NVIDIA’s Vera Whitepaper Has a Thread Loose

116 pointsposted 10 hours ago
by pella

18 Comments

twoodfin

9 hours ago

I don’t think picking a handful of SPEC benchmarks that approximate today’s most common agentic workloads (compiling code, interpreting Python) and then calling them “agentic benchmarks” is misleading at all.

That you need a whole lot of “ordinary” compute to benefit from the scaling properties of agents is the reason Nvidia is making this chip in the first place.

jcranmer

7 hours ago

The four benchmarks selected are cppcheck, llvm, cpython, and gcc [1]. These are all essentially compiler benchmarks... and all of the compiler benchmarks in SPEC cpu2026! This makes the benchmark selection somewhat suspicious to me, since it's not particularly representative of a diverse set of workloads.

I also don't buy that it's a particularly representative set of tasks you might do with agents. Also included in the SPEC benchmarks are multimedia codecs, lossless data compression codecs, sqlite (i.e., database), all of which are going to be things you should easily throw into the sets of tasks an agentic workload might do. Cherry-picking just the compiler benchmarks instead of all of SPECint... again, it just raises a couple of eyebrows.

[1] To be honest, I'm kinda surprised that both gcc and llvm are in SPEC cpu2026.

fibonacci112358

7 hours ago

The code in compilers is the closest to your typical app you can get in a benchmark like SPEC, eveerything else is actually far more specialized. Compiler code is full of small basic blocks, lots of branches, indirect memory access; it's actually harder to get good performance for such code, both for CPUs and compilers (that was part of the death of Itanium too).

jcranmer

5 hours ago

That's true of most of the applications in SPECint (SPECfp is a different matter); there's nothing special about compilers there.

Where compiler code is going to get really unusual, I suspect, is that compilers tend to be a little mono-focused on relatively few data structures. I know I was able to get measurable (single-digit percent!) performance differences in LLVM making very small tweaks to layout in llvm::Value. By contrast, when I was working on Thunderbird, the only similarly small change I could think to make that kind of difference would be to "oops, all string functions are now a cross-DLL call" (and even then, only because string handling is so dominant in that kind of application). Another kind of difference is that the compiler-based benchmarks are going to be quite light in virtual or indirect function calls (there's more of an emphasis on switch-based dispatching than vtable-based dispatching in most compiler implementations), which is going to make it a poorer proxy for some kinds of applications.

lstodd

5 hours ago

Death of Itanium was that it was a) VLIW and b) Intel was too arrogant. So it went to the same destination as later Larrabee and ATI/AMD attempts at VLIW GPUs.

That is, nowhere.

Also you are wrong and anyone sizing up an arch to put their loads onto must first try that load on it and not rely on "bah, compilers compile on it".

pjmlp

2 hours ago

Itanium only died because AMD exists, and due to various licensing reasons they were allowed to come up with AMD64.

speed_spread

4 hours ago

VLIW works for DSP applications, it's not an instant dead end. It's a good fit in cases where code path and memory accesses are predictable, like shader code.

transcriptase

7 hours ago

Let’s not forget the fact that despite their new market cap and being the beneficiary of having a near monopoly on making incredibly complex pickaxes during a gold rush… Nvidia is still the company with a long and consistent history of misleading their customers via marketing. Their greatest hits include:

- The digital equivalent of the VW emissions scandal where drivers detected when they were being benchmarked and altering rendering for better results

- Selling GPUs as having 4gb vram when it was only 3.5gb usable, the remaining 0.5 being absurdly slower and causing performance loss when used

- Using intentionally misleading naming schemes to obfuscate things like memory bus width being drastically different between what superficially appeared to be similarly spec’d cards

A dozen other less egregious but similarly disingenuous decisions

But to be clear, I’m a huge fan and continue to run Nvidia because their products are generally incredible regardless

foota

9 hours ago

This is neat. I'm only halfway through but some really cool discussion about the internals of the chips and what they offer.

amluto

6 hours ago

In today’s “agentic” world, everyone seems to have forgotten approximately everything we used to know about security. And this new CPU is going all in on value speculation. Delightful.

Maybe if “cyber” models get good enough at exploiting speculation attacks, people will start demanding equipment that is less prone to these attacks.

archipelago123

5 hours ago

Attacker: I can run any code on this machine? Time for speculation attacks!

Attacker: Oh wait, I can run any code? I already own the machine...

debugnik

34 minutes ago

Not really, Spectre showcased an attack from within a JavaScript sandbox, which isn't considered to own the machine.

Any side effects from speculation machinery can easily become a side-channel to infer values across security boundaries.

davoneus

9 hours ago

I'm glad to see the competition; nothing provokes meaningful change without it. Not surprised at Nvidia's blatant fabrications though; more of the same we've seen time and again (Superchip anyone, with 2+ year old designs).

pjmlp

2 hours ago

So typical big corp marketing material disguised as whitepaper.

brcmthrowaway

8 hours ago

So, is this rip AMD

wmf

8 hours ago

AMD announced a faster CPU two days later.

allenrb

7 hours ago

It’s a time-honored tradition to compare your upcoming product to the competition’s old news.

That said, looks like an impressive core and complete CPU built with it. Would love to see a smaller, affordable version.

jnaina

4 hours ago

Olympus’s raison d’être is to orchestrate GPUs—handling the control-heavy, latency-sensitive workloads that keep Rubin fed and the AI factory running without bottlenecks. Including tool calling and data marshaling.

They are not competing in the CPU space. Different markets.