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🚨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

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20,383 görüntüleme • 1 yıl önce •via X (Twitter)

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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

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🚨 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

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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

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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

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🚨 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

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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

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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

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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

83,352 görüntüleme • 7 ay önce

🚀 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 görüntüleme • 10 ay önce

🚨 YOUR GPU IS PROBABLY WASTING MORE THAN YOU THINK. vLLM is built to squeeze far more useful work out of your GPU when serving LLMs. Running an LLM at scale isn’t just about having a powerful GPU. The real problem is how efficiently you use its memory and compute. That’s where vLLM comes in. → High-throughput LLM inference and serving → PagedAttention for smarter KV-cache memory management → Continuous batching to keep GPUs busy → Prefix caching + chunked prefill → OpenAI-compatible API out of the box → Supports a huge range of modern LLM architectures → Quantization support for running models more efficiently And the crazy part? You can start an OpenAI-compatible inference server with: `vllm serve ` So your application can talk to your own model almost like it’s talking to OpenAI. The bigger idea: Don’t just buy more GPUs. Make the GPUs you already have work harder. That’s why vLLM has become such a major project in LLM inference. 🔥 #vLLM #AI #LLM #Inference #GPU #MachineLearning #AIInfrastructure #OpenSource #AIAgents #Developers

🚨 YOUR GPU IS PROBABLY WASTING MORE THAN YOU THINK. vLLM is built to squeeze far more useful work out of your GPU when serving LLMs. Running an LLM at scale isn’t just about having a powerful GPU. The real problem is how efficiently you use its memory and compute. That’s where vLLM comes in. → High-throughput LLM inference and serving → PagedAttention for smarter KV-cache memory management → Continuous batching to keep GPUs busy → Prefix caching + chunked prefill → OpenAI-compatible API out of the box → Supports a huge range of modern LLM architectures → Quantization support for running models more efficiently And the crazy part? You can start an OpenAI-compatible inference server with: `vllm serve ` So your application can talk to your own model almost like it’s talking to OpenAI. The bigger idea: Don’t just buy more GPUs. Make the GPUs you already have work harder. That’s why vLLM has become such a major project in LLM inference. 🔥 #vLLM #AI #LLM #Inference #GPU #MachineLearning #AIInfrastructure #OpenSource #AIAgents #Developers

Vikas gupta

14,138 görüntüleme • 16 gün önce

🚂 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 🌙

21,523 görüntüleme • 1 yıl önce

Researchers made LLM inference 14x faster and 90% cheaper. The video below depicts the speed up in action. Providers discount cached input tokens by as much as 90% because a cache hit skips prefill compute entirely. For stable system prompts and tool definitions, hit rates of 60 to 85% are achievable, which makes it the highest-leverage inference optimization. But the cost saving only works when the cached text is an exact, byte-for-byte prefix of the new request. If you change one character anywhere before it, the entire cached region is missed. Three common request patterns produce full cache misses: - A query that needs documents A and B together can't reuse B's standalone cache, because those KV entries were computed without A in front of them. - The same three documents retrieved in a different order produce a full cache miss, even though nothing about the documents changed. - In multi-turn conversations, every new turn invalidates whatever was cached beyond the stable prefix. Alibaba's production data did a study on this and found that just 10% of cached KV blocks serve 77% of all cache hits. So most of what gets cached sits in storage and is never used a single time. And the root cause is that KV entries are position-dependent. Each token's KV encodes attention to everything before it, so a cached block is only valid in the exact context it was computed in. There's a second, less discussed problem as well. Cache management runs inside the inference engine's process. Moving KV tensors between GPU, CPU, and disk competes with inference for the same resources. This is why Google's TurboQuant compresses KV caches to 3 bits with no accuracy loss and still causes a 20%+ slowdown when it runs in-process. Fixing both problems means restructuring where caching lives. Cache management moves into its own process, the engine only exchanges block IDs over shared GPU memory, and heavy data movement runs across GPU, CPU, disk, and remote storage in parallel. Non-prefix reuse gets handled by selectively recomputing only the small set of tokens that attend across document boundaries. LMCache is the open-source project (10k+ stars) that implements this exact architecture, and it plugs into vLLM, SGLang, and TensorRT-LLM. The selective recomputation part is implemented in its CacheBlend technique, which makes cached docs in any order and combination, with 2-4x faster multi-document processing. On H200s running Qwen3-235B with 50 concurrent users, LMCache's multiprocess mode delivers 14x faster time-to-first-token and 4x faster decoding compared to in-process caching. GitHub repo: (don't forget to star 🌟) My co-founder wrote a full breakdown of KV cache management. It covers the disaggregated architecture behind the 14x speed up, how CacheBlend preserves generation quality while skipping recomputation, and how to turn every document in a knowledge base into a reusable cached asset. Read it below.

Researchers made LLM inference 14x faster and 90% cheaper. The video below depicts the speed up in action. Providers discount cached input tokens by as much as 90% because a cache hit skips prefill compute entirely. For stable system prompts and tool definitions, hit rates of 60 to 85% are achievable, which makes it the highest-leverage inference optimization. But the cost saving only works when the cached text is an exact, byte-for-byte prefix of the new request. If you change one character anywhere before it, the entire cached region is missed. Three common request patterns produce full cache misses: - A query that needs documents A and B together can't reuse B's standalone cache, because those KV entries were computed without A in front of them. - The same three documents retrieved in a different order produce a full cache miss, even though nothing about the documents changed. - In multi-turn conversations, every new turn invalidates whatever was cached beyond the stable prefix. Alibaba's production data did a study on this and found that just 10% of cached KV blocks serve 77% of all cache hits. So most of what gets cached sits in storage and is never used a single time. And the root cause is that KV entries are position-dependent. Each token's KV encodes attention to everything before it, so a cached block is only valid in the exact context it was computed in. There's a second, less discussed problem as well. Cache management runs inside the inference engine's process. Moving KV tensors between GPU, CPU, and disk competes with inference for the same resources. This is why Google's TurboQuant compresses KV caches to 3 bits with no accuracy loss and still causes a 20%+ slowdown when it runs in-process. Fixing both problems means restructuring where caching lives. Cache management moves into its own process, the engine only exchanges block IDs over shared GPU memory, and heavy data movement runs across GPU, CPU, disk, and remote storage in parallel. Non-prefix reuse gets handled by selectively recomputing only the small set of tokens that attend across document boundaries. LMCache is the open-source project (10k+ stars) that implements this exact architecture, and it plugs into vLLM, SGLang, and TensorRT-LLM. The selective recomputation part is implemented in its CacheBlend technique, which makes cached docs in any order and combination, with 2-4x faster multi-document processing. On H200s running Qwen3-235B with 50 concurrent users, LMCache's multiprocess mode delivers 14x faster time-to-first-token and 4x faster decoding compared to in-process caching. GitHub repo: (don't forget to star 🌟) My co-founder wrote a full breakdown of KV cache management. It covers the disaggregated architecture behind the 14x speed up, how CacheBlend preserves generation quality while skipping recomputation, and how to turn every document in a knowledge base into a reusable cached asset. Read it below.

Avi Chawla

30,717 görüntüleme • 2 ay önce

📣 Kimi.ai's Kimi K3, an open-weight model, is now generally available and rolling out in GitHub Copilot. The model shows frontier-level abilities on agentic coding with highly cost-effective pricing. It is hosted by Fireworks. Learn more. 👇

📣 Kimi.ai's Kimi K3, an open-weight model, is now generally available and rolling out in GitHub Copilot. The model shows frontier-level abilities on agentic coding with highly cost-effective pricing. It is hosted by Fireworks. Learn more. 👇

GitHub

236,878 görüntüleme • 1 ay önce

OPENAI JUST CUT MODEL PRICES AS THE AI PRICE WAR HEATS UP 🔥 OpenAI is cutting the price of Luna by 80% and Terra by 20%. It is also rolling out a faster mode that runs at twice the speed, but costs more. The bigger picture is clear -- cheaper Chinese models and tighter corporate AI budgets are forcing the biggest AI companies to compete on price, not just performance.

OPENAI JUST CUT MODEL PRICES AS THE AI PRICE WAR HEATS UP 🔥 OpenAI is cutting the price of Luna by 80% and Terra by 20%. It is also rolling out a faster mode that runs at twice the speed, but costs more. The bigger picture is clear -- cheaper Chinese models and tighter corporate AI budgets are forcing the biggest AI companies to compete on price, not just performance.

CryptosRus

12,382 görüntüleme • 1 ay önce

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

102,747 görüntüleme • 5 ay önce

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 görüntüleme • 1 yıl önce

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 görüntüleme • 7 ay önce

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 görüntüleme • 1 yıl önce