Reading
Hippo is a Python toolkit that integrates local large language model (LLM) inference and document retrieval into a single installation package. It supports pipeline parallelism to split models across multiple devices, has built-in hybrid search (BM25 + semantic), and requires no additional installation of vector databases like ChromaDB.
Section 01
Hippo is a Python toolkit that integrates local LLM inference and document retrieval. It supports pipeline parallelism to split models across multiple devices, has built-in BM25 + semantic hybrid search, requires no additional vector databases, can be installed via pip install hippo-llm, and enables running 30B models on consumer hardware.
Section 02
Running LLMs locally and implementing RAG traditionally requires deploying multiple independent services (e.g., Ollama for inference, ChromaDB/Pinecone for vector storage). This fragmentation increases deployment complexity and maintenance costs.
Section 03
Hippo uses a lightweight pipeline parallelism approach:
Section 04
Hippo's VectorStore is implemented based on SQLite and supports:
Section 05
Section 06
/v1/chat/completions endpoint for seamless integration with LangChain and LlamaIndex;Section 07
| Configuration | Model | Speed |
|---|---|---|
| Mac Mini M2 (16GB) | Qwen3-4B-Q4 | 41 tok/s |
| RTX 5060 Ti (16GB) | Qwen3-14B-Q4 | 41 tok/s |
| 2× Mac Mini (16GB each) | Qwen3-30B-A3B-Q3 | 78 tok/s |
| Mac Mini M2 (16GB) | Qwen3-30B-A3B-Q3 | 24 tok/s |
| The multi-machine collaboration mode provides an alternative for users with limited budgets to access large model capabilities. |
Section 08
Current version is v0.3, supporting ANN indexing (suitable for collections of over 10,000 documents); the roadmap will include features like multi-sharding (over 2 devices), automatic layer balancing, and cross-shard speculative decoding. The project is open-source under the MIT license, depends on Python 3.10+ and a local Ollama service to obtain model weights, and is a practical solution to simplify local LLM deployment.
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