Cloud GPU Quotas and Cold Starts Derail Model Training
AI/ML engineers face significant challenges in accessing, managing, and relying on GPU resources for training and inference. These issues range from inconsistent environments and unreliable hardware to difficulties in scaling and data portability, hindering productivity and increasing operational overhead. The need for automation and robust infrastructure is evident.
SOURCES (60)
“Say one card in a 64-GPU job starts throwing Xid errors three hours in. The job dies with an NCCL timeout that doesn't tell you much, and somebody loses the afternoon going node by node to find the bad one. Do you test nodes before…”
“Thunder Compute (YC S24) | C++ Systems, Infrastructure, BizOps | San Francisco (Onsite) | Full-time | https://www.thundercompute.comWe're building the VMware for GPUs. Most GPUs in the cloud sit idle a lot of the time because they're bolted to one machine over PCIe. We decouple them.Our virtualization layer is a userspace shim, loaded through LD_PRELOAD, that intercepts CUDA calls and ships them over the network to a host with a physical GPU somewhere else in the data center. Workloads run uncha”
“did you set it up so that only experts are on the ssd? (and ideally have the always active stuff in vram)? I expcet that if you use --n-cpu-moe to with max gpu offload to put all the always active in vram and only have experts in system ram and ssd you'd get more speed.”
“Clustering is the main benefit of the sparks. Otherwise you’ll have to choose a machine that doesn’t have enough ram, or the $120,000 variant dgx station and have nothing inbetween”
“You don't rely on them. You rely on open weight models on servers with ZDR you can trust.”
“Yes, there's a compute shortage. I'm squeezing my lab cluster with NVMe tiering to double density, but this isn't going to get better any time soon.”
“I’m not too experienced with this, but what is the reason no one has mentioned Azure as a solution ? Is it the cost ? My last job had a handful of VMs in azure . Unfortunately I don’t get much cloud expose in my new role.”
“Thanks for sharing this OP. If you are purchasing this for a company where running AI is seen as a support function, this cost is just small % of overall operating cost. Then there is the ROI factor, use it for runing use cases with greater ROI and owning could turn out more profitable. Sometimes, you can not rent because policies require data not to leave your premises. If you look at getting the increase in datacenter costs in terms of cooling, power, spares and support - renting could still b”
“for those who dont have the means to run locally, there's cloud subs/api. if you want to run custom models, there's gpu rental. Last time I looked at this you had to first rent a gpu from runpod/vast etc, storage (or use s3), manually connect, download and run the model, tools and finally get an inference endpoint you then use with a local client. Now I think this might be much simpler? eg HF can host your model, or there are other services like featherless. whats the process now and how”
“Regarding our plans we actually calculated based on our operational costs for our servers and GPU resources we're utilizing, The costs were therefore determined by the range at which we actually make profit per group of users. I've reviewed Haggin and it does look ideal for Saas, but it gets different when your product utilizes resources that are expensive like GPU cloud resources therefore the stats determine the pricing model of the company”
“There's no world where this makes sense. Let's take it issue by issue.1. Hardware failures: If you know anything about data centers you know that servers fail all the time. I've seen estimates of AI DC servers that have a failure rate of about 9% annually. A terrestrial DC has hardware techs who get alerted to the failure, replace the bits with known good bits and then take the suspect bits for testing to either bin them or verify they're working. You have no option to economically do this in sp”
“API pricing is probably going to be cheaper than renting bare metal, and scales to your needs. The next best option, if you’re truly going to get your money’s worth - is to acquire the hardware yourself”
“Random thought I've been kicking around. We keep talking about GPU compute in terms of renting machines by the hour. But for a lot of SaaS AI workloads, the GPU itself isn't really what I want. I just want the job done. Say I have 500k documents to process. Why do I care whether that takes one GPU for 20 hours, 10 GPUs for 2 hours, or 100 GPUs for 12 minutes? What I actually care about is: $X + done by tomorrow. Obviously this gets complicated pretty quickly once you bring in privacy, re”
“I’d compare cost per completed task, including retries, rather than just GPU busy time. A cheaper setup stops being cheap if the model needs twice as many attempts to get something right”
“Requests were flat. Cost wasn't. I checked batching, model size, quantization, all the obvious things first, and all of them were fine. What's your debugging order when this happens? Feels like everyone has a different first move. submitted by /u/Born-Woodpecker-4530 [link] [comments]”
“I run a small service that measures what a recurring GPU job costs on rented cards, so read me as an interested party. Over three weeks we paid for 22 runs on RunPod, Vast.ai, Nebius and Hyperstack, about $37 in total. Here is what cost us time or money that no price list mentions. Startup is billed, and it varies a lot. RunPod containers were live in 5-10 seconds. The VMs on Nebius and Hyperstack took 3-7 minutes before anything ran. Nebius charged for those minutes, Hyperstack did not charge f”
“As some may already know, I made a Vulkan resident trainer so anyone with a gpu, can train their own model because almost all modern graphics cards can run Vulkan. But I came up with some problems. mainly that system ram would explode exponentially. so I fixed the memory leak, and also made a lot more of the work, go to GPU. The readme has all of the fixes in it now. If you still have questions, go ahead and ask them. If I can I'll answer them. submitted by /u/Savantskie1 [link]”
“No We’re partially running self hosted via a 4x rtx6000 Blackwell pro setup and since we’re training vision not a frontier llm training costs are way less than you’d expect. But in regards to like holding off to market until iOS and android are ready you wouldn’t? I just want to maximize the first retention of my user base especially before it gets too far or too many eyes on it”
“For Local deployment this idea could create opportunities for alternatives but i don't think corporations will buy cheaper hardware for their datacenter hoping they can just vibecode performance in the near future. Right now cuda still has more examples so even in this scenario Nvidia could benefit a bit from cuda because it will be easier for your local model to optimize inference on Nvidia hardware.”
“Keep the A40 as a staging box to validate model swaps before touching production. Failover sounds nice until you realize its weights have drifted from what you last deployed and nobody updated them. At concurrency 2 you won't need it running hot anyway.”
“There is an error here when attempting to perform an operation on r (which is on device 1) while the current active device is 0.”
“yeah if the container and VM management utilities are good enough I suppose this might satisfy the Paperwork Gods”
“Vmware, cause migration is a 2 year project and would involve rebuilding workshops for multiple bussiness units, its in the pipeline but still a year or 2 away so till then we pay the tax”
“You want good locality when training. Distributed training over the internet with 2 Gbps connections with 5,000 microsecond latency would be excruciatingly slow. There is good reason nVidia worked on infiniband in the 1990s, bought Mellanox in 2020, and ships compute clusters with 400 Gbps networking at <1 microsecond latency between servers. If companies stop shipping open weight models we can crowd-source the training data, but I suspect the actual training will require a non-profit to rent”
“Ohh. Some people reported seeing no bill after downsizing and then going back to 4c/24gb. Not sure what OCI is doing. Check: https://www.reddit.com/r/oraclecloud/comments/1uynaqa/im_using_payg_and_can_confirm_4c_24g_is_still/”
“That is precisely what I was trying to do. To date I’ve not needed a gpu in my server as the onboard intel igpu has been enough. I guess I’ll check out the market on GPU’s then revisit this topic. I assume more vram for gpu is better? 16g enough?”
“It’s always bothered me that after fine-tuning a model for a project, there isn’t a particularly easy way to host it without either running it locally and keeping a GPU on 24/7 or paying for an entire GPU server. There are managed options for LoRA serving on top of vLLM (AWS), but you generally still end up paying for an entire instance. I started wondering: if 99%+ of the model weights are identical between the base model and something like a rank 8–32 LoRA/QLoRA adapter, why does each adapter”
“I wrote an article about setting up local llm with multi GPU set up, with focus on budget options. Hope it helps newcomers here. submitted by /u/lblblllb [link] [comments]”
“What sort of work do you do? Not seen many individuals doing custom model development”
“Really? Link? First I'm hearing of it I mean it would make sense. When I tried renting an 8x cmp instance on vast once one of the GPUs was just dead”
“yeah i see i can switch to luna on plus. i just want the extended memory for context. luna can handle the tasks i need done, once i up the ante on my game dev, i can hand off the more complex bits to sol”
“Free GPUs on Kaggle and Colab are excellent. Turning a real research or production-style repo into something that actually runs on them is still a pain. You either: - Manually copy-paste files into cells - Clone the repo inside the notebook and immediately hit missing imports, broken paths, indentation issues, etc. - Zip and upload as a dataset (solves the clone problem but not the config/iteration problem) - End up with a notebook history that is pure garbage because the project was never meant”
“Solid writeup, and good call writing it down. The keep-alive flip is the part most people miss: "cheapest GPU" isn't even a stable answer, it depends entirely on request arrival. The same trap exists on the API side. Teams pick a provider by headline $/token and ignore that real cost is decided by how the pipeline uses it: retries, tool-call loops, long payloads, idle connections. The "cheapest" provider regularly ends up the most expensive per completed task once you cou”
“True but the sound of being able to run close to frontier 24/7 is attractive but the total cost + electricity the math prob favors cloud still”
“It’s crazy that 15k still can’t buy you local hosting for decent sized models. Tin foil hat but these mega corps do not want people to afford frontier at home.”
“Did you work that out before or after you spent exorbitant amounts of money on the hardware?”
“And since you're not running those GPUs efficiently at 95% utilization rates or higher to serve tokens profitably, you paid far more than the people who simply pay per token from Together AI or Fireworks AI or something. They also get ZDR/session privacy. If you're really tin-foil hatted, you can go to a TEE cloud like Alpha Compute or something which is probably safer/more secure than your own computer.You're very right to be wanting to use open sourced models. You're delusional for thinking th”
“Oracle killed their 24GB free VMs and I bet their 12GB is not far behind if that's any indication.”
“Why not allocate, say a few thusand GPUs and CPU cores to it and ask it to train the best model it can and then call it GPT-OSS-2? They definitely have the compute. It cannot possibly leak any of OpenAI's secret sauce (architecture, training tricks, better data) if they don't give it any of that. And the inference bill costs them electricity. submitted by /u/Aggravating-Push-207 [link] [comments]”
“to run all the heavy apps like davinci solely on cloud which means it does not take any storage , cpu,gpu from your laptop”
“NVIDIA Dynamo's SLA Planner decides how many prefill and decode workers to run by forecasting next interval's load. The whole predictor interface is one method: def predict_next(self) -> float One number, one interval ahead, no uncertainty. I wanted to know whether a time-series foundation model does better there than the simple stuff, so I built a harness that scores predictors on GPU-hours and SLO violations instead of on MAE. The key detail: every predictor is swept to the cheapest”
“Most "just self-host, it's cheaper" advice that we have heard skips the one number that decides it: how busy you keep the GPU. A GPU costs the same whether it's flat out or idle. An API only charges you when you call it. So self-hosting doesn't win on price per token. It wins once the GPU is busy enough to beat what the API would've charged you. So where's that line? Say you're running a 32B model on one GPU at about 50% utilization, against an API at $0.50 per”
“Huh, weird, what does it need fingerprinting data from the browser for in order to rate a GPU? It says it right on the data collection section: Browser/user-agent”
“I ended up with roughly $100k in Lambda.ai cloud credits and I'm trying to figure out the highest-leverage way to actually use them, or make money out of it. I'm interested in hearing people ideas and thoughts. If you had access to ~$100k of GPU compute, what would you do/build? Or, even better: what problem do you currently have where you'd happily pay for the output, but GPU cost makes it uneconomical today? submitted by /u/Bulky_Connection8608 [link] [comments]”
“In germany we have a bit pricier about 0,30€/kWh so if I calculated it correctly it would go about 150€ per year, which is okay. Because I would basically use it daily. Not every second of the day of course, but probably 2-6 hours daily. And which hardware are you using in your homeserver if I may ask?”
“So basically you more or less build a full pc with a real GPU I suppose? Probably with a 600W+ PSU?”
“Not buffered, not ECC, so for home AI lab server workloads, these would probably be overlooked.”
“I have an idea! Run the benchmark suite on two dedicated EC2 machines instead of the home lab: one runs rapira, the other runs the load tools (wrk, k6). Manage the whole rig with Terraform: apply brings it up, destroy removes it. Sketch: 2x c7a (x86 64, one vCPU per physical core), on demand, eu central 1, one cluster placement group. Server instance: php embedded plus the Rust toolchain; builds rapira from a configurable git ref, so any branch, PR, or release is benchable. Load instance: wrk, k”
“Performance wise, the memory bandwidth the gpu have define the generation performance. Anything below 250gb/s will craw to the useless speed. And even there only an MoE with A3B active parameters will be usable. Qwen3.6:35b-a3b will probably be your best bet 😅”
“Hi! I run a whole fleet of local machines. Some are used for machine learning, while others handle graphics workloads, data processing, rendering, and similar tasks. Overall, there’s nothing particularly complicated about it: they’re kept in a separate room with two air conditioners and KVM switches. I also use several separate electrical circuits, so the whole setup isn’t hanging off a single breaker panel. Most of it runs 24/7/36524/7/36524/7/365. In our case, maintaining this local hardware i”
No, datacenter GPU are not using pci-e. Same as the ram not being ddr5
“The prices of getting GPU in a cloud vary greatly and I have been hearing a lot of things about inferencing taking place closer to where the data was collected from. I am interested to find out if it makes sense to run a small setup on an edge or an on-premise rather than getting resources from the cloud. My questions are for those who have experience of their own in that area: What exactly is being run locally, either training, fine-tuning or inference? How well does power and cooling work unde”
“🚀 The feature, motivation and pitch I noticed that Modal (https://modal.com/blog/gpu mem snapshots) and InferX (https://inferx.net/) have implemented the CUDA checkpoint/restore API to drastically reduce cold start. I tried both of the services and it seems to work extremelly well. InferX told me they build it on top of vLLM so it definitly seems possible to do. Right now, I'm not aware of any open source implementation of this tech. I would love to see this feature implemented in vLLM as it wo”