Prompt
how many H100s do I need
Latest observation
It depends on what you want to do with them.
A good way to think about it:
- 1–2 H100s: prototyping, inference, fine-tuning smaller models
- 4–8 H100s: serious fine-tuning, larger inference workloads, smaller training runs
- 16–64+ H100s: training large models from scratch or heavy distributed training
- Hundreds to thousands: frontier-scale training
If you tell me:
- Training or inference
- Model size (e.g. 7B, 70B, 175B)
- Batch size / latency target
- Whether you’re using FP16, BF16, or quantization
- Your budget
…I can estimate the number of H100s much more precisely.
Brands
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
How can brands improve their AEO?
Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.
Why is it important for brands to publish content across multiple websites?
Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.
Want this interpreted for your brand?
Explorer is the free public corpus. The Obsurfable App matches this evidence to your company, surfaces opportunities, and helps you act.