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Olla vs GPUStack

Overview

Olla and GPUStack operate at different layers of the LLM infrastructure stack. GPUStack orchestrates and deploys models across GPU clusters, while Olla provides intelligent routing and failover for existing endpoints.

Core Differences

Primary Purpose

Olla: Application-layer proxy for routing and resilience

  • Routes requests to existing LLM services
  • Provides failover and load balancing
  • No model deployment or GPU management
  • Works with whatever's already running

GPUStack: Infrastructure orchestration platform

  • Deploys models across GPU clusters
  • Manages GPU allocation and scheduling
  • Handles model downloading and storage
  • Creates and manages inference endpoints

Stack Position

Application Layer:    Your Apps
Routing Layer:          Olla
Service Layer:    LLM Endpoints ([Ollama](https://github.com/ollama/ollama), [vLLM](https://github.com/vllm-project/vllm), etc)
Orchestration:       GPUStack (creates these)
Hardware Layer:      GPU Servers

Feature Comparison

Feature Olla GPUStack
Infrastructure Management
Model deployment
GPU resource management
Model downloading
Storage management
Node management
Request Handling
Request routing ✅ Advanced ✅ Basic
Load balancing strategies ✅ Multiple ⚠️ Limited
Circuit breakers
Retry mechanisms ✅ Sophisticated ⚠️ Basic
Health monitoring ✅ Continuous ✅ Instance-level
Model Management
Model discovery ✅ From endpoints N/A (deploys them)
Model name unification
Multi-provider support ❌ (GGUF focus)
Deployment
Complexity Simple (binary + YAML) Platform installation
Resource overhead ~40MB Platform overhead
Prerequisites None Kubernetes knowledge helpful

When to Use Each

Use Olla When:

  • You have existing LLM services running
  • Need intelligent routing between endpoints
  • Want automatic failover without re-deployment
  • Require advanced load balancing
  • Working with multiple LLM providers
  • Need minimal resource overhead

Use GPUStack When:

  • Starting from raw GPU hardware
  • Need to dynamically deploy models
  • Want Kubernetes-like orchestration
  • Managing a cluster of GPUs
  • Require automatic model distribution
  • Need GPU-aware scheduling

Better Together: Complementary Architecture

Olla and GPUStack work excellently together:

# Olla configuration
endpoints:
  # GPUStack-managed endpoints
  - name: gpustack-pool-1
    url: http://gpustack-1.internal:8080
    priority: 1
    type: openai

  - name: gpustack-pool-2
    url: http://gpustack-2.internal:8080
    priority: 1
    type: openai

  # Other endpoints
  - name: ollama-backup
    url: http://backup-server:11434
    priority: 2
    type: ollama

  - name: cloud-overflow
    url: http://litellm:8000
    priority: 10
    type: openai

Benefits of Combined Deployment:

  1. GPUStack manages the GPU infrastructure
  2. Deploys models based on demand
  3. Handles GPU allocation
  4. Manages model lifecycle

  5. Olla provides the reliability layer

  6. Routes between GPUStack instances
  7. Fails over to backup endpoints
  8. Provides circuit breakers
  9. Unifies access to all endpoints

Real-World Scenarios

Scenario 1: GPU Cluster with Fallbacks

                 Olla
    ┌─────────────┼─────────────┐
    ↓             ↓             ↓
GPUStack     Ollama        Cloud API
(Primary)   (Backup)      (Overflow)

How it works:

  • GPUStack manages your main GPU cluster
  • Olla routes requests, preferring GPUStack
  • Falls back to Ollama if cluster is busy
  • Overflows to cloud if everything is saturated

Scenario 2: Multi-Site Deployment

        Global Olla Instance
    ┌───────────┼───────────┐
    ↓           ↓           ↓
GPUStack    GPUStack    Direct
(Sydney)   (Melbourne)  Endpoints

Scenario 3: Development to Production

Development:  Laptop → Olla → Local Ollama
                    Cloud (fallback)

Production:   Apps → Olla → GPUStack Cluster
                  Cloud (overflow)

Integration Patterns

Pattern 1: GPUStack Primary, Others Secondary

# Olla prioritises GPUStack but maintains alternatives
endpoints:
  - name: gpustack-primary
    url: http://gpustack:8080
    priority: 1
  - name: manual-backup
    url: http://ollama:11434
    priority: 5

Pattern 2: Geographic Distribution

# Olla routes to nearest GPUStack region
endpoints:
  - name: gpustack-syd
    url: http://syd.gpustack:8080
    priority: 1  # For Sydney users
  - name: gpustack-mel
    url: http://mel.gpustack:8080
    priority: 1  # For Melbourne users

Performance Considerations

Resource Usage

  • Olla: ~40MB RAM, negligible CPU
  • GPUStack: Platform overhead + model memory
  • Combined: Minimal additional overhead from Olla

Latency

  • Olla routing: <2ms overhead
  • GPUStack: Model loading time (first request)
  • Combined: Olla can route around cold-start delays

Common Questions

Q: Does Olla duplicate GPUStack's routing? A: No. GPUStack does basic request distribution. Olla adds sophisticated load balancing, circuit breakers, and multi-provider support.

Q: Can Olla deploy models like GPUStack? A: No. Olla only routes to existing endpoints. Use GPUStack for model deployment.

Q: Should I use both in production? A: Yes, if you need both GPU orchestration and reliable routing. They're designed for different layers.

Q: Can Olla route to non-GPUStack endpoints? A: Absolutely! Olla works with any HTTP-based LLM endpoint.

Migration Patterns

Adding Olla to GPUStack

  1. Deploy Olla in front of GPUStack endpoints
  2. Configure health checks and priorities
  3. Add backup endpoints (Ollama, cloud)
  4. Point applications to Olla

Adding GPUStack to Olla Setup

  1. Deploy GPUStack cluster
  2. Add GPUStack endpoints to Olla config
  3. Set appropriate priorities
  4. Monitor and adjust load balancing

Conclusion

GPUStack and Olla are complementary tools that excel at different layers:

  • GPUStack: Infrastructure orchestration and model deployment
  • Olla: Intelligent routing and reliability

Together, they provide a complete solution: GPUStack manages your GPU infrastructure while Olla ensures reliable, intelligent access to all your LLM resources.