I'm building a small LLM training stack and need help picking GPU infrastructure

Absolutely — I can help you choose GPU infrastructure for a small LLM training stack.

To recommend something useful, I’d want to know a few things:

1. Training scale

   • Are you doing fine-tuning (LoRA/QLoRA/full fine-tune) or training from scratch?
   • What model sizes are you targeting? (e.g. 7B, 13B, 70B)

2. Budget

   • Rough monthly budget or capex?
   • Are you okay with cloud, on-prem, or a mix?

3. Throughput needs

   • Is this for research / experimentation, or do you need consistent production-ish training runs?
   • How many training jobs run concurrently?

4. Preferred stack

   • PyTorch + Hugging Face?
   • Distributed training framework like FSDP, DeepSpeed, or Megatron-LM?

5. Operational constraints

   • Any need for low startup time, data residency, or air-gapped environments?
   • Do you already have networking/storage planned?

Quick rule of thumb

• 1–2 GPUs: great for LoRA/QLoRA on 7B–13B models
• 4–8 GPUs: practical for full fine-tuning and larger experiments
• 16+ GPUs: start thinking about high-speed interconnects, NCCL tuning, shared storage, and scheduler/orchestration

Common GPU choices

• NVIDIA L40S: strong price/perf for training and inference, good default for small stacks
• NVIDIA A100 80GB: excellent if you need large VRAM and stable distributed training
• NVIDIA H100: best performance, usually best for serious multi-GPU training, but expensive
• RTX 4090: great budget option for local labs if you can handle consumer-GPU tradeoffs

Infrastructure recommendations by use case

• Cheap experimentation: 1–2× RTX 4090 or L40S
• Serious fine-tuning: 2–4× L40S or A100s
• Large-scale training: A100/H100 with NVLink/InfiniBand and proper parallel storage

If you answer these 4 questions, I can give you a concrete recommendation:

1. What model sizes?
2. Fine-tune vs train from scratch?
3. Cloud or on-prem?
4. Budget range?