Placeholder Image

Subtitles section Play video

  • - We have looked at a lot of ballin' GPU over the years,

  • whether it's the six titan views we had

  • for the six editor's project,

  • three GV100 Quadras for 12K Ultrawide gaming,

  • or even this unreleased mining GPU,

  • the CMP 170 HX.

  • There are not a lot of cards out there

  • that we have not been able to get our hands on

  • in one way or another,

  • except for one.

  • Until now,

  • the Nvidia A100,

  • this is their absolute top dog,

  • AI enterprise high performance compute,

  • big data analytics monster,

  • and they refused to send it to me.

  • Well, I got one anyway Nvidia,

  • so, deal with it.

  • Just like everyone's gotta deal with my segues,

  • smart deploy provides out of the box windows imaging support

  • for over 1,500 computer models.

  • You can deploy one windows image to any hardware model

  • with ease and you can get free licenses worth over $500

  • at smartdeploy.com/linus.

  • (upbeat music)

  • The first two questions on your mind,

  • are probably why we weren't able to get one of these.

  • and what ultimately changed

  • that resulted in me holding one in my hands right now.

  • The answer to the first one

  • is that Nvidia just plain

  • doesn't seed these things to reviewers

  • and at a cost of about $10,000.

  • It's not the sort of thing that I would just,

  • you know, buy.

  • 'Cause I got that swagger.

  • You know what I'm saying?

  • As for how we got one,

  • I can't tell you.

  • And in fact,

  • we even blacked out the serial number

  • to prevent the fan who reached out offering to get us one,

  • from getting identified.

  • This individual agreed to let us

  • do anything we want with it.

  • So you can bet your butt,

  • we're gonna be taking it apart.

  • And all we had to offer in return

  • was that we would test Ethereum mining on it,

  • send a shroud, that'll allow 'em to actually cool the thing

  • and reassemble it before we return it.

  • So let's compare it really quickly to the CMP 170 HX,

  • which it is the most similar card that we have.

  • It's the silver metal

  • and it's not ripped for my pleasure.

  • Regrettable. - [Jake] Alright.

  • - And we actually have one other point of comparison.

  • This isn't a perfect one.

  • This is an RTX 3090.

  • And what would've been maybe more apt

  • is the Quadro or rather they dropped the Quadro banding.

  • But the A6000.

  • Unfortunately that's another really expensive card

  • that I don't have a legitimate reason to buy

  • and Nvidia wouldn't send one of those

  • for the comparison either.

  • So the specs on this are pretty similar.

  • We're gonna use it as a standin'

  • since we're not really looking

  • at any workstation loads anyway.

  • So the A100.

  • This is a 40 gigabyte card.

  • I'm gonna let at that sink in for a second.

  • And the craziest part,

  • is that 40 gigs is not even enough

  • for the kinds of workloads

  • that these cards are used to crunch through.

  • We're talking enormous data sets

  • to the point where this 40 gig model,

  • is actually obsolete now,

  • replaced by an 80 gig model.

  • And these NV Link bridge,

  • connectors on the top here,

  • let's go ahead and pull these off.

  • These, there we go,

  • are used to link up multiples of these cards

  • so they can all pull memory

  • and work on even larger data sets.

  • Now the diet, the center of it,

  • is a seven nanometer TSMC manufactured GPU

  • called the GA 100.

  • We're gonna pop this shroud off.

  • We're gonna take a look at it.

  • It has a base clock of just 765 megahertz,

  • but it'll boost up to fourteen ten.

  • That memory runs

  • at a whopping one and a half terabytes a second

  • of bandwidth on a massive

  • 5,120 bit bus.

  • It's got 6,912 CUDA cores

  • and, what is it?

  • 250 watt TDP.

  • Woooh.

  • She's packing.

  • - [Jake] Oh, you're just going right for it.

  • - I'm going right for.

  • - [Jake] Oh geez.

  • - This is Linus tech tips.

  • - [Jake] And basically every part of this

  • is identical to the CMP card.

  • - It kinda looks that way.

  • I mean the color's obviously different.

  • - Yeah, but it looks like the clamshell

  • is two pieces in the same manner.

  • There's no display outputs.

  • The fins look the same.

  • - Now here's something the CMP card specifically

  • didn't even contain the hardware for video in code,

  • if I recall correctly.

  • - Yeah, this doesn't have anything.

  • - Okay, so it's not that it was fused off.

  • It's just plain not on the chip.

  • - Not on GA 100, yeah.

  • - Okay but,

  • - GA102, which is like 3090.

  • - Yes.

  • - Does have it. - Ooh.

  • - And A6000.

  • - Okay, you ready?

  • - Oh God!

  • So yeah. - Hey.

  • - It's like exactly the same on the inside.

  • Same junk power connector.

  • - Wow.

  • That is super junk,

  • check this out guys.

  • It uses a single eight pin EPS power connector,

  • which you might think is a PCIE power connector.

  • So here, look, I'll show you guys.

  • This is an eight pin,

  • like normal GPU connector,

  • but watch, cannot go in.

  • But if we take the connector

  • out of our CPU socket on the motherboard,

  • There you go although,

  • the clips are interfering a little bit.

  • I mean, what the heck is going on here,

  • ladies and gentlemen?

  • - You need more power.

  • - Yeah exactly.

  • - So you can combine two PCIE connectors into that.

  • - [Andy] Can't remember how to get it outta here.

  • I see the fingerprint of the technician

  • who assembled the card though.

  • - I think we have to unclip this part first.

  • Oh, there's a little screw, right?

  • - There's a little screw.

  • - Haha, third type of screws.

  • - [Andy] Yourself. - Didn't see that one, nerd.

  • - [Andy] You're a nerd.

  • - [Jake] Your face is a nerd.

  • - [Andy] Your but nerd.

  • - [Jake] Whoa.

  • - It's not coming off, Jake.

  • - What? You gotta like tilt it out, buddy.

  • Whoa, whoa, whoa.

  • Don't pull the cooler off.

  • - See?

  • It's like it's caught back here.

  • - Hey ho.

  • Hey, how you doing?

  • - Jesus.

  • - Stressful.

  • Look, maybe if we break it,

  • you'll actually have to buy one.

  • - I don't wanna buy one.

  • That's not the goal. - What?

  • - I thought you put your hand up for a high five.

  • I was like, "well, what are you talking about?

  • I don't want to buy one."

  • - Why not?

  • Whoa, what is going on here?

  • You see that?

  • - It looks like there was a thermal pad there or something,

  • but there isn't, its like greasy.

  • - It actually,

  • no, look at it closer.

  • It's not greasy.

  • It's, you see how this is brushed almost.

  • Or looks somebody sandblasted it.

  • - That part's not.

  • I don't remember that on this card.

  • - Alright, so the spring loading mechanism

  • is just from the bend of the back plate, that's kinda cool.

  • - [Jake] So I checked the CMP thing.

  • Doesn't look like it.

  • - [Andy] I wonder why they wouldn't have like a map.

  • - [Jake] This doesn't look brushed at all.

  • What did we, last time we twisted?

  • - [Andy] No, I don't think we did.

  • - Yeah we did.

  • - [Andy] I'm pretty sure I just rimmed on it.

  • - [Jake] Oh God! No.

  • You were against rimming on it.

  • And then we were like, just twist a little.

  • - [Jake] Oh.

  • God.

  • Ah.

  • It has an IHS.

  • It looks basically the same.

  • - [Andy] Yeah.

  • - [Jake] We're gonna have to clean that off and see

  • there's not much alcohol.

  • - [Jake] No, I like to go in dry first.

  • So yep, that's the same thing, alright.

  • I mean, this isn't the first time Nvidia

  • has used the same Silicon in two different products

  • with two different capabilities.

  • We see the same thing

  • with their Quadro lineup versus their GForce lineup

  • where things will just be disabled

  • through drivers or fusing off different functional units

  • on the chip.

  • What I wanna know then

  • is besides the lack of NV Link connectors on this one.

  • - Well, they are in there.

  • They're just not accessible and they probably don't work.

  • - Right.

  • What is the actual difference

  • in function between them?

  • (Jake sighs)

  • - Well, this one doesn't have full PCIE 16X,

  • - Right? - It does less memory.

  • I think it has way less transistors,

  • but it is still a GA100.

  • - Yeah, so the transistors are there.

  • - Yeah, they're probably just not functional.

  • Let me see what the chip number is on that one.

  • - Yeah, 'cause were we not even able

  • to find a proper Nvidia.com reference to this one anyway.

  • So we're just relying on someone else's spec sheet.

  • So the transistor count could just be wrong.

  • - Okay, so this is so the CMP card was a GA.

  • - Look at this guy?

  • - Yeah.

  • What a weirdo.

  • GA 100-105F.

  • And this is a GA100-833.

  • - If it's a GA100,

  • I guess it could be a different GA100.

  • I don't know.

  • - I mean, it used to be back in the day,

  • you would assume that it's just using the same Silicon

  • as the GForce cards because Nvidia's data center business

  • hadn't gotten that big yet,

  • but nowadays, they can totally justify,

  • an individual, like new guide design

  • for a particular lineup

  • of enterprise product.

  • - And interestingly enough,

  • the SXM version doesn't have an IHS

  • at least it seems that way.

  • But the SXM version is also like 400 Watts.

  • And this is like 250.

  • - [Andy] Yeah, totally different classes

  • of capabilities, alright?

  • Let's put it back together then, shall we?

  • - I got your new goop. - Goop me.

  • - I brought two goops.

  • - We're going for the no look catch.

  • - Oh yeah baby. - Yes.

  • X marks the spot, baby.

  • My finest work.

  • - Maybe it'll perform better now.

  • - [Andy] Probably not.

  • (Jake laughs)

  • (Andy truck signals)

  • We're backing it up.

  • (Jake chuckles)

  • - [Jake] Cool story, bro.

  • - [Andy] Thanks.

  • Thanks bro.

  • - Where's our back plate.

  • Did you take it?

  • Oh shoot.

  • - Yes. - Black.

  • I thought it was gold.

  • I was looking for gold.

  • (Jake laughs)

  • - [Jake] Aren't we all. - I don't know about you,

  • but I found my gold.

  • - What's that?

  • - Yvonne.

  • - Shut up (chuckles)

  • - Alright.

  • Alright.

  • Let's get going here.

  • Which one do you wanna put on the bench first?

  • - What do you mean?

  • We're not gonna compare to that thing.

  • It doesn't do anything.

  • We don't need this thing.

  • - But here we go, boys.

  • - We can't put this in the first lock.

  • 'Cause we don't have a display output.

  • - You like the bottom one? - Yeah,

  • - You're a bottom?

  • - Sure.

  • - This, okay.

  • This is how you flex IT style.

  • Now you might have noticed

  • at some point that the A100

  • doesn't have any sort of cooling fan.

  • It's just one big fat, long heat sink

  • with a giant vapor chamber under it to spread the heat

  • from that massive GPU.

  • So Jake actually designed

  • what we call the shroud donator.

  • It allows us to take these two screws

  • that are on the back of the cart

  • for securing it in a server chassis,

  • because that's how it's designed to be used.

  • So it's passive,

  • but there's lots of airflow going through the chassis,

  • and then lets us take those screw holes,

  • and mount a fan to the back of the cart.

  • It's frankly not amazing.

  • (Jake chuckles)

  • - What? No.

  • That is aerodynamics at its peak.

  • You should hire me to work on F1 cars, okay?

  • - Yeah.

  • Not so much.

  • - Yeah.

  • It only blows probably more air out this end

  • from the back pressure than it does on this end. (laughs)

  • But it's enough to cool it, I swear.

  • - It is. - Yeah.

  • - Let's go ahead and turn on the computer, shall we?

  • - Oh yeah, so a couple interesting points here.

  • It wouldn't boot right off the bat.

  • You have to enable Above 4G decoding.

  • And then I also had to go in and I think it's called like

  • 4G MMIO or something like that.

  • I had to set that to 42.

  • - Okay.

  • - The answer to the universe.

  • - Yes.

  • Thank you.

  • And they are both here.

  • A100 PCIE 40 fricking gigabytes.

  • - I installed the like game ready driver for the 3090,

  • and then I installed the data center driver,

  • and I think it overwrote it,

  • but the game ready driver,

  • it still showed as like active

  • and you could do stuff with the A100 and vice versa.

  • So it's probably fine.

  • - Now, interestingly,

  • the A100 doesn't show up in task manager at all.

  • - [Jake] Did the CMP, I can't,

  • - [Andy] remember. - No, no.

  • I don't think it did actually, anyways.

  • - What do you wanna do in Blender,

  • classroom?

  • BMW?

  • BMW's probably too short.

  • - Yeah.

  • Let's do classroom.

  • I think BMW on a 3090 is like 15 seconds

  • or something like that anyway so.

  • - That's also like the spiciest 3090.

  • - [Jake] That you can get. Yeah, pretty much.

  • It's just so thick.

  • Why would you ever use it?

  • - Because you wanted,

  • - Is it even doing anything like (chuckles)

  • - Here's one reason,

  • 'cause you can do classroom renders

  • in a minute and 18 seconds, that's why?

  • - Okay.

  • Well, what about the A100?

  • You didn't plug the fan in, hey.

  • - Oh whoops.

  • How hot is this?

  • - Probably warm.

  • - Fortunately it hasn't been doing anything.

  • Time to beat is a minute and 18 seconds.

  • So let's go ahead and see how it does.

  • - It feels like this is the intake.

  • I mean it's hot.

  • So like, - Oh yeah.

  • But it's going.

  • It's going Jake.

  • It's going.

  • You did good.

  • - It works enough.

  • This should be like, this is all.

  • - This should be way faster. - Way huge GPU, right?

  • - [Andy] It's actually slower.

  • - [Jake] How much?

  • Not by much.

  • - It's like a few seconds, but it's slower.

  • - So it's worse in CUDA.

  • What about Optixs?

  • So the interesting thing

  • is this card doesn't have Ray Tracing cores.

  • The 3090 does,

  • see you'd think that Optixs

  • would only work on the 3090, right?

  • - Do you want me to just try the A100?

  • - Yeah, sure.

  • It's still GPU compute.

  • - I mean you gotta give it to it in terms of efficiency.

  • For real though, even running two renders to the 3090's one,

  • the average power consumption here is still lower.

  • - [Jake] Yeah well, and looking at while it's running,

  • it's like 150 Watts.

  • - Yeah.

  • - [Jake] Versus 350 or whatever it was on the 1390.

  • - Yeah, ready to go again?

  • - [Jake] Yep.

  • - Okay. - [Jake] Oh my God.

  • - Man, this thing is fast.

  • - What's the power consumption?

  • - [Andy] Holy bananas.

  • - [Jake] 353.

  • Still like just,

  • I want one of these.

  • This thing is sick.

  • (Jake laughs)

  • It's way faster.

  • - Yeah.

  • There's no question.

  • We don't even need to.

  • - It's gonna be like thirty seconds.

  • - Yeah.

  • Not even close.

  • - So do you wanna know why?

  • - I would love to know why.

  • - You said it earlier.

  • You just weren't really thinking about it.

  • This has half the CUDA course of a 3090,

  • it's likes seven thousandish I think.

  • - Right, so it's just full of like machine learning stuff.

  • - Yeah, so it has basically half the CUDA cores.

  • So the fact that it is even close

  • is kind of crazy in CUDA mode.

  • But in Optix, what I found out

  • is Optixs will use the Tensor cores

  • for like AI Denoising,

  • - [Andy] But nothing else.

  • - Which you'll see in there.

  • So I think it's falling back to CUDA for the other stuff.

  • - [Andy] Got it.

  • - But the 3090 has Ray Tracing and Tensor cores so.

  • - Right.

  • - It just demolishes (chuckles)

  • - Where's the thing where you can select apps

  • and then tell it which GPU to use.

  • Yeah, here we go.

  • No, so it'll not allow you to select the A100 to run games,

  • even if we could pipe it through our onboard,

  • or through a different graphics card like we did with that.

  • - [Jake] It doesn't have DirectX Ray

  • - Mining card ages ago.

  • No DirectX support whatsoever.

  • - [Jake] Let's check it in GPU-Z.

  • - So way fewer CUDA cores.

  • You can see that

  • we go from over 10,000,

  • to a lot less than 10,000.

  • Pixel fillrates are actually higher.

  • I guess that's your HBM2 memory talking.

  • - [Jake] One point five Gigabytes per second.

  • - What's a 39,

  • One point five terabytes per second.

  • It's like

  • - [Jake] 50% or more

  • - 60% almost.

  • - Holy banana.

  • - But what about the supported tech?

  • Yeah, so we can do CUDA, OpenCL,

  • - [Jake] PhysX (laughing)

  • - Sure.

  • - [Jake] We should set it as the PhysX card.

  • - Dedicated PhysX card.

  • All the rag dolls everywhere.

  • - [Jake] And OpenGL but not Direct anything or Vulkan even.

  • - OpenGL.

  • Now that's interesting.

  • - [Jake] Go to the advanced tab.

  • 'Cause you can select

  • like a specific DirectX version

  • at the top under General.

  • Like well, the DX 12.

  • What does it say?

  • Device not found.

  • It's the same as the mining card.

  • It'll do OpenCL.

  • So we can't mine on it (chuckles)

  • - Alright. I mean, should we try that?

  • - [Jake] Yeah, we could do mining or folding or.

  • - Sure, I have a feeling that's gonna kind of suck

  • for that too.

  • - There's not. - Like AI in mining.

  • - I don't think so.

  • It's still a big GPU dude.

  • - So you can't.

  • - Well suck is relative, right?

  • Like for the price you'd never buy.

  • - I think it might be better than the CMP card though.

  • Just a little bit. - Shut up.

  • - I think so.

  • So the only thing you can adjust,

  • I think this is the same with the CMP card

  • is the core clock and the power limit.

  • You can't mess with the memory speed.

  • - [Andy] And you can move the power limit only down

  • it looks like.

  • - [Jake] Yeah.

  • Top is the 3090,

  • bottom is the A100.

  • - [Andy] Wow.

  • That is a crap tone faster than a 3090.

  • - [Jake] It's pretty much the same as the CMP,

  • but look at the efficiency.

  • - 714 kilo hash per watt.

  • - [Jake] And I bet you if we lower the power limit

  • to like 80,

  • it's a little bit lower speed.

  • Maybe we can go, I don't know.

  • We probably don't have to tinker with this too much.

  • I mean, it doesn't draw that much power to begin with,

  • I guess. - Yeah.

  • I think it's pretty fricking efficient

  • right outta the box.

  • - I mean the efficiency is better.

  • It's a little bit better,

  • but before it was doing 175 mega hash

  • roughly at 250 Watts,

  • so it's pretty pretty good.

  • 3090, you can probably do like 300 Watts

  • with 120 mega hash.

  • We're running the folding client now.

  • I've had it running for a few minutes,

  • and it's kind of hard to say.

  • The thing with folding is,

  • based on whatever project you're running,

  • which is whatever job the server has sent you to process,

  • your points per day will be higher or lower.

  • So it's possible that the A100 got a job

  • that rewards less points than the 3090 did.

  • It does look like it's a bit higher,

  • but you can see our 39.

  • This is like a little,

  • like comparison app thing

  • is 31% lower than the average.

  • So it's probably just that this job

  • doesn't give you that many points.

  • - Got it.

  • - The interesting part is

  • the 3090's drawing.

  • 400 watt.

  • - [Both] 400.

  • - Holy shnikes.

  • - [Jake] A100 is drawing.

  • - 240.

  • (Jake laughing)

  • Man, that's efficient

  • and performance per what?

  • Maybe gamers don't care that much.

  • Actually we know for a fact,

  • gamers don't care that much.

  • In the data center, that's everything,

  • because the cost of the card,

  • is trivial compared to the cost of power delivery,

  • and cooling on a data center scale.

  • - Especially when you have eight of these

  • with a 400 watt power budget,

  • like you would get on the SXM cards in a single chassis,

  • times 50 chassis,

  • like that's a lot of power (chuckles)

  • - Let's try something, machine learning.

  • - Unfortunately for obvious reasons,

  • most machine learning or deep learning,

  • whatever you want to call it, benchmarks,

  • don't run on windows.

  • So instead I've switched over to Ubuntu

  • and we've set up the CUDA Toolkit,

  • which is gonna include our GPU drivers

  • that we need to even run the thing

  • as well as Docker and the Nvidia Docker Container,

  • which will allow us to run the benchmark.

  • We're gonna be running the ResNet-50 benchmark,

  • which runs within TensorFlow two.

  • This is a really, really common benchmark

  • for big data, clusters and stuff.

  • Except our cluster, is just one GPU.

  • In a separate window, I've got Nvidia SMI running.

  • It's kind of like the Linux version of MSI Afterburner,

  • but it's made by Nvidia, so not quite,

  • but what it's good for,

  • is at least telling us our power and the memory usage,

  • which we should see spike a lot

  • when we run this benchmark,

  • I took the liberty of pre-creating a command

  • to run the benchmark.

  • So we're gonna be running with XLA on

  • to hopefully bump the numbers a bit.

  • We will do that for the A100 as well.

  • So no worries there.

  • It should be the same

  • as well as using, what do you want?

  • Look, he left cause he didn't have time for this.

  • And now he's back.

  • This is the world's most expensive lint roller.

  • (Andy chuckles)

  • I even don't remember what I was saying, damn it.

  • Distractions aside, we're gonna be running with XLA on.

  • That'll probably give us a bit higher number

  • than you would normally,

  • but it is still accurate

  • and we're gonna be running the same settings

  • on the A100 as well.

  • So no concerns there.

  • We'll also be using a batch size of 512

  • as well as fp16 rather than fp32.

  • So if you wanna re-create these tests yourself,

  • you totally can.

  • Let's see what our 3090 can do.

  • Look at that 24 gigs of VRAM completely used.

  • God, I don't know if there's any application

  • aside from like Premier that will use all that VRAM.

  • I'm sure Andy can attest to that (strained laugh)

  • Okay, 1,400 images a second.

  • That's pretty respectable.

  • I think like a V100,

  • which is the predecessor to the A100

  • does like less than 1000.

  • So the fact that a 3090,

  • which is a consumer gaming card

  • can pull off those kind of numbers is huge.

  • Mind you, the wattage, 412 Watts.

  • That's a lot of power.

  • It'll be interesting to see how much more efficient

  • the A100 is when we try that after.

  • The test is done now,

  • and the average total images per second

  • is 1,400 and 35.

  • It's pretty good.

  • I've gone ahead and added our A100

  • so we can run the benchmarks on that instead.

  • And I'm expecting,

  • this is gonna be substantially more performant.

  • So it's the same test.

  • I'm just gonna run the command here.

  • Gonna wait a few seconds.

  • We got Nvidia SMI up again.

  • You can see that it's just running on the A100.

  • The RAM on the 3090 is not getting filled.

  • We're just using that as a display output.

  • See, all 40 gigabytes used.

  • That's crazy.

  • (Jack laughing)

  • If we thought the 3090 was fast.

  • Look at that Andy.

  • That's like a full 1000 images more,

  • we're getting like 2400

  • instead of 1400

  • and the icing on the cake.

  • If you look at Nvidia SMI,

  • we're using like 250 Watts

  • instead 400,

  • while getting like almost double the performance.

  • That is nuts.

  • - Probably the coolest thing

  • about this whole experience though,

  • is seeing the Ampere architecture

  • on a seven nanometer manufacturing process.

  • 'cause you gotta remember

  • while none of this is applicable to our daily business.

  • What this card does do,

  • is excite me for the next generation of Nvidia GPUs.

  • Because even though the word on the street

  • is that the upcoming Ada Lovelace architecture,

  • is not going to be that different from Ampere.

  • Consider this, Nvidia's gaming lineup

  • is built on Samsung's eight nanometer node,

  • while the A100 is built on TSMC's seven nanometer node.

  • Now we've talked a fair bit about how nanometers,

  • from one fab to another,

  • can't really be directly compared in that way.

  • But what we can do, is say that it is rumored,

  • that Nvidia will be building

  • the newer ADA Lovelace gaming GPUs

  • on TSMC's five nanometer node,

  • which should perform even better

  • than their seven nanometer node.

  • And if the efficiency of improvements

  • are anything like what we're seeing here,

  • we are expecting those cards

  • to be absolute freaking monsters.

  • So good luck buying one.

  • (Jake laughing)

  • Hey, at least you can buy one of these.

  • We've got new pillows, that's right.

  • This is the, what are we calling it?

  • - [Jake] Couch ripper.

  • - The couch ripper the couch rip.

  • It's an AMD themed version

  • of our CPU pillow with alpaca and regular filling blend.

  • And this video is brought to you by our sponsor,

  • ID agent.

  • 90% of data breaches start with a phishing email.

  • So you can reduce your organization's chance

  • of experiencing a cybersecurity disaster

  • by up to 70% with security awareness training.

  • That includes phishing simulation,

  • Bullphish ID by ID agent is a Phish simulation platform

  • that transforms your biggest attack surface,

  • into your biggest defensive asset.

  • You can add every employee to your security team

  • with security awareness training

  • that empowers them to spot and stop Phishing threats.

  • You can automate training campaigns

  • and reporting for stress free,

  • consistent training that gets results.

  • Choose from a rich set of

  • Plug and Play Phishing campaign kits

  • and video lessons accompanied by short quizzes,

  • or you can create your own fishing campaigns

  • and training materials easily.

  • Bullphish ID provides effective affordable one-Stop

  • phishing resistance training

  • that fits any business and budget.

  • Get two months for free and 50% off setup

  • at bullphishid@it.idagent.com/linus

  • If you guys enjoyed this video,

  • maybe go check out our previous video,

  • looking in more depth at the CMP 170 HX.

  • - [Jake] I like this silver better.

  • - If we were smart,

  • we'd be mining on this,

  • but we're not that smart.

  • - [Jake] Well, you know, mining is bad.

- We have looked at a lot of ballin' GPU over the years,

Subtitles and vocabulary

Click the word to look it up Click the word to find further inforamtion about it