What hardware do I need to efficiently scale LLM inference infrastructure locally versus using a managed cloud provider like Together AI?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What hardware do I need to efficiently scale LLM inference infrastructure locally versus using a managed cloud provider like Together AI?
3 real searches · 1 real-search pass
Together AI inference pricing dedicated endpoints hardware GPU types 2026 officialGoogle ↗
OBSERVEDComparison
vLLM hardware requirements GPU memory tensor parallelism continuous batching documentationGoogle ↗
OBSERVEDInstruction
NVIDIA H200 H100 GPU memory bandwidth specifications officialGoogle ↗
OBSERVEDReason
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (3)
Classes come from the query-classifier (a local Laya model). 3 of 3 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT3 real searches · gpt-6
Together AI inference pricing dedicated endpoints hardware GPU types 2026 official Google ↗
vLLM hardware requirements GPU memory tensor parallelism continuous batching documentation Google ↗
NVIDIA H200 H100 GPU memory bandwidth specifications official Google ↗
- Components — NVIDIA HGX AI Factory (HGX H100, H200, B200) · docs.nvidia.com
- Dedicated Model Inference · together.ai
- Pricing · together.ai
- Optimization and Tuning · docs.vllm.ai
- docs.vllm.cc · docs.vllm.cc
- vLLM · huggingface.co
- vllm/docs/serving/parallelism_scaling.md at main · vllm-project/vllm · github.com
- How much will I be charged to deploy a model? · support.together.ai
Queries
3 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Together AI inference pricing dedicated endpoints hardware GPU types 2026 official OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 | |
vLLM hardware requirements GPU memory tensor parallelism continuous batching documentation OBSERVED | Google ↗ AI Mode ↗ | — | Instruction | — | — | 1x of 1 | |
NVIDIA H200 H100 GPU memory bandwidth specifications official OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |