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:¶
- GPUStack manages the GPU infrastructure
- Deploys models based on demand
- Handles GPU allocation
-
Manages model lifecycle
-
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¶
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¶
- Deploy Olla in front of GPUStack endpoints
- Configure health checks and priorities
- Add backup endpoints (Ollama, cloud)
- Point applications to Olla
Adding GPUStack to Olla Setup¶
- Deploy GPUStack cluster
- Add GPUStack endpoints to Olla config
- Set appropriate priorities
- 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.