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I had to replace a boot ssd in a n9k-c93108 but i can't find any working images? Could anyone help me with such a thing? Thanks!

I really like the HGX SXM4 A100 setups instead of PCIE AIC with a bridge. If your purchasing a whole server with everything I don't think there is much cost difference.

our 3090s running in a server room so the heat issue is not bad for us have the lowest latency with bs1 inference

Machine (252GB total) Package L#0 NUMANode L#0 (P#0 126GB) L3 L#0 (16MB) L2 L#0 (512KB) + L1d L#0 (32KB) + L1i L#0 (32KB) + Core L#0 PU L#0 (P#0) PU L#1 (P#32) L2 L#1 (512KB) + L1d L#1 (32KB) + L1i L#1 (32KB) + Core L#1 PU L#2 (P#1) PU L#3...

Right, we were investigating numa domains and such. Also correct that our 3090 test rig is a single processor i9 9900k, so much simpler topology. And we're talking about ~10ms/req, it seems much higher than could be possible with a numa issue and the machine not being under significant load.

No, we are trying to figure out if its just too early with pytorch or a driver issue or something else? And by faster I ment lower latency per frame processed in small batch sizes, not more frames processed in big batches...

The use case is storing a buffer of all frames from a bunch of cameras that infeed into a vision system in a factory so we can scrub through the data if we want, it uses about 1-2tb per 24h of run time. * all means frames that actually matter, not frames where for example no motion happened...

I would like to stand up 2-4TB of memory for a rotating image cache not writing to a disk? These images are not critical to store for long periods, right now we just store a few minutes worth of content. Suggestions?

I am interested in the 2x 48-500W and US-8-150W switches and 2x UAP‑AC‑HD I'm not local, shipping to CA would be required, but I will take both switches at once? Let me know.

We are doing awesome with the RTX 3090 for pytorch and vision. It seems to be faster than RTX Titan and A100 SXM

Is anyone else doing inferance on a100 with pytorch? We run a number of vision machines with the following cards RTX Titan, RTX 3090, SXM A100 The important metric for us is frame to class latency with bursts of frames, we batch results up to N frames per batch and count the round trip time...

Anyone getting rid of V100s yet? Let me know, I could use a few.

What kind of server chassis are people using for 4x or 8x RTX Titan configurations? I know there are a number of things built up for V100

Anyone have the firmware to update these?

I've exaggerated the spanning tree event frequency, it just worries me that the network has become too large.

How big is too big for people setting up flat networks? I'm getting to the point where I think there are a lot of spanning tree events and now I've had a situation with a Ubiquiti ES-16-XG 10gb switch that failed to block a spanning tree loop causing a forwarding loop, it took me a little bit...

I have a number of vlans and want to spin up containers that provide vlan specific services, this works awesome and is super easy with Docker. Now I want to also drop that container on the bridge 172.17/16 network and pull data with telegraf, this works with an error when I try to add the...

This is awesome, I have looked into this problem just enough to become frustrated and switch to MNX QSFP cards and have a pile of cards on a shelf that do not accept "locked"/unapproved SFP+ modules for SM LR 10k. I felt like I had done something wrong here and just needed to take more time on...

Yes, I think its quite important to decouple the storage performance and network performance. Most of the testing I have done with these MNX CX3 cards gets about 22gig/sec from host to switch to host For me, this is a very typical iperf3 output for a single thread from a xeon 2697v2 to another...

Have you tried multiple iperf3 processes on different ports at the same time?