r/gpu

I got the gpu for free as a PC repair shop threw it away for overheating. new thermal pads cost almost as much as it was worth.

it's powered by USB c and in made an adjustable pwm signal from a 555 timer to make the fans turn slowly.

i built my pc in 2020 with a 570p xt. is this a good upgrade at a fair price?

Ok... So long story short, bought a pair 32gb and a 16gb V100s one with a PCIE adaptor mounting board and one with this external style PCIE mounting board. The expensive one went into the PCIE and long story short, it poofed the second I applied power to it. I read and re-read the instructions I tried AI, I asked elsewhere, I even looked at the Chinese manufacturers website "GPU Cables" is the most vaguest bullshit I've ever encountered. Everything told me PCIE cable or "GPU Cables", I saw redit saying CPU cables, but that was them powering the device directly or inside a server. So I figured the adaptor board must be doing something to change the polarity before it hit the card. NOPE. POOF. Dead. I took the whole thing apart and inspected everything with a voltmeter, the only damaged is one MOSFETs chip, GPU and VRAM both checked out fine. So, I got a hot air solder kit and replacement MOSFET with a spare on the way. Still not 100% sure if it was a case of wrong cables or the fact I bought this used and maybe the Mosfet was on the way out... Anyway I intend to repair. Weirdly the adaptor board has no scorch marks and tested perfectly fine.

So this brings me to my current situation, my lesser V100 card has an external mount. I literally have a separate PSU that is powering just it and connects with the other PSU to the motherboard so it will turn on. As you can see the mother wires are going to the external mount to the PCIE adaptor card taking in the signal from the V100 to the motherboard. I tested this thing with a voltmeter trying to determine if it's PCIE or CPU... I marked red all the spots the beeped on red while black was grounded in a screwhead. And once when it was at the PLUS leg of a capacitor. The results are above. AI is telling it's PCIE. But I have no idea, because I don't know which side is "up" since the clip tag is pointed down, it's upside down. Hence why I marked everything in red and took enough photos to communicate the issue/layout, etc. even found some PCIE extension cables to illustrate which side would be yellow if plugged into this.

Can someone please tell me if this is PCIE or CPU? I'm honestly too afraid to turn it on, I've already smoked a card worth over a grand and if this smoked too I'm going to spiral. My heart hurts at the amount of equipment I just lost and I need at least one V100 to be working to do my college work.

Any help would be appreciated.

This is the ebay auction I got the adaptor from, I tried messaging the seller twice, since they are selling multiple, no response.

External Nvidia Tesla P100 V100 SXM2 PCI-E X16 +1* SFF-8654 Adapter +2 Cable | eBay

The other card adaptor that blew up the 32gb card I bought the seller did get back to me, but he basically sent me the same text I got from google translating the manual... Which is vague.

Huh, apparently I forgot to add the one shot of my workbench I took so you can see the other side of what it's plugged into... Let me know if you need that. I can't seem to add another photo to this post for some reason (I rarely post on Reddit at all).

https://github.com/Grubre/smol-gpu

The designer says it's for educational purposes, but the amount of stuff stripped away makes me question how much it actually teaches about real GPU architecture.

Here's what's been simplified away:

1. Sequential warp scheduling : one warp runs to completion, then the next. No latency hiding at all.

2. No warp-level parallelism within a core : only one warp occupies resources at a time.

3. No cache hierarchy : cores talk directly to global memory.

4. Separated program and data memory : Harvard style, not unified.

5. No shared memory / scratchpad : so no cooperative algorithms between threads.

6. No barrier / synchronization primitives : no __syncthreads() equivalent.

7. No reconvergence stack in hardware : divergence is handled purely through manual masking.

8. No memory coalescing : each thread issues its own memory request.

9. No FPU, no special function units : integer only.

10. No atomics, no fence : subset of RV32I.

At this point it's basically executing one warp after another on each core. If you squint, this is just a multicycle processor that happens to run 32 threads in lockstep. Yes, the SIMT model and execution masking are there, but without pipelining, warp interleaving, or caches, you're not really seeing what makes GPUs fast.

Is there any deeper reasoning behind stripping this much out? And more importantly, I've gone through the RTL and spotted what look like potential race conditions in a few places. Is this repo even a legit baseline to build a more advanced GPU on top of, or would you be better off starting from scratch?

How much VRAM bandwidth does an application or a game pull? Is the traffic over PCIe a bottleneck? What's the CPU/GPU load, RAM/VRAM occupation, temperatures, power draw, clock frequencies? hw-smi works with all CPUs and all Nvidia/AMD/Intel GPUs, on both Windows and Linux.

There are lots of cool hardware monitoring tools already - but they all are either OS-specific, only for CPU or GPU, only for one particular vendor's GPUs, and none of them tell you the VRAM/PCIe bandwidth to give clues about application bottlenecks. 2 years ago I thought to myself: I can do this better. Now I share this powerful tool with the world, for free. Have fun!

https://github.com/ProjectPhysX/hw-smi