What infrastructure choices matter most when scaling AI workloads?

General Discussion
1

As AI workloads move from experimentation to larger training and inference environments, infrastructure decisions can have a significant impact on performance, scalability, and operating costs. The broader Artificial Intelligence Market also covers a wide range of AI applications and technologies, which makes the underlying infrastructure question increasingly relevant.

For developers working with larger models, I’m interested in how you approach infrastructure selection when an AI workload starts growing.

For example, how do you decide between different GPU configurations for:

  • Model training
  • Fine-tuning
  • Inference
  • Batch processing
  • Development and testing

I’m also interested in the practical trade-offs between GPU availability, memory capacity, networking, storage, and cost.

For workloads that are not running continuously, do you generally prefer on-demand infrastructure, spot instances, or a combination of both?

And when scaling an AI workload, which factor tends to become the biggest bottleneck first: GPU capacity, memory, storage, networking, or software optimization?

I’d be interested in hearing how other developers approach these decisions, particularly for LLM training and inference workloads.

2

Interesting question. From my experience, once an AI workload starts scaling, infrastructure selection becomes less about choosing the “fastest GPU” and more about selecting the right execution path for the workload.

I tend to look at it differently by workload:

Training: memory capacity/bandwidth, interconnect and scaling efficiency become critical very quickly.
Fine-tuning: memory headroom, checkpoint/data movement and reproducibility often matter more than peak compute.
Inference: model load time, TTFT, sustained throughput, KV-cache pressure, concurrency and runtime stability become more useful than a single tokens/sec number.
Batch workloads: utilization and cost efficiency usually dominate.
Development/testing: availability, fast provisioning and reproducibility matter more than maximum performance.

For intermittent work I generally prefer on-demand infrastructure for validation, controlled experiments and evidence-sensitive runs. Spot can be very attractive for fault-tolerant workloads that can safely restart, but I would avoid making a critical one-shot validation dependent on preemptible capacity.

One lesson from our governance-runtime work is that the bottleneck also tends to move as the infrastructure improves. First it may be GPU memory or model loading; later it can become networking/topology, scheduler placement, provider stability, storage/data movement, or the software/orchestration layer itself.

So I would not ask only:

“Which GPU is fastest?”

I would ask:

“Which infrastructure gives this workload the most reliable, reproducible and economically sensible execution path?”

For regulated or mission-critical AI, I would add one more dimension: the infrastructure also needs to support the evidence and control requirements around execution, not only the inference itself.

We recently moved part of our own governance-runtime validation work onto Verda’s Blackwell/B300 environment, so this trade-off between raw capability and full execution-path reliability is very relevant to what we are testing now.