---
title: What small open source models can handle real AI agent tasks?
description: "Small open-source models in the 100M to 1B parameter range already handle most of the inference an agent runs around its main LLM: embeddings, reranking, and more."
canonical_url: https://superlinked.com/blog/small-open-source-models-for-ai-agent-tasks
last_updated: 2026-06-16
---

Small open-source models in the 100M to 1B parameter range already handle most of the inference an agent runs around its main LLM: embeddings, reranking, entity extraction, OCR, and multimodal search.

They fit on a single GPU and rival paid APIs on these tasks.

The Superlinked Inference Engine (SIE) ships 85+ of them pre-configured, each quality-verified against MTEB in CI, *so any one is a single call away: [github.com/superlinked/sie](https://github.com/superlinked/sie)*.

The work that scales with agent usage is rarely generation.

It is the repetitive, high-volume inference: embedding every chunk, reranking every retrieval, extracting fields from every document.

Those are exactly the jobs small specialized models do well.

Below is a working shortlist by task, with the model identifiers you pass to SIE.

<BlogSieCta />

### Retrieval and embeddings

- **Stella v5** (`NovaSearch/stella_en_400M_v5`): a 400M dense encoder, strong general-purpose embeddings for semantic search and RAG.
- **BGE-M3** (`BAAI/bge-m3`): dense, sparse, and multi-vector output from one checkpoint, useful for hybrid retrieval without running three models.
- **all-MiniLM-L6-v2** (`sentence-transformers/all-MiniLM-L6-v2`): small and fast, comfortable on CPU for local or low-volume work.

```python
from sie_sdk import SIEClient
from sie_sdk.types import Item

client = SIEClient("http://localhost:8080")
print(client.encode("NovaSearch/stella_en_400M_v5", Item(text="Hello world"))["dense"].shape)
# (1024,)
```

### Reranking

- **BGE-reranker-v2-m3** (`BAAI/bge-reranker-v2-m3`): a cross-encoder that scores query and document pairs directly, lifting precision before context reaches the LLM. Call it with `score`.

### Extraction and structured fields

- **GLiNER** (`urchade/gliner_multi-v2.1`): zero-shot named-entity recognition. You pass the labels you want at query time, with no training data, which suits agents that pull fields from arbitrary text. Call it with `extract`.

### Documents, OCR, and vision

- **Florence-2**: compact vision model for OCR, captioning, and detection, for agents that read PDFs and scans.
- **SigLIP**: image and text embeddings for multimodal search.
- **ColQwen2.5**: multi-vector, ColBERT-style retrieval over visual documents.

### Why not one server per model?

Because a single agent turn might chain four of these, and the classic pattern gives each its own GPU pool. SIE packs many onto a shared GPU with on-demand loading and least-recently-used eviction. An L4 with 24GB keeps two to three standard models hot at once, while all 85+ stay reachable at query time. Trying a newer open-weight model is a one-line identifier change, never a new deployment.

## FAQ: choosing and running small models

**Are these accurate enough to replace a large model for these tasks?** For embeddings, reranking, and extraction, yes. These are mature open-weight categories, and SIE checks each supported model against MTEB quality targets in CI rather than asking you to take accuracy on faith.

**What GPU do I need to run a few at once?** An L4 with 24GB holds two to three standard models hot simultaneously. The rest of the catalog loads on demand and evicts the least-recently-used model under memory pressure, so VRAM bounds concurrency, not catalog size.

**How do I pick between two encoders for the same task?** Benchmark them on your own data with the same SIE call and compare. The model selection guide at [/docs/choosing](/docs/choosing) walks through the tradeoffs.

**Can I serve my own fine-tuned small model, not just the catalog?** Yes. Register it against the running cluster through the config service and call it by identifier like any other model.

Browse the full catalog at [/models](/models), or *clone the engine and call your first model in two minutes: [github.com/superlinked/sie](https://github.com/superlinked/sie)*.