What GPU setup should I use for a small ML team?

It depends mostly on what kind of ML work your team does, but for a small team, the best default is usually:

Good general recommendation

1 shared GPU server + optional cloud burst

• 1 server with 2–4 GPUs
• Use it for:
  • model prototyping
  • fine-tuning
  • small/medium training jobs
  • inference/testing
• Add cloud GPUs only when you need occasional extra capacity

This is usually the best balance of cost, simplicity, and utilization.

What to choose by team size / workload

If your team mostly does:

1) Classical ML / light deep learning

• 1 GPU server with 1–2 GPUs
• Example GPUs:
  • NVIDIA RTX 4090 for budget-friendly local training
  • NVIDIA L4 if you care more about inference efficiency
• Good for:
  • experimentation
  • moderate training
  • development environments

2) LLM fine-tuning / CV / more serious training

• 1 server with 2–4 high-memory GPUs
• Better GPU options:
  • NVIDIA A100 80GB if budget allows
  • NVIDIA H100 if you need top performance
  • RTX 6000 Ada if you want workstation-ish flexibility with more VRAM than consumer cards
• Why:
  • VRAM matters more than raw speed for many ML tasks
  • multiple GPUs help with parallel experiments and bigger models

3) Mostly inference / serving

• 1–2 GPUs optimized for inference
• Examples:
  • L4
  • A10
  • L40S
• These are often more cost-effective than training-focused cards

Practical “small team” setup I’d recommend

If you have 3–8 people and a mix of experimentation + fine-tuning:

Best balanced on-prem setup

• 1 GPU server
• 4× NVIDIA L40S or 2× A100 80GB
• 256–512 GB RAM
• Fast NVMe storage
• 10/25 GbE networking

Lower-budget setup

• 1 server with 2× RTX 4090
• Great for prototyping and smaller training jobs
• Not ideal if you need lots of VRAM, ECC, or enterprise support

More enterprise / higher budget

• 1 server with 2–4× A100/H100
• Best if model size and turnaround time matter a lot

Decision rule

Choose based on the biggest bottleneck:

• Need cheapest option → RTX 4090
• Need lots of VRAM → A100 80GB / L40S / RTX 6000 Ada
• Need fast inference → L4 / L40S
• Need large-scale training → A100/H100
• Need flexibility for multiple users → 2–4 GPUs in one shared server

My default advice

For a small ML team, start with:

• one shared 4-GPU server
• preferably L40S or A100-class if budget allows
• otherwise 2× RTX 4090
• plus cloud GPUs for overflow

If you want, I can give you a recommendation tailored to:

1. team size,
2. budget,
3. whether you train LLMs/CV models, and
4. whether you want on-prem or cloud.