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I'm a huge fan of OpenRouter and their interface for solid LLM's but I recently jumped into fine tuning / modifying my own vision models for FPV drone detection

I'm a huge fan of OpenRouter and their interface for solid LLM's but I recently jumped into fine tuning / modifying my own vision models for FPV drone detection (just for fun) and my daily workstation and it's 2080 just wasn't good enough. Even in 2025 it's cool how solid a setup dual 3090's still are. nvlink is an absolute must but it's incredibly powerful. I'm able to run the latest Mistral thinking models and relatively powerful yolo based VLM's like the ones RoboFlow is based on. Curious if anyone else is still using 3090's or has feedback for scaling up to 4-6 3090s. Thanks everyone ;)

deleted 1y ago
[deleted]
CraigJPerry 1y ago
if it's just for detection would audio not be cheaper to process? I'm imagining a cluster of directional microphones, and then i don't know if it's better to perform some sort of band pass filtering first since it's so computationally cheap or whether it's better to just feed everything into the model directly. No idea. I guess my first thought was just sounds from a drone likely is detectable reliably at a greater distance than visual, they're so small and a 180 degree by 180 degree hemisphere of pixels is a lot to process. Fun problem either wayway.
fxtentacle 1y ago
The 3090 are a sweet spot for training. It’s the first generation with seriously fast VRAM. And it’s the last generation before Nvidia blocked NVlink. If you need to copy parameters between GPUs during training, the 3090 can be up to 70% faster than 4090 or 5090. Because the latter two are limited by PCI express bandwidth.
- jacquesm 1y ago
  To be fair though, the 4090 and 5090 are much easier capable of saturating PCI express than the 3090 is, even at 4 lanes per card the 3090 rarely manages to saturate the links, it still handsomely pays off to split down to 4 lanes and add more cards. I used: https://c-payne.com/ https://c-payne.com/ Very high quality and manageable prices.
  - ericdotlee 1y ago
    I've purchase 16 of these - cpayne is great! Hope he finds a US distributor to help with tariffs a bit!
    - jacquesm 1y ago
      What blew me away is the quality and price point of what obviously can't be a very high volume product. This guy makes amazing stuff.
jacquesm 1y ago
I've built a rig with 14 of them. NVLink is not 'an absolute must', it can be useful depending on the model and the application software you use and whether you're training or inferring. The most important figure is the power consumed per token generated. You can optimize for that and get to a reasonably efficient system, or you can maximize token generation speed and end up with two times the power consumption for very little gain. You also will likely need to have a way to get rid of excess heat and all those fans get loud. I stuck the system in my garage, that made the noise much more manageable.
- breakds 1y ago
  I am curious about the setup of 14 GPUs - what kind of platform (motherboard) do you use to support so many PCIe lanes? And do you even have a chassis? Is it rack-mounted? Thanks!
  - jacquesm 1y ago
    I used a large supermicro server chassis, a dual Xeon motherboard with 7 8 lane PCI Express slots, all the ram it would take (bought second hand), splitters, four massive powersupplies. I extended the server chassis with aluminum angle riveted onto the base. It could be rack mounted but I'd hate to be the person lifting it in. The 3090s were a mix, 10 of the same type (small, and with blower style fans on them) and 4 much larger ones that were kind of hard to accommodate (much wider and longer). I've linked to the splitter board manufacturer in another comment in this thread. That's the 'hard to get' component but once you have those and good cables to go with them the remaining setup problems are mostly power and heat management.
    - breakds 1y ago
      Thanks that is very inspiring. I thought there are no blower type consumer GPUs, but apparently they exist!
      - jacquesm 1y ago
        I got them second hand off some bitcoin mining guy. https://www.tomshardware.com/news/asus-blower-rtx3090 https://www.tomshardware.com/news/asus-blower-rtx3090 Is the model that I have.
vladgur 1y ago
I am exploring options just for fun. a used 3090 is around $900 on ebay. a used rtx 6000 ADA is around $5k 4 3090s are slower at inference and worse at training than 1 rtx 6000. 4x3090 would consume 1400W at load. Rtx 6000 would consume 300W at load. If you god forbid live in California and your power averages 45 cents per kwh, 4x3090 would be $1500+ more per year to operate than a single RTX 6000[0] [0] Back of the napkin/ChatGPT calculation of running the GPU at load for 8 hours per day. Note: I own a pc with a 3090, but if i had to build an AI training workstation, i would seriously consider cost to operate and resale value(per component).
- logicallee 1y ago
  >I am exploring options just for fun. Since you're exploring options just for fun, out of curiosity, would you rent it out whenever you're not using it yourself, so it's not just sitting idle? (Could be noisy and loud). You'd be able to use your computer for other work at the same time and stop whenever you wanted to use it yourself.
  - vladgur 1y ago
    It depends. At my electricity cost, 1 hour of 3090 or 1 hour of Rtx 6000 would cost the same 0.45 Just checked vast.ai. I will be losing money with 3090 at my electricity cost and making a tiny bit with rtx 6000. Like with boats it’s probably better to rent GPUs then buy them
    - justinclift 1y ago
      Would a solar panel setup be an option for fixing that? :)
    - logicallee 1y ago
      (you should also be compensated for the noise and inconvenience from it, not only electricity.) It sounds like you might rent it out if the rental price were higher.
- supermatt 1y ago
AJRF 1y ago
You really don't need NVLink, you won't saturate the PCIe lanes on a modern motherboard with dual 3090s. Tim Dettmers amazing GPU blog post posits NVLink doesn't start to become useful until you are at 128+ GPUs https://timdettmers.com/2023/01/30/which-gpu-for-deep-learning/#What_is_NVLink_and_is_it_useful https://timdettmers.com/2023/01/30/which-gpu-for-deep-learni...
XCSme 1y ago
I bought a 2nd 3090 2 years ago for like 800eur, still a good price even today I think. It's in my main workstation, and my idea was to always have Ollama running locally. The problem is that once I have a (large-ish) model running, all my VRAM is almost full and GPU struggles to do things like playing back a YouTube video. Lately I haven't used local AI much, also because I stopped using any coding AIs (as they wasted more time than they saved), I stopped doing local image generations (the AI image generation hype is going down), and for quick questions I just ask ChatGPT, mostly because I also often use web search and other tools, which are quicker on their platform.
- lifeinthevoid 1y ago
  I run my desktop environment on the iGPU and the AI stuff on the dGPUs.
  - XCSme 1y ago
    That's a real good point! Unfortuatenly, my CPU (5900x) doesn't have an iGPU. The last 5 years iGPU got a bit out of trend. Now maybe they actually make a lot of sense, as there is a clear use-case which involves having dedicated GPU always in-use which is not gaming (and gaming is different, cause you don't often multi-task while gaming). I do expect to see a surge in iGPU popularity, or maybe a software improvement to allow having a model always available without constantly hogging the VRAM.
    - XCSme 1y ago
      PS: I thought Ollama had a way to use RAM instead of VRAM (?) to keep the model active when not in use, but in my experience that didn't solve the problem.

deleted 1y ago

[deleted]

CraigJPerry 1y ago

if it's just for detection would audio not be cheaper to process? I'm imagining a cluster of directional microphones, and then i don't know if it's better to perform some sort of band pass filtering first since it's so computationally cheap or whether it's better to just feed everything into the model directly. No idea. I guess my first thought was just sounds from a drone likely is detectable reliably at a greater distance than visual, they're so small and a 180 degree by 180 degree hemisphere of pixels is a lot to process. Fun problem either wayway.

fxtentacle 1y ago

The 3090 are a sweet spot for training. It’s the first generation with seriously fast VRAM. And it’s the last generation before Nvidia blocked NVlink. If you need to copy parameters between GPUs during training, the 3090 can be up to 70% faster than 4090 or 5090. Because the latter two are limited by PCI express bandwidth.

jacquesm 1y ago
To be fair though, the 4090 and 5090 are much easier capable of saturating PCI express than the 3090 is, even at 4 lanes per card the 3090 rarely manages to saturate the links, it still handsomely pays off to split down to 4 lanes and add more cards. I used: https://c-payne.com/ https://c-payne.com/ Very high quality and manageable prices.
- ericdotlee 1y ago
  I've purchase 16 of these - cpayne is great! Hope he finds a US distributor to help with tariffs a bit!
  - jacquesm 1y ago
    What blew me away is the quality and price point of what obviously can't be a very high volume product. This guy makes amazing stuff.

jacquesm 1y ago

I've built a rig with 14 of them. NVLink is not 'an absolute must', it can be useful depending on the model and the application software you use and whether you're training or inferring. The most important figure is the power consumed per token generated. You can optimize for that and get to a reasonably efficient system, or you can maximize token generation speed and end up with two times the power consumption for very little gain. You also will likely need to have a way to get rid of excess heat and all those fans get loud. I stuck the system in my garage, that made the noise much more manageable.

breakds 1y ago
I am curious about the setup of 14 GPUs - what kind of platform (motherboard) do you use to support so many PCIe lanes? And do you even have a chassis? Is it rack-mounted? Thanks!
- jacquesm 1y ago
  I used a large supermicro server chassis, a dual Xeon motherboard with 7 8 lane PCI Express slots, all the ram it would take (bought second hand), splitters, four massive powersupplies. I extended the server chassis with aluminum angle riveted onto the base. It could be rack mounted but I'd hate to be the person lifting it in. The 3090s were a mix, 10 of the same type (small, and with blower style fans on them) and 4 much larger ones that were kind of hard to accommodate (much wider and longer). I've linked to the splitter board manufacturer in another comment in this thread. That's the 'hard to get' component but once you have those and good cables to go with them the remaining setup problems are mostly power and heat management.
  - breakds 1y ago
    Thanks that is very inspiring. I thought there are no blower type consumer GPUs, but apparently they exist!
    - jacquesm 1y ago
      I got them second hand off some bitcoin mining guy. https://www.tomshardware.com/news/asus-blower-rtx3090 https://www.tomshardware.com/news/asus-blower-rtx3090 Is the model that I have.

vladgur 1y ago

I am exploring options just for fun. a used 3090 is around $900 on ebay. a used rtx 6000 ADA is around $5k 4 3090s are slower at inference and worse at training than 1 rtx 6000. 4x3090 would consume 1400W at load. Rtx 6000 would consume 300W at load. If you god forbid live in California and your power averages 45 cents per kwh, 4x3090 would be $1500+ more per year to operate than a single RTX 6000[0] [0] Back of the napkin/ChatGPT calculation of running the GPU at load for 8 hours per day. Note: I own a pc with a 3090, but if i had to build an AI training workstation, i would seriously consider cost to operate and resale value(per component).

logicallee 1y ago
>I am exploring options just for fun. Since you're exploring options just for fun, out of curiosity, would you rent it out whenever you're not using it yourself, so it's not just sitting idle? (Could be noisy and loud). You'd be able to use your computer for other work at the same time and stop whenever you wanted to use it yourself.
- vladgur 1y ago
  It depends. At my electricity cost, 1 hour of 3090 or 1 hour of Rtx 6000 would cost the same 0.45 Just checked vast.ai. I will be losing money with 3090 at my electricity cost and making a tiny bit with rtx 6000. Like with boats it’s probably better to rent GPUs then buy them
  - justinclift 1y ago
    Would a solar panel setup be an option for fixing that? :)
  - logicallee 1y ago
    (you should also be compensated for the noise and inconvenience from it, not only electricity.) It sounds like you might rent it out if the rental price were higher.
supermatt 1y ago

AJRF 1y ago

You really don't need NVLink, you won't saturate the PCIe lanes on a modern motherboard with dual 3090s. Tim Dettmers amazing GPU blog post posits NVLink doesn't start to become useful until you are at 128+ GPUs https://timdettmers.com/2023/01/30/which-gpu-for-deep-learning/#What_is_NVLink_and_is_it_useful https://timdettmers.com/2023/01/30/which-gpu-for-deep-learni...

XCSme 1y ago

I bought a 2nd 3090 2 years ago for like 800eur, still a good price even today I think. It's in my main workstation, and my idea was to always have Ollama running locally. The problem is that once I have a (large-ish) model running, all my VRAM is almost full and GPU struggles to do things like playing back a YouTube video. Lately I haven't used local AI much, also because I stopped using any coding AIs (as they wasted more time than they saved), I stopped doing local image generations (the AI image generation hype is going down), and for quick questions I just ask ChatGPT, mostly because I also often use web search and other tools, which are quicker on their platform.

lifeinthevoid 1y ago
I run my desktop environment on the iGPU and the AI stuff on the dGPUs.
- XCSme 1y ago
  That's a real good point! Unfortuatenly, my CPU (5900x) doesn't have an iGPU. The last 5 years iGPU got a bit out of trend. Now maybe they actually make a lot of sense, as there is a clear use-case which involves having dedicated GPU always in-use which is not gaming (and gaming is different, cause you don't often multi-task while gaming). I do expect to see a surge in iGPU popularity, or maybe a software improvement to allow having a model always available without constantly hogging the VRAM.
  - XCSme 1y ago
    PS: I thought Ollama had a way to use RAM instead of VRAM (?) to keep the model active when not in use, but in my experience that didn't solve the problem.