will be the year we bring inference closer to users than ever. We’re deploying more than twenty Runware Inference Pods across major cities in Europe and the US, delivering sub-10ms latency AI. And we’re extending Runware to support every model on Hugging Face, running them faster, cheaper, and more reliably than anyone else. A special thank you to Dawn Capital for leading this round, alongside Speedinvest Comcast Ventures Insight Partners a16z speedrun 🧊 Zero Prime VC Alex Menn at Begin Capital and all our early backers who supported us long before Runware looked like it does today.

Today we’re announcing our $50M Series A! Over the last year, we’ve grown from a small group with a big idea into a platform powering AI for hundreds of millions of end users across image, video, audio, and more. Since Q3, we’ve grown revenue by 10x, placing Runware comfortably among the fastest growing companies in the world. 🚀 When Runware started, the mission was simple: build the fastest and most reliable multimodal inference platform on the planet and make it accessible to everyone. We built our own Sonic Inference Engine, our own GPU infrastructure, our own inference pods, and a set of optimizations that push the limits of what an AI provider can do at scale. 2026 will be the year we bring inference closer to users than ever. We’re deploying more than twenty Runware Inference Pods across major cities in Europe and the US, delivering sub-10ms latency AI. And we’re extending Runware to support every model on Hugging Face, running them faster, cheaper, and more reliably than anyone else. A special thank you to Dawn Capital for leading this round, alongside Speedinvest Comcast Ventures Insight Partners a16z speedrun 🧊 Zero Prime VC Alex Menn at Begin Capital and all our early backers who supported us long before Runware looked like it does today.

Runware

63,519 次观看 • 7 个月前