DeepSeek AI just open-sourced WILD HIDDEN gem tech—this could change everything!
imagine an AI That's faster smarter and cheaper to run last week we followed deep seeks open source week event and let me tell you they were dropping some seriously impressive open- Source projects every single day I scrolled through some X posts about it but here's the thing a lot of the posts about it are filled with numbers and deep Tech jargon making it hard to see the big picture unless you're an AI researcher so I made a little video to unpack it all for you I'm breaking down each of deep seeks announcements in a way that's easy to digest no confusing jargon just straight forward simple explanations I want you to see why these updates are actually worth caring about like a friend chatting with you over coffee welcome back to AI handbook your go-to source for the latest AI breakthroughs to stay ahead of the curve make sure to subscribe and turn on notifications so you don't miss out so let's kick things off with day zero this was deep seek just getting warmed up like a little teaser before the main event think of it as them rolling out the red carpet setting the stage for the really big releases to come we're a tiny Team Deep seek AI exploring AGI starting next week we'll be open- sourcing five repos sharing our small but sincere progress with full transparency deep seeks Vibe is super refreshing isn't it they're keeping it real with that humble tone I mean words like tiny team and small but sincere progress make them feel approachable like they're just a group of curious folks tinkering in a garage it's a nice change from the usual Tech Bravada we sometimes see but at that opening line they're not that much shy about their big dream chasing artificial general intelligence it's like they're throwing down the gauntlet saying hey we're in this to win it it sure looks like they're going to be the Front Runners in the AGI race and I'm here for it then there's that promise of five open- Source repositories all built with full transparency that's a bold move it's like they're inviting us all backstage to see how the magic happens they're setting the stage for something big and it's got me curious sounds like they're ready to share some seriously cool stuff with the world all right let's keep going these humble building blocks in our online service have been documented deployed and battle tested Ed in production as part of the open- source Community we believe that every line shared becomes Collective momentum that accelerates the journey exactly this sentence is like the heartbeat of deep seeks whole open- Source week Vibe they're not just tossing out some cool AI tools and calling it a day they're playing a bigger game trying to bring the whole AI Community together like a giant brainstorm session instead of hiding away in some secret lab they're betting that that AGI and maybe even ACI down the road will show up faster if everyone's pitching in it's like they're saying hey let's all share our toys and build something amazing together that idea of collective momentum is so spoton if they're right this open- Source approach could turbocharge AI progress in ways we can't even imagine yet let's see what day one offered day one was all about Flash MLA and I'm pumped to break it down for you honored to share flash MLA our efficient MLA decoding kernel for Hopper gpus optimized for variable length sequences and now in production so flash MLA stands for Matrix language algorithms and it's built to work with nvidia's shiny new Hopper gpus think of those as the latest greatest engines for powering AI in plain English flash MLA is like a turbocharger for AI models it's designed to handle text or data that keeps changing lengths like when you're chatting with a bot or translating a sentence that could be short or super long it keeps everything running fast and smooth no hiccups allowed bf16 support now let's talk about bf16 or brain floating 16 it's a clever way to do math in AI imagine your baking cookies you don't need to measure every sprinkle exactly right bf16 lets the AI crunch numbers quickly without wasting space so it can process more stuff while still being efficient less memory more speed win-win paged KV cach block size 64 then there's the KV cache this is the real hero KV stands for key value and it's like the r is short-term memory picture this you're texting with a chatbot without a KV cache every time you send a message the AI would have to reread the whole conversation from the start o so slow but with a KV cache it's like oh yeah I remember what you just said and Bam it replies in a Flash the page depart means it organizes that memory into neat little chunks and the block size of 64 that's how much data it grabs at once 64 units per go it's like grabbing a handful of chips instead of picking them up one by one way smarter and faster 3,000 GB per second memory bound and 580 terlop compute bound on h800 all right let's dig into these big numbers like we're unwrapping a fun puzzle together promise it'll be easy to follow first up 3,000 GB per second picture this GBS means gigabytes per second so 3,000 GB per second is like flash MLA moving 3 terabytes of data every single second inside the gpus memory that's wild right next 580 flops okay flop sounds fancy it's short for Terror floating Point operations per second but it's just a way to measure how fast the GPU can do math at 580 flops the h800 GPU is blasting through 580 trillion calculations every second think of it like a super smart calculator on steroids solving millions of tiny problems faster than you can blink that's the muscle behind re's heavy lifting all right here's the bottom line nice and simple flash MLA is like a speed demon for AI it's all about cranking up efficiency making the most of memory and raw computing power on those beefy h800 gpus the result Lightning Fast AI processing with barely any lag and perfect for stuff like real-time chat Bots or instant translations where you can't afford to wait around it's a slick move by Deep seek to kick things off and that's just day one can you believe it they're already bringing the heat ready to see what's next let's keep rolling all right day two star is DPP and it's all about making AI teamwork a breeze with something called expert parallel EP excited to introduce DPP the First open- Source EP communication library for Mo model training and inference EP is a clever way to train mixture of experts m models those are AI setups where you've got a bunch of expert minimodels each great at its own thing imagine a team of Specialists one's a math Wiz another's a language Guru and so on instead of one person or GPU trying to handle every task solo which takes forever EP splits the work across multiple gpus each expert gets their own piece to chew on and they all work at the same time in parallel boom job done way faster efficient and optimized all-to-all communication both internode and internode support with Envy link and RDMA here's the scoop nvlink is like a high-speed express lane for gpus hanging out in the same machine it lets them swap info Crazy Fast think of it as a group of friends passing notes in class without the teacher slowing them down no bottlenecks just sippy communication so all those expert VII pieces can work together seamlessly then there's dma or remote direct memory access this one's the Champ for when your gpus are on different machines like across a room or even a data center RDMA lets them send data straight to each other without bugging the CPU to play middleman it's like texting your buddy directly instead of asking someone else to relay the message way quicker and less hassle this cuts out the lag and makes massive AI training feel like a breeze together NV link and RDMA are like the Dream Team for DPP keeping the chatter fast whether the gpus are neighbors or miles apart deep seeks basically handing out a turbo boost for big scale AI projects and it's all open source pretty awesome huh High throughput kernels for training and inference pre-filling High throughput means DPP can handle large amounts of data efficiently low latency kernels for inference decoding low latency ensures AI models generate responsive quickly making realtime applications like chatbots much more responsive native fp8 dispatch support flexible GPU resource control for computation communication overlapping so what's the big deal here's the Scoop faster AI training models learn quicker because the gpus are swapping info at lightning speed that cuts down the time it takes to get them ready like going from weeks to days sweet right snappier AI responses for real-time stuff like chat Bots this means they're on the ball replying to you without that awkward pause it's like they've had a shot of espresso saves cash and energy by using tricks like fp8 and Slick communication EnV link RDMA it needs less computing muscle that's less power lower costs and suddenly AI feels more doable for smaller teams not just the big shots think of it like swapping out old crackly walk talkies for super fast Wi-Fi everything just flows better deep seeks basically saying here's a tool to make AI quicker cheaper and sharper go have fun with it and since it's open source it's up for grabs love how it's coming together me too let's roll on to day three and see what deep seeds got cooking next introducing deep GMM and fp8 GMM library that supports both dense and mams powering V3 slr1 training in inference so deep GMM is all about supercharging matrix multiplication the heavy lifting behind how AI models crunch numbers it's a slick Library built to handle FPA General Matrix multiplications GMM which is a fancy way of saying it does super fast math with 8bit Precision why is that cool it means AA can process huge piles of data quicker using less memory and still keep things accurate enough to trust up to 1350 Plus fp8 teraflops on Hopper gpus this thing's a beast it's hitting over 1,350 trillion calculations per second using that 8bit fp8 magic on nvidia's Hopper gpus that's like a math wizard solving problems faster than you can say wow no heavy dependency as clean as a tutorial fully just in time compiled deep GMM doesn't mess around with premade plans it builds its math tricks on the fly with just in time jit compilation think of it like a chef whipping up a fresh dish right when you order no stale leftovers here core logic at 300 lines yet outperforms expert tuned kernels across most Matrix sizes it's just 300 lines of code super simple but it beats out those fancy hand tuned setups Pros spend ages perfecting it's like a tiny car outracing a tricked out Supercar across all kinds of tracks supports dense layout and Tumo layouts it's flexible handling regular dense Matrix setups plus two special layouts for mixture of experts Moi models that's like having a tool that works for both big group projects and small specialized teams super versatile all right let's wrap up day three with a quick and juicy bottom line deep GMM makes AI calculations faster more efficient and easier to use it's like giving AI models a supercharged calculator that works at mind-blowing speeds while using less memory deep seeks basically saying here's a fast efficient boost for your AI enjoy and that's day three in the bag ready for more let's jump into day four and see what's next on the menu deep seek optimizes AI training with smart parallelism and introduces three powerful tools to make AI training faster and more efficient let's break them down dual pipe a bidirectional pipeline parallelism algorithm for computation communication overlap in V3 slr1 training dual pipe is all about making AI training smarter and faster by multitasking like a pro picture this your cooking dinner normally you chop all your veggies first then start cooking step by step right but what if you could chop while the stove's already going that's the magic of duel pipe it lets AI process data like doing the math and transfer data moving it between gpus or machines at the same time no waiting around it's like having two hands working in perfect syn this combo seriously speeds things up instead of one task holding up the other dual pipe keeps everything flowing cutting down the time it takes to train those big AI models EB an expert parallel load balancer for V3 slr1 so eppb stands for expert parallel load balancer and it's a genius little tool for mixture of experts Moi models you know those AI setups where different experts handle specific tasks here's the catch sometimes one expert gets slammed with work while others are just chilling twiddling their thumbs that's like a traffic jam in your AI inefficient and slow eppb swoops in like a superhero manager it balances the workload across all the gpus making sure no one's overloaded and no one's slacking the result no bottlenecks no slowdowns just faster more efficient AI training this pairs perfectly with deep seeks other tools like DPP and dual pipe keeping those Hopper gpus firing on all cylinders and of course its open source deep seeks sharing the love again analyze computation communication overlap in V3 slr1 profile data is like a performance coach for AI it's a tracking tool that helps developers figure out how their AI training is doing and spot any trouble spots imagine you're wearing a fitness tracker it tells you when you're slowing down or where you need to push hard profile data does that for AI it digs into the nitty-gritty showing exactly where things are lagging or getting stuck maybe one part of the model's taking too long or a GPU is not pulling its weight it finds those bottlenecks so developers can tweak and speed things up bottom line deep seek is making AI training faster and more efficient by using dual pipe letting AI compute and transfer data at the same time by using eppb balance ing workloads so no GPU is overloaded and with profile data helping developers find and fix slowdowns together it's like deep seek took AI training from a bumpy single Lane Road and turned it into a multi-lane highway everything flows better quicker and with less hassle all open source of course onto day five 3fs Thruster for all deep seek data access Firefly file Sy system 3fs a parallel file system that utilizes the full bandwidth of modern ssds and RDMA networks AI models need to read and write massive amounts of data at lightning speed deep seeks 3fs Firefly file system is a superhighway for AI data making it way faster than traditional storage systems let's break it down 6.6 tib /s agregate red throughput in a 180 node cluster picture this 180 machines teamed up reading 6.6 terabytes per second that's about 7.26 terabytes in everyday terms it's like downloading an entire streaming services worth of shows in a blink this is the total speed across the cluster showing off how three FS flexes modern ssds and RDMA networks to keep data flying for AI training 3.66 ti/ Min throughput on Grays sort Benchmark in a 25 node cluster gray sort is a real world test for Speed gray sort measures how fast a system can sort massive amounts of data usually in terabytes why does this matter the faster a system sorts data the quicker AI models can learn it's not just theoretical gray sort shows how well a system actually performs with real world massive data sets 40 plus Gibb slsp throughput per client node for cavh lookup here's where it gets personal each client node one of the 500 plus machines hitting the system can pull over 40 gibbes per second for KV cache lookups that's key value caching for AI inference like a chatbot remembering our convo without rethinking every word per node that's blazing fast around 43 GB per second disaggregated architecture with strong consistency semantics this is the brains of the operation disaggregated mean storage and Compu are split up so they can scale independently like keeping the kitchen and dining room separate but perfectly in sync strong consistency ensures every node sees the same fresh data no mixups like everyone getting the latest menu at once training data pre-processing data set loading checkpoint saving SL reloading embedding Vector search and cavey cach lookups for inference in V3 slr1 this is 3fs showing off its versatility it's the backbone for deep seeks V3 and R1 models handling everything from prepping data loading data sets saving progress searching vectors to caching for quick inference it's like a Swiss army knife for AI workf flows all right let's wrap up this 3fs magic from Deep seek with a quick friendly bottom line the Fire Flyer file system 3fs is like slapping an ultra fast high-speed storage system onto AI ey letting it gobble up massive data piles in a snap it's like swapping out a clunky old hard drive for a supercharged SSD but scaled up for AR's Wildest Dreams deep seeks basically handed the community a turbo boost for big projects all open source thank you for watching that's a wrap on deep seeks open source week I hope this breakdown made things clear and easy to understand if you found this useful don't forget to follow share this video and drop a comment let me know what you think till next time stay curious
2025-03-10 19:57
