Bringing AI on-prem: How to use local models in LangChain
Bringing AI on-premises empowers you with enhanced security, reduced costs, and greater independence. This guide explains how to practically go about it.
Jan 30, 2025 • 9 Minute Read
For organizations prioritizing data security or aiming to reduce cloud dependencies, running local models can be a game-changer. Hosting AI solutions on-premises ensures sensitive information remains in-house while eliminating reliance on external APIs.
This guide explores how to load and configure local language models within LangChain, addresses challenges such as memory constraints and hardware acceleration, and provides best practices for optimizing inference. By the end, technical leaders will have the tools and knowledge to implement private, efficient AI solutions using LangChain.
Why bring AI on-prem?
Running AI models on-premises offers several advantages:
- Data Security: Keeping data within your infrastructure ensures compliance with stringent regulations (e.g., GDPR, HIPAA) and mitigates risks of data leaks from cloud providers.
- Reduced Costs: For high-frequency inference tasks, local models can avoid recurring API usage costs.
- Independence: Avoid vendor lock-in and maintain control over your infrastructure.
However, there are challenges:
- Hardware Requirements: Running state-of-the-art models like GPT-3 or GPT-4 locally requires powerful GPUs or TPUs.
- Setup Complexity: Hosting models on-premises involves managing dependencies, hardware acceleration, and memory optimization.
- Updates and Maintenance: Unlike cloud providers that continuously update models, local deployments require manual upgrades.
Options for running local models with LangChain
LangChain provides a modular framework for integrating AI models, making it a strong choice for on-premise deployments. Below are common options for running local models:
1. Hugging Face Transformers
Hugging Face offers a vast library of open-source models, ranging from smaller, efficient models (e.g., DistilBERT) to larger models like BLOOM or LLaMA. These models can be downloaded and run locally.
Pros:
- Open-source and highly customizable.
- Vast community support and documentation.
Cons:
Larger models may require high-end GPUs and careful memory management.
2. OpenAI-Compatible Open Source Models
Projects like GPT-J, GPT-NeoX, and Falcon provide high-quality, OpenAI-compatible alternatives for local deployment.
Pros:
- Performance comparable to GPT-3 for many use cases.
- Lower costs than cloud-hosted GPT APIs.
Cons:
Setup complexity increases for fine-tuning and hardware optimization.
3. Specialized Inference Engines
Libraries like DeepSpeed and ONNX Runtime are designed for optimizing large model inference on local hardware.
Pros:
- Drastically improve inference speeds with hardware acceleration.
- Reduce memory usage with model parallelism.
Cons:
Require additional expertise to configure effectively.
Step-by-step tutorial: Running local models with LangChain
This tutorial will guide you through setting up LangChain with a local Hugging Face model for private inference.
1. Install required libraries
First, install LangChain, Hugging Face Transformers, and any additional tools for running local models:
pip install langchain transformers accelerate sentencepiece
LangChain: Framework for building LLM-powered applications.
Transformers: Library for accessing pre-trained models.
Accelerate: Optimizes multi-GPU/TPU inference.
2. Download and configure the model
Choose a lightweight model for this example, such as distilGPT-2:
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load tokenizer and model
model_name = "distilgpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
print(f"Model '{model_name}' loaded successfully!")
For more powerful models, consider using Falcon or LLaMA:
model_name = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
3. Integrate with LangChain
LangChain’s LLM wrappers make it easy to interface with local models. Use the HuggingFacePipeline integration:
from langchain.llms import HuggingFacePipeline
from transformers import pipeline
# Create a text generation pipeline
text_gen_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
# Wrap the pipeline in a LangChain LLM
llm = HuggingFacePipeline(pipeline=text_gen_pipeline)
# Test LangChain integration
prompt = "Explain the importance of data security in AI."
print(llm(prompt))
4. Address Memory Constraints
Running larger models locally requires managing memory effectively:
- Use quantized models to reduce precision and save memory:
pip install bitsandbytes
from transformers import BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb_config, device_map="auto")
- Enable offloading to CPUs or disk for larger models:
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", offload_folder="offload_dir")
5. Optimize Inference
Use libraries like Accelerate or DeepSpeed to optimize inference performance:
from accelerate import infer_auto_device_map
device_map = infer_auto_device_map(model)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map=device_map)
Additionally, use ONNX Runtime to convert the model for optimized inference:
pip install onnxruntime
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, OnnxConfig
# Export to ONNX
onnx_config = OnnxConfig(model.config)
model.save_pretrained("onnx_model", onnx_config=onnx_config)
6. Deploy Locally
Create a local REST API for inference using Flask:
pip install flask
from flask import Flask, request, jsonify
app = Flask(__name__)
@app.route('/generate', methods=['POST'])
def generate():
data = request.json
prompt = data['prompt']
response = llm(prompt)
return jsonify({'response': response})
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5000)
Best Practices for On-Prem AI Solutions
1. Hardware acceleration
- Use GPUs with sufficient VRAM (e.g., NVIDIA A100, RTX 3090) for large models.
- Leverage TPUs if available for faster inference.
2. Model selection
- For resource-constrained environments, choose smaller models like distilGPT-2 or MiniLM.
- For complex applications, use larger models like Falcon-7B or GPT-NeoX with optimization techniques.
3. Regular updates
- Monitor new model releases and updates from Hugging Face or other repositories to stay current.
4. Monitoring and logging
- Implement monitoring to track model performance, latency, and memory usage.
5. Compliance and security
- Regularly audit your infrastructure to ensure compliance with regulations like GDPR or HIPAA.
Advantages of on-premises AI
1. Complete control over data and infrastructure
On-premises deployments provide full control over where and how data is stored, processed, and accessed. This is particularly valuable for organizations handling sensitive information, such as financial records or healthcare data.
Example: A healthcare provider running AI on-premises ensures that sensitive patient data is never exposed to third-party cloud providers, reducing the risk of data breaches.
Why it matters:
- Helps comply with strict data sovereignty laws (e.g., GDPR, HIPAA).
- Allows for more customized security measures, tailored to specific organizational needs.
2. Avoid recurring costs
Running models locally eliminates the need for recurring API costs from cloud-based services, which can become significant for high-frequency inference tasks.
Example: A call center using a locally deployed language model for real-time transcription saves on API costs compared to using OpenAI's GPT-4 API for millions of monthly queries.
Why it matters:
- Predictable, upfront hardware costs replace variable cloud billing.
- Especially advantageous for businesses with consistent or high-volume usage.
3. Independence from cloud providers
Deploying locally eliminates vendor lock-in, allowing organizations to retain flexibility and control over their AI strategy.
Example: A manufacturing company using local models can switch tools or frameworks without relying on the infrastructure of a single cloud vendor like AWS or Azure.
Why it matters:
- Prevents dependence on specific providers, reducing migration complexity in the future.
- Avoids service outages or pricing changes dictated by external vendors.
4. Enhanced privacy and security
Local deployment minimizes the risk of data exposure through third-party APIs or servers, an essential requirement for industries with stringent privacy standards.
Example: Government agencies running AI models locally can ensure that sensitive national data never leaves their secure networks.
Why it matters:
- Enables compliance with security audits and certifications.
- Reduces exposure to supply chain attacks targeting external API providers.
5. Performance consistency
On-premises deployments can achieve predictable performance without being affected by internet latency or cloud provider downtimes.
Example: A stock trading firm using AI for real-time predictions benefits from the low-latency and uninterrupted operation of on-premises infrastructure.
Why it matters:
- Guarantees stable performance for mission-critical systems.
- Eliminates the dependency on network connectivity for AI inference.
Trade-offs of on-premises AI
1. High initial costs
Setting up an on-premises infrastructure involves significant capital expenditure (CAPEX) for hardware, software, and setup.
Example: Deploying a single NVIDIA A100 GPU, which is highly suitable for large language models, can cost upwards of $10,000.
Why it’s a challenge:
- The upfront investment can be prohibitive for small businesses or startups.
- May require significant budget planning and long-term ROI calculations.
2. Requires technical expertise
Running AI models locally demands expertise in model optimization, hardware acceleration, and system maintenance.
Example: Fine-tuning a large model like Falcon-7B locally requires knowledge of tools like DeepSpeed and memory management strategies like offloading.
Why it’s a challenge:
- Organizations without skilled AI engineers may struggle to maintain optimal performance.
- Troubleshooting hardware issues or software dependencies can slow development cycles.
3. Maintenance Overhead
Unlike cloud-hosted solutions that are managed by vendors, on-premises systems require regular maintenance, including software updates, hardware repairs, and dependency management.
Example: A team deploying Hugging Face models locally must manually update models or libraries to incorporate improvements or bug fixes.
Why it’s a challenge:
- Ongoing maintenance adds to operational costs.
- Regular downtime for updates or hardware failures can disrupt workflows.
4. Scalability constraints
Scaling on-premises infrastructure to handle additional workloads or larger models often requires purchasing more hardware, which is slower and costlier compared to cloud solutions.
Example: A research lab deploying GPT-J locally faces challenges scaling their infrastructure as new projects demand higher GPU throughput.
Why it’s a challenge:
- Expansion requires long lead times for procurement and setup.
- May not be cost-effective for workloads with highly variable demand.
5. Slower access to model updates
Cloud providers frequently update their AI models with performance improvements, bug fixes, and new features. On-premises deployments miss out on these automatic updates.
Example: Organizations using an older version of an open-source model locally might lag behind their cloud-based competitors using cutting-edge API models.
Why it’s a challenge:
- Keeping up-to-date requires manually downloading and implementing new versions.
- Missing updates can result in inferior performance compared to competitors relying on updated cloud services.
6. Energy consumption
Running large models locally, especially over long durations, consumes significant amounts of energy, which can lead to increased operational costs.
Example: A data center hosting a fine-tuned LLaMA-13B model might incur thousands of dollars in annual electricity costs.
Why it’s a challenge:
- Energy efficiency can become a bottleneck for organizations aiming for sustainability.
- Long-term operational costs can outweigh the initial savings from eliminating cloud dependencies.
How to Mitigate Trade-Offs
Start small
Experiment with lightweight models (e.g., DistilBERT, MiniLM) before scaling to larger systems.
Use hardware acceleration
Optimize inference with GPUs, TPUs, or libraries like ONNX Runtime or DeepSpeed.
Adopt hybrid models
Combine on-premises and cloud solutions, using the cloud for burst workloads while keeping sensitive tasks on-premises.
Monitor and optimize
Track energy usage, performance, and maintenance needs using tools like Prometheus or Grafana for proactive optimization.
By understanding the pros and cons and implementing mitigation strategies, organizations can confidently embrace on-premises AI to align with their goals and constraints.
Conclusion
Bring your AI on-premeses provides benefits such as enhanced security, reduced cost, and greater independence. By leveraging tools like LangChain and Hugging Face Transformers, it’s now easier than ever to implement local AI solutions.
While challenges like hardware requirements and optimization persist, adopting best practices ensures robust and scalable deployments. With this guide, technical leaders can confidently evaluate and deploy private, powerful AI systems tailored to their needs.
Further AI tutorials by this author
- What is RAG: Definition, use cases, and how to implement it
- LLMs: Transfer Learning with TensorFlow, Keras, Hugging Face
- Ethical AI: How to make an AI with ethical principles using RLHF
- How to Deploy an LLM for Production Use-Cases
- How to Create a GenAI Powered Real-Time Data Processing Solution
- Creating a large language model from scratch: A beginner's guide