RTX PRO 6000 w/ 4-bit AI Models: Quantization Breaks

Key Takeaways

Hello community. So great that you are back. Today we talk about a 4bit quantization of your LLMs and the ideas. H maybe stop your blind quantization or your agents. Today we talk about a paradox. A paradox that a 4-bit model might consume more energy, run slower, and has a lower accuracy than the full 16- bit baseline model. The authors call it a quantization trap. Now I know that you have a standard protocol. You want to run a 70B model here on your normal machine on your normal laptop and you just go here with a 4bit quantization. However, even if there was empirical success of a near lossless quantization, things changed because in today's study, the authors tell us, you know what, we found a quantization trap. We're reducing here the precision from an 16 bit to a 4 bit increases here the net energy consumption while also degrading here the reasoning accuracy significantly. The authors look at two infrastructure at an H100 GPU and of course an row 6000 Nvidia GPU because there is a difference you know here with the new 6000 we have here fifth generation tensor cores with FP4 but careful this is a knife that cuts both ways so let's have a look stop blind quantization for agents so if you are Building an agentic workflow that relies on a chain of sort. If you use a reasoning model or anything with a little bit more of [snorts] a sequential logic, blindly applying here for bit condensation will sabotage your system. Simple message. So you are likely increasing here your cloud build, your energy, your latency while also degrading the agent's ability to hold a logical thread. And they have a beautiful chapter here on the mathematics on the mathematics of failure for a 4bit quantized model. Well, they start here. Okay, they went with a threedimensional vector and they say, you know, it's nice. Normally, you just go here for the economic value of the throughput and the memory cent. But what about we include now trust in our equation or this means you the accuracy of the reasoning chain. What is this not just about speed or memory or energy? What about we ask hey does the result make sense? Is it a correct result? What about the accuracy of reasoning? And they include also environmental. Beautiful. They define here a significant parameter the casting overhead ratio and they say this is easy. Yeah. Our tow casting here is the time spent the AI mile spans here on dequantizing here the weight tensors let's say from a 4bit back to a 16 bit for the computation and then we have here towel composite and this is the time spent on the actual computation and you know this is interesting because if you spend more time here on the quantizing and the dequantizing then you just lose a lot of energy and The finding was simple. On an H100 GPU, a Mistral 7B model here, the core for an 8 bit precision reached 2.5. This is not what you want because this means that for every millisecond spent doing mathematics, the GPU wasted 2.45 milliseconds just packing and unpacking data. The authors found something to call a sequential amotization failure. Why doesn't this happen in the standard training or the highest rut serving? Well, it's about amotization because usually you load a weight once and apply them to a batch of 128 plus requests. But in a multihop reasoning, if you have complex reasoning traces, if you have reasoning large language models, the process is auto reggressive and often inherently sequential. So now the situation changes. Now the authors found that the smaller models suffer much more. Counterintuitively smaller models like a 0.6B model perform even worse. Why? Because for the smaller models the matrix multiplication here is incredibly fast. But it finishes so quickly that it hits the GPU's corner launch latency floor. However, the dequantization overhead still remain the same. So if you look at current tensor cores like an H100, they are optimized for high precision mathematics. Using them for a low bit interference introduces suddenly a translation layer of course casting that is now computational expensive time energy and it has a significant effect on the reasoning precision. Reasoning chains in general chain of sorts no are unique fragile and you also show us and there's a lot of data please have a look at the report yourself a 4% drop in a single token accuracy can compound into a 30% drop in reasoning success because early logical errors act as false premise for later steps. Imagine a 4% drop in a single token in a currency as a 30% drop in the reasoning. And therefore, recommendation one, until everyone upgrades from an H100 to at least a black, well, a 6000 Pro or whatever you have, using a 4-bit quantization or complex reasoning tasks is mathematically irrational and we should stick to 16 bit or 8bit native formats here for high fidelity agents. If you work on an H100, now situation changes. Of course, the second part is what if you have a pro 6000 because now you do have a fifth generation tensor core. Now you do have FP4. So the situation changes you from the hardware side. Now the efficiency trap is gone. Of course, of course you have the hardware. So you can use another 4bit quantization without a massive energy spike or the latency penalties as you have seen on the H100. So the hardware now finally supports here the free low bit interference. But guess what? The accuracy trap remains. And this is the interesting part that the orers show us in this new paper. Yes, you bought a new hardware. Yes, it is real fast. And yes, you still have a problem with the accuracy. If you have a chain of sort, a four-bit quantization simply breaks the system. And they tell us for the [clears throat] multihop reasoning. This means agents that solve let's say mathematical problems or coding problems or chain of sort whatever you still cannot trust the 4-bit model. And they show that the reasoning accuracy collapses regardless of how fast your hardware is. and it makes somehow sense but they proved it on a mathematical basis and they even wrote here a theorem I'm going to show you a little bit later now they say using a blackwell FP4 for a single hub task like a classification or a simple chat no on social media or a simple summarization where precise deductive chains are not strictly necessary great use it go with Blackwell 6000 Pro FP4 single hop will work fine But whenever you start to have a complex reasoning like in science, in mathematics, in finance, in medicine, you must still stick to a higher precision 8 bit and 16 bit to maintain here to trust the reasoning accuracy even if the hardware tempts you to go lower. So the industry marketing around Blackwell suggest it unlocks a 4bit AI and this is correct but there are nuances to this no because this paper now let's say corrects this narrative by the marketing department of Nvidia a little tiny bit no yes blackwell unlocks efficient 4-bit AI but it does not magically make a 4-bit EI model smart enough for complex reasoning this is not going to happen. So for your reasoning heavy research or your product or whatever, treat Blackwell as a tool that allows you to run 8bit models with the speed of previous 4-bit implementation. And do not fall into the trap of pushing down to FP4 just because the hardware supports it because yes, your agent will be fast. Yeah, your agent will be energy efficient. Yeah. and your agent will be wrong. So therefore, even with the mighty Blackwell or whatever comes next, we still face a fundamental precision problem in quantized EI models. And the paper proves that reasoning accuracy is invariant to the hardware speed itself. Now let's have a look at the research results. Here they show you here an H100 and here you have in let's say blue FP16 the accuracy [clears throat] the reasoning accuracy beautiful then you have let's say in pink 8 bit okay it's a little bit less but then if you go to four bit on an H100 you really have here what they call a reasoning collapse look you really break down here in your reasoning accuracy and if we said yeah okay but let's go and let's say I I a 6000 Pro. Okay, so here we have it. Here I am. Here Falcon 3B and a Mistro 7B on a Blackwell RTX 6000 Pro. Here we go. So the Falcon 3, here we are. Here you have the costing overhead ratio, our formula. And then here the reasoning accuracy what I'm really interested in. No, and yes, it is true. The blackwell, the native FP4 here allows our core to drop below one. This is great. 4bit is finally efficient. Yes, marketing of Nvidia is correct. But look at the reasoning accuracy. Yes, you're efficient. But look in blue, you have the 16 bit precision, the accuracy here. In red, you have the 8 bit. Oh, it's over there. And in green you have the forfeit accuracy for reasoning. You see the difference. This is also what's happening. [snorts] So sometimes the marketing mentions just one dimension of the complexity and maybe forgets to mention here that there is also the other side. But of course this was just Falcon 3B. You see smaller is really worse. You see this is a significant jump here. And if you go here to a 7B mist, you see it is not as bad. No, it is just this jump down here from the blue 16 bit down to the red 8 bit. So gives you a good idea about it. Yeah. And they tell you here, okay, rendering aggressive compression a failed strategy for high fidelity reasoning. And they wrote here beautiful theorem. As I told you, this is also a mathematical paper. If you want to have a more strategic deep dive, please read the paper and especially have a look out for theorem 4.5. The amodization and trust, the accuracy decoupling that is happening here and they show this on a 6000 Pro. So this means the quantization noise compounds across the reasoning hops degrading the reasoning trust the reasoning accuracy in a manner that cannot be amotized by batching. This is if you want to 4.5 and moreover multihub reasoning remains structurally sequential of course and includes a fixed per token overhead that do not scale linearly with the bit width. So therefore be careful. A 4bit quantization can break your causal reasoning, your logic chain that you need in your single agent or multi- aent system. I hope you enjoyed it. I hope there were some new data. Have a look at the study. Maybe leave a like. Become a member of my channel. I hope to see you in my next video.