bastawhiz
13 hours ago
There's no way the red v2 is doing anything with a 120b parameter model. I just finished building a dual a100 ai homelab (80gb vram combined with nvlink). Similar stats otherwise. 120b only fits with very heavy quantization, enough to make the model schizophrenic in my experience. And there's no room for kv, so you'll OOM around 4k of context.
I'm running a 70b model now that's okay, but it's still fairly tight. And I've got 16gb more vram then the red v2.
I'm also confused why this is 12U. My whole rig is 4u.
The green v2 has better GPUs. But for $65k, I'd expect a much better CPU and 256gb of RAM. It's not like a threadripper 7000 is going to break the bank.
I'm glad this exists but it's... honestly pretty perplexing
oceanplexian
13 hours ago
It will work fine but it’s not necessarily insane performance. I can run a q4 of gpt-oss-120b on my Epyc Milan box that has similar specs and get something like 30-50 Tok/sec by splitting it across RAM and GPU.
The thing that’s less useful is the 64G VRAM/128G System RAM config, even the large MoE models only need 20B for the router, the rest of the VRAM is essentially wasted (Mixing experts between VRAM and/System RAM has basically no performance benefit).
syntaxing
12 hours ago
Split RAM and GPU impacts it more than you think. I would be surprised if the red box doesn’t outperform you by 2-3X for both PP and TG
overfeed
11 hours ago
> I'm also confused why this is 12U. My whole rig is 4u.
I imagine that's because they are buying a single SKU for the shell/case. I imagine their answer to your question would be: In order to keep prices low and quality high, we don't offer any customization to the server dimensions
ottah
9 hours ago
That's just such a massively oversized server for the number of gpus. It's not like they're doing anything special either. I can buy an appropriately sized supermicro chassis myself and throw some cards in it. They're really not adding enough value add to overspend on anything.
ericd
10 hours ago
Was that cheaper than a Blackwell 6000?
But yeah, 4x Blackwell 6000s are ~32-36k, not sure where the other $30k is going.
segmondy
9 hours ago
folks have too much money than sense, gpt-oss-120b full quant runs on my quad 3090 at 100tk/sec and that's with llama.cpp, with vllm it will probably run at 150tk/sec and that's without batching.
amarshall
9 hours ago
You're almost certainly (definitely, in fact) confusing the 120b and 20b models.
Aurornis
7 hours ago
> gpt-oss-120b full quant runs on my quad 3090
A 120B model cannot fit on 4 x 24GB GPUs at full quantization.
Either you're confusing this with the 20B model, or you have 48GB modded 3090s.
ericd
8 hours ago
How're you fitting a model made for 80 gig cards onto a GPU with 24 gigs at full quant?
Havoc
5 minutes ago
He said quad 3090 not single
zozbot234
8 hours ago
MoE layers offload to CPU inference is the easiest way, though a bit of a drag on performance
ericd
8 hours ago
Yeah, I'd just be pretty surprised if they were getting 100 tokens/sec that way.
EDIT: Either they edited that to say "quad 3090s", or I just missed it the first time.
bastawhiz
9 hours ago
I bought the A100s used for a little over $6k each.
ericd
8 hours ago
Oh, why'd you go that route? Considering going beyond 80 gigs with nvlink or something?
Aurornis
7 hours ago
> There's no way the red v2 is doing anything with a 120b parameter model.
I don't see the 120B claim on the page itself. Unless the page has been edited, I think it's something the submitter added.
I agree, though. The only way you're running 120B models on that device is either extreme quantization or by offloading layers to the CPU. Neither will be a good experience.
These aren't a good value buy unless you compare them to fully supported offerings from the big players.
It's going to be hard to target a market where most people know they can put together the exact same system for thousands of dollars less and have it assembled in an afternoon. RTX 6000 96GB cards are in stock at Newegg for $9000 right now which leaves almost $30,000 for the rest of the system. Even with today's RAM prices it's not hard to do better than that CPU and 256GB of RAM when you have a $30,000 budget.
zozbot234
13 hours ago
> And there's no room for kv, so you'll OOM around 4k of context.
Can't you offload KV to system RAM, or even storage? It would make it possible to run with longer contexts, even with some overhead. AIUI, local AI frameworks include support for caching some of the KV in VRAM, using a LRU policy, so the overhead would be tolerable.
tcdent
13 hours ago
Not worth it. It is a very significant performance hit.
With that said, people are trying to extend VRAM into system RAM or even NVMe storage, but as soon as you hit the PCI bus with the high bandwidth layers like KV cache, you eliminate a lot of the performance benefit that you get from having fast memory near the GPU die.
zozbot234
11 hours ago
> With that said, people are trying to extend VRAM into system RAM or even NVMe storage
Only useful for prefill (given the usual discrete-GPU setup; iGPU/APU/unified memory is different and can basically be treated as VRAM-only, though a bit slower) since the PCIe bus becomes a severe bottleneck otherwise as soon as you offload more than a tiny fraction of the memory workload to system memory/NVMe. For decode, you're better off running entire layers (including expert layers) on the CPU, which local AI frameworks support out of the box. (CPU-run layers can in turn offload to storage for model parameters/KV cache as a last resort. But if you offload too much to storage (insufficient RAM cache) that then dominates the overhead and basically everything else becomes irrelevant.)"
bastawhiz
9 hours ago
The performance already isn't spectacular with it running all in vram. It'll obviously depend on the model: MoE will probably perform better than a dense model, and anything with reasoning is going to take _forever_ to even start beginning its actual output.
ranger_danger
13 hours ago
I know llama.cpp can, it certainly improved performance on my RAM-starved GPU.
ottah
9 hours ago
Honestly two rtx 8000s would probably have a better return on investment than the red v2. I have an eight gpu server, five rtx 8000, three rtx 6000 ada. For basic inference, the 8000s aren't bad at all. I'm sure the green with four rtx pro 6000s are dramatically faster, but there's a $25k markup I don't honestly understand.