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Reduce RAM Usage

Reduce RAM Usage​

If you are deploying Open WebUI in a RAM-constrained environment (such as a Raspberry Pi, small VPS, or shared hosting), there are several strategies to significantly reduce memory consumption.

On a Raspberry Pi 4 (arm64) with version v0.3.10, these optimizations reduced idle memory consumption from >1GB to ~200MB (as observed with docker container stats).

Quick Start​

Set the following environment variables for immediate RAM savings:

# Use external embedding instead of local SentenceTransformers
RAG_EMBEDDING_ENGINE=ollama

# Use external Speech-to-Text instead of local Whisper
AUDIO_STT_ENGINE=openai
tip

These settings can also be configured in the Admin Panel > Settings interface - set RAG embedding to Ollama or OpenAI, and Speech-to-Text to OpenAI or WebAPI.

Why Does Open WebUI Use So Much RAM?​

Much of the memory consumption comes from locally loaded ML models. Even when using an external LLM (OpenAI or separate Ollama instance), Open WebUI may load additional models for:

FeatureDefaultRAM ImpactSolution
RAG EmbeddingLocal SentenceTransformers~500-800MBUse Ollama or OpenAI embeddings
Speech-to-TextLocal Whisper~300-500MBUse OpenAI or WebAPI
RerankingDisabled~200-400MB when enabledKeep disabled or use external
Image GenerationDisabledVariableKeep disabled if not needed

⚙️ Environment Variables for RAM Reduction​

Offload Embedding to External Service​

The biggest RAM saver is using an external embedding engine:

# Option 1: Use Ollama for embeddings (if you have Ollama running separately)
RAG_EMBEDDING_ENGINE=ollama

# Option 2: Use OpenAI for embeddings
RAG_EMBEDDING_ENGINE=openai
OPENAI_API_KEY=your-api-key

Offload Speech-to-Text​

Local Whisper models consume significant RAM:

# Use OpenAI's Whisper API
AUDIO_STT_ENGINE=openai

# Or use browser-based WebAPI (no external service needed)
AUDIO_STT_ENGINE=webapi

Disable Unused Features​

Disable features you don't need to prevent model loading:

# Disable image generation (prevents loading image models)
ENABLE_IMAGE_GENERATION=False

# Disable code execution (reduces overhead)
ENABLE_CODE_EXECUTION=False

# Disable code interpreter
ENABLE_CODE_INTERPRETER=False

Reduce Background Task Overhead​

These settings reduce memory usage from background operations:

# Disable autocomplete (high resource usage)
ENABLE_AUTOCOMPLETE_GENERATION=False

# Disable automatic title generation
ENABLE_TITLE_GENERATION=False

# Disable tag generation
ENABLE_TAGS_GENERATION=False

# Disable follow-up suggestions
ENABLE_FOLLOW_UP_GENERATION=False

Database and Cache Optimization​

# Disable real-time chat saving (reduces database overhead)
ENABLE_REALTIME_CHAT_SAVE=False

# Reduce thread pool size for low-resource systems
THREAD_POOL_SIZE=10

Vector Database Multitenancy​

If using Milvus or Qdrant, enable multitenancy mode to reduce RAM:

# For Milvus
ENABLE_MILVUS_MULTITENANCY_MODE=True

# For Qdrant  
ENABLE_QDRANT_MULTITENANCY_MODE=True

🚀 Recommended Minimal Configuration​

For extremely RAM-constrained environments, use this combined configuration:

# Offload ML models to external services
RAG_EMBEDDING_ENGINE=ollama
AUDIO_STT_ENGINE=openai

# Disable all non-essential features
ENABLE_IMAGE_GENERATION=False
ENABLE_CODE_EXECUTION=False
ENABLE_CODE_INTERPRETER=False
ENABLE_AUTOCOMPLETE_GENERATION=False
ENABLE_TITLE_GENERATION=False
ENABLE_TAGS_GENERATION=False
ENABLE_FOLLOW_UP_GENERATION=False

# Reduce worker overhead
THREAD_POOL_SIZE=10

💡 Additional Tips​

  • Monitor Memory Usage: Use docker container stats or htop to monitor RAM consumption
  • Restart After Changes: Environment variable changes require a container restart
  • Fresh Deployments: Some environment variables only take effect on fresh deployments without an existing config.json
  • Consider Alternatives: For very constrained systems, consider running Open WebUI on a more capable machine and accessing it remotely