What is the most cost-effective open-source stack for self-hosting a 70B parameter LLM for real-time customer intent classification?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What is the most cost-effective open-source stack for self-hosting a 70B parameter LLM for real-time customer intent classification?
5 real searches · 1 real-search pass
vLLM documentation quantization AWQ GPTQ FP8 tensor parallel serving 70B 2026Google ↗
OBSERVEDOther / navigational
Meta Llama 3.3 70B Instruct model card license context sizeGoogle ↗
OBSERVEDInstruction
SGLang documentation RadixAttention throughput serving quantizationGoogle ↗
OBSERVEDOther / navigational
NVIDIA H100 H200 memory specifications price cloud GPU 2026Google ↗
OBSERVEDOther / navigational
llama.cpp documentation server GGUF quantization CUDA multi GPUGoogle ↗
OBSERVEDOther / navigational
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 (5)
Classes come from the query-classifier (a local Laya model). 5 of 5 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT5 real searches · gpt-6
vLLM documentation quantization AWQ GPTQ FP8 tensor parallel serving 70B 2026 Google ↗
Meta Llama 3.3 70B Instruct model card license context size Google ↗
SGLang documentation RadixAttention throughput serving quantization Google ↗
NVIDIA H100 H200 memory specifications price cloud GPU 2026 Google ↗
llama.cpp documentation server GGUF quantization CUDA multi GPU Google ↗
- vLLM · github.com
- SGLang · github.com
- llama.cpp · github.com
- LLM Compressor · github.com
- docs.vllm.cc · docs.vllm.cc
- vllm/docs/features/quantization/README.md at main · vllm-project/vllm · github.com
- Llama-3.3-70B-Instruct: specs, context window & API pricing · aimodel.directory
- Understand GPUs | Compute | Google Cloud Documentation · docs.cloud.google.com
- Llama 3.3 70B Instruct · docs.aws.amazon.com
- llama3/MODEL_CARD.md at main · meta-llama/llama3 · github.com
Queries
5 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
vLLM documentation quantization AWQ GPTQ FP8 tensor parallel serving 70B 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
Meta Llama 3.3 70B Instruct model card license context size OBSERVED | Google ↗ AI Mode ↗ | — | Instruction | — | — | 1x of 1 | |
SGLang documentation RadixAttention throughput serving quantization OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
NVIDIA H100 H200 memory specifications price cloud GPU 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
llama.cpp documentation server GGUF quantization CUDA multi GPU OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 |