How to Reduce GPU Cost by More Than 40% for ML Workloads

Source: Dev.to

TL;DR
A100 → H100 → H200 marks a major performance leap. Choose based on memory needs, compute demands, and cost per workload. A100s remain highly cost‑efficient for training and fine‑tuning, H100s deliver excellent throughput for inference, and H200’s 141 GB VRAM unlocks memory‑heavy and long‑context models. Aquanode — a multi‑cloud GPU marketplace that makes switching between these GPUs easy and cost‑effective.

The GPU landscape has changed more in two years

The GPU landscape has evolved rapidly, and 2025 brings the biggest gap in capability since the V100 era. As teams train and deploy larger models, the real question becomes which GPU offers the best cost‑performance for their workflow.

Matching GPU specs to your workload matters, but so does flexibility. Aquanode helps developers compare and deploy A100, H100, and H200 instances from multiple providers through one account.

A100 vs H100 vs H200: What actually matters

1. Memory Capacity

• A100: 40 GB or 80 GB
• H100: 80 GB
• H200: 141 GB

Memory has become the limiting factor for many LLM and multimodal workloads. Models that push beyond 80 GB benefit significantly from the H200. On Aquanode, teams choose H200s for long‑context LLMs, high‑concurrency inference, and larger batch sizes without micro‑batching.

2. Raw Compute and Architecture

Hopper GPUs (H100 and H200) bring transformer‑optimized kernels, FP8 acceleration, and higher throughput. This often results in two to four times faster training and even larger gains for inference. Many teams on Aquanode upgrade from A100s to H100s when production workloads demand more throughput.

3. Cost‑Performance

Hourly pricing is misleading; the real metric is cost per completed run. An H100 that finishes a job in a third of the time can be cheaper than an A100. An H200 that avoids sharding or reduces parallelism overhead can shorten epochs significantly.

Aquanode’s marketplace makes this easy to evaluate by showing side‑by‑side pricing across multiple cloud providers and enabling quick switching when prices shift.

So which GPU is best for your workload in 2025?

If you’re fine‑tuning models on a budget

• Pick: A100
• Fits in 40 GB or 80 GB
• No need for Hopper‑specific features
• Benefit from cheaper hourly pricing

A100s remain the price‑efficiency leader for small and mid‑sized teams.

If you’re training medium or large transformer models

• Pick: A100 or H100
  • Cost‑sensitive: A100
  • High throughput: H100

Unless your model exceeds 80 GB or needs very large batches, the A100 still offers unbeatable value.

If you’re training or serving LLMs with long context

• Pick: H200
• 141 GB VRAM, 128k+ token context
• Large mixture‑of‑experts, multimodal LLMs
• Inference servers handling many concurrent requests

If your model strains 80 GB or doesn’t fit at all, H200 is the natural upgrade.

If you’re running high‑volume inference

• Pick: H100 or H200
• Big batches, high throughput, FP8 acceleration
• Transformer‑engine optimizations

In 2025, Hopper‑based GPUs outperform A100s dramatically for inference workloads.

The underrated factor: Flexibility across providers

GPU pricing, availability, and regions vary widely across cloud platforms. Relying on a single provider can slow development or inflate costs.

Aquanode solves this by offering:

• One account for multiple cloud providers
• A unified dashboard for A100, H100, and H200
• Pause and resume features
• Easy provider switching
• Consistent pricing visibility across regions

In modern AI development, flexibility is as important as raw performance.

How to choose your GPU in under 60 seconds

Ask yourself:

1. Does your model fit in 80 GB?

   • No → H200
   • Yes → A100 or H100

2. Is cost your priority? → A100

3. Is speed your priority? → H100

4. Is your workload memory‑bound? → H200

5. Do you want to avoid cloud lock‑in?

   • Use Aquanode to switch providers easily

Final Thoughts

GPU choice now has a dramatic impact on training and inference velocity. The A100 remains a dependable workhorse, the H100 delivers unmatched throughput, and the H200 opens the door to long‑context and memory‑intensive models.

Aquanode enables teams to choose the right GPU for each stage of their workflow without being tied to a single cloud’s pricing or availability.