H200 GPU Droplet Comparison and Costs | Generated by AI
Question: Compare these two DigitalOcean GPU droplet options (H200 x8 vs H200 single GPU) and explain the differences and which is more cost-efficient.
Answer:
1. Raw Specs Comparison
| Feature | H200 ×8 | H200 ×1 |
|---|---|---|
| GPUs | 8 × H200 | 1 × H200 |
| VRAM | 1.1 TB | 141 GB |
| vCPU | 192 | 24 |
| RAM | 1920 GB | 240 GB |
| Boot Disk | 2 TB NVMe | 720 GB NVMe |
| Scratch Disk | 40 TB NVMe | 5 TB NVMe |
| Price | $3.44/GPU/hr | $3.44/GPU/hr |
| Total Price | $27.52/hr | $3.44/hr |
2. Per-GPU Value (Important Insight)
Both have exactly the same price per GPU:
- $3.44 / GPU / hour
- Same CPU per GPU: 24 vCPU
- Same RAM per GPU: 240 GB
- Same disk ratio per GPU
So H200×8 = just 8× H200 bundled together.
There is no bulk discount.
3. When to Choose H200 ×1
Use single GPU if:
- Testing models
- Fine-tuning (LoRA / QLoRA)
- Small-medium LLM (7B–70B quantized)
- Inference server
- Budget sensitive
- You don’t need multi-GPU training
This is most cost-efficient for general usage.
4. When to Choose H200 ×8
Use 8 GPUs only if you need:
- Training large LLM (70B–400B)
- Multi-GPU distributed training
- Tensor parallel / pipeline parallel
- High throughput inference
- Huge batch size
- Full precision model loading
Example workloads:
- Llama-3 405B training
- Mixtral full FP16 multi-node
- RLHF training
- Multi-tenant inference
5. VRAM Perspective
Single H200:
- 141 GB VRAM
-
Enough for:
- 70B FP8
- 34B FP16
- 13B FP32
- 405B quantized (sharded CPU offload)
H200 ×8:
- 1.1 TB VRAM total
-
Can run:
- 405B FP16
- multi-model serving
- huge batch inference
6. Cost Reality
Per hour:
- H200 ×1 → $3.44
- H200 ×8 → $27.52
Per day:
- H200 ×1 → $82.56/day
- H200 ×8 → $660.48/day
Per month (24/7):
- H200 ×1 → ~$2,476
- H200 ×8 → ~$19,814
This is why people say H200 is expensive.
7. Which One You Should Pick
Most developers should pick:
→ H200 ×1
Only pick ×8 if you explicitly need:
- distributed training
-
141GB VRAM
- multi-GPU scaling
Otherwise you’re wasting 8× money.
Simple Rule
- experimenting → H200 ×1
- inference server → H200 ×1
- fine-tuning → H200 ×1
- training huge LLM → H200 ×8
The two options are linear scale, not different tiers.