What is Search Agent 8B?

An open-source 8-billion-parameter model that behaves like a research detective, breaking questions into many local search queries.

Yes, for inference you’ll need at least 24 GB VRAM; an RTX A 6000 is a recommended baseline.

Can I use it with my own data?

Absolutely—replace the Wikipedia index with your corporate PDFs or knowledge base.

Is it safe to run on-premises?

Yes, all data stays locally; no external API calls are made.

Inference is cheap—$0.10/hour on an RTX A 6000—and training can be done on rented GPUs, avoiding paid API fees.

Does it support multi-turn conversations?

Yes, the model handles up to 15 tool calls per turn, with evidence verification at each step.

Where can I download the weights?

The entire repo is on Hugging Face: [SearchAgent-8B-hq — Hugging Face (2026)](https://huggingface.co/aidenjhwu/SearchAgent-8B-hq)

Published: December 28, 2025 · Last updated: January 30, 2026

By Claudiu RAVEICA - Systems Performance Engineer. Expertise: LLM retrieval and evaluation, SEO measurement (GSC/GA), Distributed systems performance

Search Agent 8B: Zero-Cost Deep Research With Open-Source AI

TL;DR

8-billion-parameter model that turns every question into 10–15 local searches.
Trained on >3 million queries—saved me about $1,500 in API fees.
Runs on a single RTX A 6000 GPU (48 GB VRAM) with 15.2 GiB RAM.
Uses a detective-style workflow: gather evidence, verify, answer.
All weights, code, and training scripts are free on Hugging Face.

Table of Contents

Why This Matters

I’ve spent countless hours chasing the next best research answer only to hit slow API responses, unreliable data, or a steep price tag. The core pain points—expensive paid search APIs, latency, and data privacy—make it hard to build robust research agents, especially in environments under sanctions or with tight budgets. Search Agent 8B tackles these problems head-on: it’s an open-source model that performs deep, evidence-based queries against a local database, removing the need for external APIs entirely. That means you can keep your data on-premises, avoid latency spikes, and never pay for a single query again.

Core Concepts

Search Agent 8B is built on the Qwen3-8B foundation but is fine-tuned to act like a detective rather than a chatbot.

Problem decomposition: the model first breaks a complex question into 10–15 sub-queries.
Local search: each sub-query hits a local retrieval server (Wikipedia, company docs, or custom PDFs) instead of the public web.
Evidence collection: the agent gathers snippets from each result.
Verification: it cross-checks evidence across sources to spot contradictions.
Synthesis: the final answer is a concise, verified summary.

This detective workflow is powered by a reinforcement-learning training loop that uses outlier suppression to kill off bad trajectories—anything that repeats a query or produces malformed tool calls gets a zero reward, keeping the policy focused on clean, useful behavior. The strategy was introduced in Search Agent 8B’s HQ training and is expected to spread to other models by 2026 SearchAgent-8B — Free Deep Research Without the Bill (2026).

How to Apply It

Below is a step-by-step guide to get Search Agent 8B running on your own machine, complete with cost calculations and hardware notes.

Prerequisites
- NVIDIA RTX A 6000 (48 GB VRAM) or equivalent 24 GB GPU for inference.
- 15.2 GiB system RAM.
- Docker (recommended) or pip install torch==2.4.0 and pip install vllm.
Clone the repo
```
git clone https://huggingface.co/aidenjhwu/SearchAgent-8B-hq
cd SearchAgent-8B-hq
```
The weights and configuration live here SearchAgent-8B-hq — Hugging Face (2026).

Set up the local retrieval server

# Download a free Wikipedia dump
wget https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-abstract.xml.bz2
# Convert to FAISS index (you can use the repo’s helper script)
python build_index.py --source enwiki-latest-abstract.xml.bz2

The script pulls the latest offline data, so you’re never dependent on live internet.

Run the agent with vLLM
```
vllm serve . --model-type qwen3 --max_seq_len 8192
```
vLLM handles batching and memory efficiently; it’s the engine behind the fast responses.
Test a query
```
python demo.py --prompt "What was the amount of cyber-security investment in 2024 for the US defense budget?"
```
The model will break the request into 12 sub-queries, fetch evidence, and produce a verified answer that matches the $67 M figure in the PDF we used during training.
Cost analysis
- Training: 300 iterations × 1,000 runs × 10 queries ≈ 3 million offline queries.
- If paid API at $0.50/1k queries, that’s about $1,500 in fees.
- Local server uses no API credits—your only cost is the GPU.
- Running inference on a single RTX A 6000 costs roughly $0.10/hour, compared to $5/hour on a paid API endpoint.

Pitfalls & Edge Cases

Issue	Why it matters	Mitigation
GPU memory	8B models still need 48 GB VRAM for full-batch inference.	Use 16-bit precision (BF16) and enable –max_seq_len tuning.
Local data freshness	Offline corpora can become stale.	Schedule nightly updates to your Wikipedia dump or sync with internal docs.
Outlier suppression bugs	Over-aggressive zero-reward can kill valid multi-turn strategies.	Fine-tune the suppression threshold on a small validation set.
Tool call mis-format	The agent may issue malformed search calls.	Wrap the retrieval engine in a validation layer that checks query syntax.
Limited compute for training	300 iterations on 16 GPUs still takes ~5 days.	Use a cloud GPU rental (e.g., Massed Compute) for scaling.

Open Questions

How do we keep the local knowledge base up to date without retraining?
Continuous incremental updates to the FAISS index can keep the data fresh.
Can the same training approach work with other LLMs?
The outlier suppression strategy is generic and should translate to models in 2026.
What security measures are needed for internal data?
Use encrypted disk storage and access control for the retrieval server.

Quick FAQ

What is Search Agent 8B?
An open-source 8-billion-parameter model that behaves like a research detective, breaking questions into many local search queries.
Do I need a GPU?
Yes, for inference you’ll need at least 24 GB VRAM; an RTX A 6000 is a recommended baseline.
Can I use it with my own data?
Absolutely—replace the Wikipedia index with your corporate PDFs or knowledge base.
Is it safe to run on-premises?
Yes, all data stays locally; no external API calls are made.
What about cost?
Inference is cheap—$0.10/hour on an RTX A 6000—and training can be done on rented GPUs, avoiding paid API fees.
Does it support multi-turn conversations?
Yes, the model handles up to 15 tool calls per turn, with evidence verification at each step.
Where can I download the weights?
The entire repo is on Hugging Face: SearchAgent-8B-hq — Hugging Face (2026).

Conclusion

If you’re a researcher, ML engineer, or product manager who needs a reliable, cost-effective research agent, Search Agent 8B is a game-changer. Download it, plug in your own data, and enjoy a zero-API, on-premises solution that saves you money and gives you control. If you lack a powerful GPU or your use case doesn’t involve complex, evidence-based queries, you might prefer a smaller, off-the-shelf chatbot. Either way, Search Agent 8B demonstrates how open-source tooling can level the playing field for deep research.