What’s the best GPU setup for a startup with 2 ML engineers?

For a startup with 2 ML engineers, the “best” GPU setup usually means maximizing flexibility and minimizing operational pain, not buying the biggest box.

Best practical setup: cloud-first + 1 local workstation

1) Use cloud GPUs for most training

This is usually the best choice early on because:

• no upfront capex
• easy to scale up/down
• access to newer GPUs
• no maintenance, power, or cooling headaches

Good cloud options:

• AWS: g5/g6 for moderate workloads, p4/p5 for large training
• GCP: A2/A3 instances
• Azure: NC/ND series
• Specialized providers: Lambda, CoreWeave, RunPod, Paperspace (often cheaper/simpler)

If your team is small, cloud often beats owning hardware until GPU utilization is consistently high.

2) Buy 1 strong local workstation for dev + prototyping

A shared on-prem box is great for:

• quick experiments
• debugging
• small fine-tunes
• avoiding cloud latency/cost for iterative work

Recommended local setup:

• 1 GPU if budget is tight, 2 GPUs if both engineers work in parallel often
• GPU: NVIDIA RTX 4090 (best value) or RTX 6000 Ada (pro/VRAM/stability)
• RAM: 128 GB
• CPU: 16–32 cores
• Storage: 2–4 TB NVMe SSD
• PSU/cooling: high-quality, since the 4090 is power-hungry

If you want a single “buy-it-once” recommendation

Best value option

• 1x RTX 4090 workstation
• Keep heavier training in the cloud

This is usually the sweet spot for an early startup.

Best shared team option

• 2x RTX 4090 workstation if your workloads fit in 24 GB VRAM
• Or 2x RTX 6000 Ada if you need more VRAM and reliability

How to choose based on workload

Choose 4090s if:

• you do CV, smaller LLM fine-tunes, prototyping
• you care most about cost/performance
• 24 GB VRAM is enough most of the time

Choose RTX 6000 Ada / higher-VRAM GPUs if:

• you fine-tune larger LLMs
• you need more than 24 GB VRAM per GPU
• you want ECC/pro-grade cards and better multi-GPU reliability

What I’d avoid

• Buying lots of GPUs upfront before knowing utilization
• Consumer multi-GPU setups if you expect heavy distributed training
• A single giant server unless you already know you’ll keep GPUs busy

My default recommendation

For a 2-person ML startup:

• Cloud GPUs for training
• 1 local 4090 workstation for development
• Upgrade to 2 GPUs or a second workstation once utilization justifies it

If you tell me:

1. your budget,
2. whether you train LLMs or mostly CV/tabular models, and
3. whether you prefer cloud or on-prem,

I can suggest a very specific build.