---
title: Olla vs GPUStack - GPU Orchestration vs Load Balancing
description: Understand the differences between Olla and GPUStack. Learn how these complementary tools work together for GPU cluster management and LLM routing.
keywords: olla vs gpustack, gpu orchestration, llm deployment, kubernetes ai, gpu cluster management, model deployment
---

# Olla vs GPUStack

## Overview

[Olla](https://github.com/thushan/olla) and [GPUStack](https://github.com/gpustack/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:

```yaml
# Olla configuration
discovery:
  static:
    endpoints:
      # GPUStack-managed endpoints (OpenAI-compatible API)
      - name: gpustack-pool-1
        url: http://gpustack-1.internal:8080
        priority: 1
        type: openai-compatible  # or "openai" - accepted alias

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

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

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

### Benefits of Combined Deployment:

1. **GPUStack manages the GPU infrastructure**
   - Deploys models based on demand
   - Handles GPU allocation
   - Manages model lifecycle

2. **Olla provides the reliability layer**
   - Routes between GPUStack instances
   - Fails over to backup endpoints
   - Provides circuit breakers
   - 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
```yaml
# Olla prioritises GPUStack but maintains alternatives
discovery:
  static:
    endpoints:
      - name: gpustack-primary
        url: http://gpustack:8080
        type: openai-compatible
        priority: 1
      - name: manual-backup
        url: http://ollama:11434
        type: ollama
        priority: 5
```

### Pattern 2: Geographic Distribution
```yaml
# Olla routes to nearest GPUStack region
discovery:
  static:
    endpoints:
      - name: gpustack-syd
        url: http://syd.gpustack:8080
        type: openai-compatible
        priority: 1  # For Sydney users
      - name: gpustack-mel
        url: http://mel.gpustack:8080
        type: openai-compatible
        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.