Groq LPU - Finally, an AI chip that doesn't suck at inference

Groq's Language Processing Unit is what happens when someone finally stops trying to make graphics cards do AI inference and builds a chip that actually understands what transformers need. GPUs were designed to render triangles in video games, not process sequences of tokens. Groq said "fuck it" and built chips specifically for linear algebra - the matrix multiplication math that actually happens when you run language models.

Finally, software that isn't fighting the hardware

If you've ever spent a weekend debugging CUDA kernels that break every damn driver update, you'll appreciate this: instead of wrestling with hardware constraints, Groq lets software run the show. This was a nightmare on GPUs until Groq fixed it. No more writing model-specific kernels. No more hiring CUDA wizards who cost a fortune to make your inference not suck.

Their software-first compiler just takes whatever PyTorch or TensorFlow throws at it and figures out how to run it fast as hell. While you're still debugging memory allocation errors on your GPU cluster, Groq's compiler is automatically optimizing across multiple chips without you having to think about it.

The assembly line that actually makes sense

Groq built an assembly line for matrix math instead of the GPU clusterfuck where everything fights for resources. Their programmable assembly line moves data through processing units in a predictable sequence, telling each unit exactly what to do and where to put the result.

Meanwhile, GPU's "hub and spoke" clusterfuck where everything has to fight for access to shared resources. LPU data just flows smoothly through the pipeline, no waiting for memory controllers to sort their shit out. When you chain multiple chips together, they work like one big assembly line instead of a networking nightmare.

The TSP is basically Groq's answer to GPU clusterfucks - instead of everything fighting for resources, data just flows through the pipeline like an actual assembly line should work.

Performance you can actually predict

Get this: LPU performance is predictable down to individual clock cycles. No more "it usually takes 200ms but sometimes 2 seconds for no fucking reason" like with GPU inference latency spikes. When Groq says it'll take X cycles, it takes X cycles. Every time.

This matters when you're trying to hit SLA targets and don't want to get woken up at 3am because your inference latency randomly spiked. GPU cluster started acting weird during peak traffic - took us way too long to figure out it was thermal throttling. Users were not happy. With GPUs, you're always guessing performance. With LPUs, you can actually plan capacity and make promises to customers.

Memory that doesn't hate you

GPU memory is a nightmare - separate HBM chips, cache hierarchies, switches everywhere, all fighting for bandwidth. Groq just put everything on one chip with something like 80TB/s bandwidth. For context, your fancy A100 gets maybe 8TB/s from its off-chip memory when everything's working perfectly.

No more memory hierarchy bullshit - everything the chip needs is right there instead of shuffling data around like an idiot.

That 10x memory bandwidth means no more waiting for data to shuffle around between memory layers. Everything the chip needs is right there, ready to go. It's like having your entire dataset cached in L1 cache instead of fetching from disk every time.

So that's the basic idea - assembly line beats clusterfuck. Now let's look at how they actually built this thing.

Tensor Streaming Processor - built for how transformers really work

Groq's Tensor Streaming Processor finally gets what language models actually do: generate one token at a time in sequence. While GPUs are trying to parallelize everything because that's what they're built for, transformers are fundamentally sequential. You can't generate token N+1 until you've processed token N.

Their software figures out all the complex mapping bullshit automatically, so you don't have to become a hardware wizard to make your models run fast.

The TSP streams data through processing tiles that handle 16 vector elements at once, which maps perfectly to how attention heads and matrix multiplications actually work in transformers. Instead of fighting the sequential nature, they embraced it.

A compiler that doesn't make you want to cry

Remember spending months optimizing CUDA kernels for your specific model, only to get CUDA_ERROR_OUT_OF_MEMORY when you try to run it in production? CUDA optimization is a nightmare that takes forever. Groq's compiler says screw that nonsense. It automatically figures out how to map your model across chips without you having to become a hardware expert.

The compiler maps out every data movement ahead of time, which is why performance is predictable. No runtime surprises, no "it worked in dev but not in prod" moments. When you deploy to production, you know exactly how it'll perform because the compiler already planned the entire execution.

How to actually get your hands on this

You've got two choices, depending on how much control you need and how much money you want to spend:

GroqCloud is the easy button - API calls that just work without you having to rack servers or hire a team to manage inference infrastructure. Good for most use cases where you don't mind your data hitting Groq's servers. Rate limits are pretty generous for a free tier.

GroqRack is for when you need everything on-premises because compliance reasons or your CTO is paranoid about data leaving the building. You're looking at rack-scale hardware purchases and convincing your IT team to deploy custom silicon. Budget most of a year and costs more than a car. But then it's yours and your data never leaves the building.

Your power bill will actually be readable

10x better energy efficiency isn't marketing bullshit - it's what happens when you stop fighting the hardware and build something that makes sense. No complex scheduling circuits fighting each other, no data ping-ponging between memory and compute units.

This efficiency matters when you're running thousands of requests per second and your power bill is making your CFO cry. GPU clusters eat electricity like it's free. LPU infrastructure uses a fraction of the electricity of equivalent GPU clusters, which means less money hemorrhaging on power and cooling.

The energy savings compound at scale - what costs thousands in GPU power consumption might cost hundreds with LPU infrastructure.

14nm that somehow beats 4nm GPUs

Here's the kicker: Groq's current chips are built on 14nm processes - ancient by semiconductor standards - yet they still demolish modern 4nm GPUs at inference. Something like 80TB/s bandwidth while GPUs struggle with a tenth of that. When they eventually move to 4nm processes, the performance gap could get even more ridiculous.

The software-first approach means hardware improvements just make everything faster without you having to rewrite code or re-optimize models. Unlike GPU development where new architectures require months of kernel rewrites.

So that's how they built it. Now you probably have questions about whether this actually works in practice.