Deploying Local LLMs like LLaMA using Triton Inference Server

Large Language Models (LLMs), such as Meta’s LLaMA, are increasingly being deployed locally to avoid external dependencies, reduce latency, and ensure data privacy. Triton Inference Server, an open-source solution developed by NVIDIA, offers a highly efficient way to serve LLMs. This blog will guide you through deploying LLaMA models using Triton Inference Server, building an inference layer on AWS, and provide a Python streaming example using LangChain.

1. Introduction to Triton Inference Server

Origin and Purpose

Triton Inference Server, developed by NVIDIA, was designed to simplify the deployment of machine learning models in production environments. Originally focused on GPU-based inference, it now supports CPUs as well and caters to various types of models, including LLMs.

Key Features:

• Multi-framework Support: Triton supports PyTorch, TensorFlow, ONNX, TensorRT, and Hugging Face Transformers.
• Concurrent Model Execution: Serve multiple models or different versions of a model concurrently.
• Dynamic Batching: Automatically batches requests to improve throughput.
• Model Monitoring: Real-time metrics for memory, compute, and latency.

Open Source and Licensing

Triton is fully open-source and available under the Apache 2.0 license, making it highly adaptable for commercial use.

2. Building the Inference Layer on AWS

To deploy LLaMA or similar LLMs, you can leverage AWS’s cloud infrastructure for scalable and cost-effective hosting. Two primary options for this deployment are EC2 and EKS.

Option 1: EC2 (Elastic Compute Cloud)

EC2 instances provide flexible, scalable compute capacity.

Recommended Instances:

• g5.xlarge: Equipped with NVIDIA A10G GPUs, suitable for cost-effective LLM inference.
• p4d.24xlarge: High-performance instance with NVIDIA A100 GPUs for demanding LLM workloads.
• c7g.xlarge (Graviton-based): For CPU-only workloads.

Pros:

• Simpler setup compared to EKS.
• No container orchestration overhead.

Cons:

• Not as scalable as EKS.

Option 2: EKS (Elastic Kubernetes Service)

EKS allows you to deploy Triton in a Kubernetes cluster, enabling auto-scaling and better management of large workloads.

Recommended Node Types:

• g5.xlarge GPU nodes: Economical for GPU-based inference.
• c6g.large: Cost-efficient ARM-based nodes for lightweight processes.

Pros:

• Supports auto-scaling and container orchestration.
• Better suited for complex deployments.

Cons:

• Slightly steeper learning curve.

3. Preparing the LLaMA Model for Triton

1. Model Conversion: Ensure the LLaMA model is exported as ONNX or TensorRT format for optimal inference performance.
2. Configuration: Create a config.pbtxt file specifying model input/output dimensions, max batch size, and precision.
3. Deploy to Triton: Upload the model repository (model files and config) to the /models directory of the Triton Server.

4. Python Example: Streaming LLaMA Responses Using LangChain and Triton API

Streaming is an essential feature for efficient and user-friendly interactions with LLMs. Below is a Python example using LangChain to get streaming responses.

Installation

pip install tritonclient[all] langchain

Code Example

import tritonclient.grpc as grpcclient
from langchain.llms import LLMChain
from langchain.prompts import PromptTemplate

# Triton Client Connection
TRITON_URL = "<your_triton_server_url>:8001"
client = grpcclient.InferenceServerClient(url=TRITON_URL)

# LangChain Setup
template = "Answer the following question: {question}"
prompt = PromptTemplate(template=template, input_variables=["question"])

class TritonLLM:
    def __init__(self, client):
        self.client = client

    def stream_response(self, question):
        inputs = grpcclient.InferInput("INPUT_TEXT", [1], "BYTES")
        inputs.set_data_from_numpy(np.array([question.encode("utf-8")], dtype=object))

        outputs = grpcclient.InferRequestedOutput("OUTPUT_TEXT", class_count=1, binary_data=True)

        response = self.client.infer("llama_model", [inputs], outputs=[outputs])
        return response.as_numpy("OUTPUT_TEXT").item().decode("utf-8")

# Get streaming answer
llm = TritonLLM(client)
question = "What are the advantages of using Triton Inference Server?"
print("Response:")
for chunk in llm.stream_response(question):
    print(chunk, end="", flush=True)

Explanation:

• Triton Inference Server Client: Connects to the Triton server using gRPC.
• LangChain Prompt: Defines a simple question-answering template.
• Streaming: The stream_response function streams the model’s output in chunks for real-time response.

5. Best Practices for Local LLM Deployment

Optimizing Performance:

• Dynamic Batching: Enable Triton’s dynamic batching to improve GPU utilization.
• Quantization: Convert models to INT8 or FP16 precision to reduce memory usage.
• Concurrency Settings: Configure multiple instances of the model to handle concurrent requests.

Cost-Effectiveness:

• Instance Selection: Start with cost-effective instances like g5.xlarge and scale up based on throughput needs.
• Spot Instances: Use AWS spot instances for significant cost savings during non-critical workloads.

Scalability:

• Use EKS with Auto-scaling to automatically adjust compute resources based on demand.

Security:

• Restrict access to Triton via IAM policies and secure network configurations.
• Use HTTPS endpoints instead of plain gRPC for secure communication.

Conclusion

Deploying LLaMA and other LLMs using Triton Inference Server provides a robust and scalable inference solution. By leveraging AWS infrastructure, you can achieve high-performance and cost-effective deployment. Combining Triton with frameworks like LangChain makes it seamless to build and serve real-time LLM applications.