Low-Latency Inference Caching for LLMs with ElastiCache

Question

Pulumi · Accepted Answer

To achieve low-latency inference caching for Large Language Models (LLMs) using AWS ElastiCache, you would need to create an ElastiCache Redis cluster which serves as an in-memory data store. The ElastiCache Redis cluster would provide fast read and write capabilities to cache the inference results, which can be retrieved quickly for subsequent requests, reducing the need to perform costly recomputation.

Below, I'm going to create a Pulumi program that provisions an ElastiCache cluster suitable for caching inference results. We will use the `aws.elasticache.Cluster` resource, which represents a Redis cluster. We'll configure the cluster with appropriate cache node type and engine version, and specify security groups to control access to the cluster.

Here's what each part of the program does:

1. **Imports and Initialization**: We begin by importing required Pulumi packages.
2. **Cluster Security Group**: We create an AWS security group for our ElastiCache cluster to ensure that only specific traffic can reach it.
3. **ElastiCache Subnet Group**: We create an ElastiCache subnet to determine which subnets in your VPC will contain the cache clusters.
4. **ElastiCache Cluster**: We define an ElastiCache cluster resource with the selected node type, cluster ID, number of nodes, etc. This is our Redis cache which will store inference results.
5. **Exports**: At the end of the program, we export the primary endpoint of the ElastiCache cluster. This is the address you'll use to write to and read from the cache.

Let's take a look at the Pulumi program in Python:

```python
import pulumi
import pulumi_aws as aws

# Create a VPC Security Group for the ElastiCache cluster to control access
elasticache_security_group = aws.ec2.SecurityGroup('elasticache-security-group',
    description='Enable Redis port',
    ingress=[
        {'from_port': 6379, 'to_port': 6379, 'protocol': 'tcp', 'cidr_blocks': ['0.0.0.0/0']}
    ])

# Create an ElastiCache Subnet Group
elasticache_subnet_group = aws.elasticache.SubnetGroup('elasticache-subnet-group',
    subnet_ids=[
        # Replace these with the actual subnet IDs of your VPC
        'subnet-xxxxxxxxxxxxxx',
        'subnet-yyyyyyyyyyyyyy'
    ])

# Configure the ElastiCache cluster
elasticache_cluster = aws.elasticache.Cluster('example-cluster',
    engine='redis',
    node_type='cache.m5.large',
    num_cache_nodes=1,
    parameter_group_name='default.redis3.2',
    engine_version='3.2.10',
    port=6379,
    subnet_group_name=elasticache_subnet_group.name,
    security_group_ids=[elasticache_security_group.id])

# Export the cluster's primary endpoint
pulumi.export('elasticache_cluster_primary_endpoint', elasticache_cluster.primary_endpoint_address)
```

Make sure to replace the `subnet_ids` with your actual AWS VPC subnet IDs where you want the ElastiCache cluster to be located.

In this program:

- We've created an AWS security group named `elasticache-security-group` that enables traffic on the Redis port (6379).
- `elasticache_subnet_group` is the subnet group associated with the ElastiCache cluster. You would replace the placeholder subnet IDs with the actual subnet IDs of your VPC.
- The `elasticache_cluster` is the Redis cluster with the specified cache node type and engine version tailored for Redis 3.2.10. You can adjust these parameters based on your specific needs.
- Finally, we export the Redis endpoint address as `elasticache_cluster_primary_endpoint`. This endpoint is used to connect your application to the Redis cluster.

Connecting your inference service to the Redis cluster requires additional code that is dependent on the specifics of your application. Typically, this involves setting up a Redis client in your codebase and caching inference results using the primary endpoint provided by the ElastiCache cluster.