GPU Infrastructure for Solo Founders: 3 Clouds, $0.15/hour, Production ML

You don’t need a data center to train and deploy ML models. I run production inference for under $5/day.

This post covers the actual GPU infrastructure behind InkCloak — an AI text detector built on a LoRA fine-tuned DeBERTa model. I’ll walk through training, benchmarking, and deployment across three cloud platforms, with real costs attached to every step.

The Gap Nobody Talks About

Every ML tutorial ends the same way: “And now you have a trained model!” Then silence. No mention of how to serve it to real users. No discussion of cold starts, batch sizes, scale-to-zero, or what happens when your GPU bill hits $500/month.

Solo founders building ML products face a specific problem: you need production-grade inference without production-grade budgets. The options are either (a) wrap someone else’s API and compete on UX, or (b) own your model and own your infrastructure. I chose (b).

My Stack

Here is exactly what I run, with costs:

Training — RunPod community GPUs. An RTX 3090 costs $0.22/hour. LoRA fine-tuning a DeBERTa-v3-large detector on 2,400 texts across 8 different LLMs takes 15 minutes. Total cost per training run: $0.15.

Benchmarking — Same RunPod instance, same session. Running the RAID benchmark dataset (1,838 texts, 3 model comparisons) costs another $0.19. I get AUROC, TPR@FPR thresholds, and confusion matrices in one script.

Production Inference — RunPod Serverless with A4000 16GB workers. Dynamic batching with batch=8 yields 90 requests/second per worker. Three workers handle 270 req/sec — enough for 20K daily active users. Cost: $0.17/hour per active worker, zero when idle.

VPN/Proxy Infrastructure — DigitalOcean VPS ($4-6/month) for AmneziaWG tunnels, API proxies, and Cloudflare Workers. Not GPU-related, but part of the operational stack.

Three Platforms I Tested

RunPod: The Winner for ML

RunPod splits into two products that matter:

Community Cloud — on-demand GPUs for training and experimentation. Prices range from $0.16/hour (RTX 3090) to $0.34/hour (A5000). You SSH in, run your scripts, download artifacts, terminate. No contracts, no quotas, no approval processes.

# Create a pod with PyTorch pre-installed
runpodctl create pod \
  --name deberta-train \
  --gpuType "NVIDIA RTX 3090" \
  --imageName runpod/pytorch:2.1.0-py3.10-cuda11.8.0-devel \
  --disk 20 \
  --public-ip

Serverless — auto-scaling inference endpoints. You upload a handler, configure min/max workers, and RunPod manages the rest. Scale-to-zero means you pay nothing during off-hours. Cold start with a pre-loaded model volume: ~5 seconds. Without volume: ~30 seconds.

Network Volumes ($0.07/GB/month) persist model weights between pod terminations. One gotcha: volumes lock you to a specific datacenter. For one-off training, I skip volumes and SCP the LoRA adapter out (it is only 24MB).

Google Cloud Platform: The Budget Option

GCP offers $300 in free credits for new accounts. T4 Spot instances cost $0.11/hour — cheaper than RunPod — but they are preemptible. Google can reclaim your instance at any time with 30 seconds notice.

For batch jobs (processing a backlog of texts overnight), Spot T4s are excellent. For real-time inference serving actual users, the preemption risk makes them unsuitable as a primary endpoint. I use GCP as a fallback tier: if RunPod Serverless has a regional outage, a GCP batch endpoint can absorb overflow.

One friction point: GPU quota requests. New GCP accounts start with zero GPU quota. You submit a request, wait 24-48 hours, and sometimes get denied. Plan ahead.

Bare Metal: The Endgame

At sustained GPU usage above $500/month, owning hardware beats renting. A used Tesla T4 costs ~$200 on eBay. Colocation runs $50-100/month. Break-even happens around month 3-4.

I am not there yet. At current traffic (pre-launch), my GPU spend is $15-25/month. Bare metal makes sense when InkCloak hits 5K+ DAU with consistent load. Until then, scale-to-zero is the economically rational choice.

LoRA: Why Fine-Tuning Is Cheaper Than You Think

Full fine-tuning of DeBERTa-v3-large requires 40GB+ VRAM and costs $50+ per run. You need an A100 or H100. It is slow, expensive, and wasteful for most use cases.

LoRA (Low-Rank Adaptation) changes the equation entirely:

# LoRA config — training 1.8% of parameters
lora_config = LoraConfig(
    r=16,                    # rank
    lora_alpha=32,           # scaling factor
    target_modules=["query", "value"],  # attention layers only
    lora_dropout=0.1,
    bias="none",
    task_type="SEQ_CLS"      # sequence classification
)

VRAM: 12GB (fits on RTX 3090 or A4000)
Training time: 15 minutes on 2,400 texts
Cost: $0.15 per run
Adapter size: 24MB (vs 1.3GB full model)
Accuracy: AUROC 0.9948, TPR@5%FPR 96.75% — matching or exceeding full fine-tune

You are training 1.8% of the model’s parameters. The rest stay frozen. The LoRA adapter is a small file you can version, A/B test, and swap without redeploying the base model.

This is the actual moat. Anyone can wrap the GPT-4 API. Not everyone has their own fine-tuned model with their own training data running on their own infrastructure. The adapter file is yours. The weights are yours. No API provider can pull the rug.

Real Cost Breakdown

Operation	Cost	Time
Training run (LoRA, RTX 3090)	$0.15	15 min
Benchmark run (RAID, 1838 texts)	$0.19	20 min
Inference, per active hour	$0.17	ongoing
Monthly at 1K DAU (scale-to-zero)	$15-25	—
Monthly at 5K DAU (2 workers avg)	$60-80	—

Compare this to API-based alternatives for the same volume:

Provider	Monthly cost at 1K DAU
OpenAI GPT-4	$200-500
Anthropic Claude	$150-400
Self-hosted (RunPod Serverless)	$15-25

The 10x cost difference is not a rounding error. It is the difference between a sustainable solo business and one that needs funding to cover API bills.

Lessons Learned (Hard-Won)

torch version must match CUDA driver. cu118 vs cu121 vs cu130 — mixing these produces silent failures or cryptic segfaults. Check nvidia-smi output and install the matching torch wheel. Every time.
Never delegate GPU ops to background agents. I learned this the expensive way. An AI agent created a RunPod pod, started a training job, hit an error, and moved on to the next task — leaving the pod running. Always run GPU operations in the foreground where you can see the terminate command execute.
Network Volumes lock you to one datacenter. If EU-RO-1 has no A4000 availability, your volume sitting in EU-RO-1 is useless. For training artifacts, SCP the files out instead of relying on persistent volumes.
SSH needs --public-ip on RunPod. Not obvious from the docs. Without it, you get a web terminal only. The CLI flag is --public-ip when creating pods via runpodctl.
Cold start matters for real users. A 30-second cold start is fine for batch processing. For a user clicking “Detect” and waiting, it is unacceptable. Pre-loaded model volumes cut this to 5 seconds. Minimum active workers of 1 eliminates it entirely (at $0.17/hour baseline cost — about $4/day).
Batch size is the throughput lever. Going from batch=1 to batch=8 took inference from 12 req/sec to 90 req/sec on the same A4000. No code change beyond the handler’s batch logic. Profile this before scaling horizontally.
LoRA adapters are version-controllable. At 24MB each, you can store dozens in git LFS or a cloud bucket. Roll back a bad fine-tune in seconds. Try doing that with a 70B full fine-tune.

The Real Gap Is Smaller Than You Think

The distance between “I have a Jupyter notebook that classifies text” and “I have a production ML service handling thousands of requests” is not as large as the industry makes it seem. The tools exist. RunPod Serverless handles auto-scaling. LoRA handles efficient training. Scale-to-zero handles cost.

The moat is real. Anyone can call GPT-4. But not everyone has a fine-tuned DeBERTa model that achieves 0.9948 AUROC, runs on $0.17/hour GPUs, processes 90 requests per second, and stores zero user data. That combination of accuracy, cost, speed, and privacy is something you can only get by owning your own model on your own infrastructure.

The total investment to get here: about $5 in GPU time, a weekend of scripting, and the willingness to SSH into machines and read CUDA error messages. No VC check required.