MemoryError
builtins.MemoryError
Stack trace
Traceback (most recent call last):
File "app.py", line 42, in <module>
response = client.chat.completions.create(model="llama-70b", messages=messages)
File "/usr/local/lib/python3.9/site-packages/ollama/client.py", line 120, in create
self._load_model(model)
File "/usr/local/lib/python3.9/site-packages/ollama/client.py", line 85, in _load_model
raise MemoryError("Insufficient memory to load the model")
MemoryError: Insufficient memory to load the model Why it happens
Ollama models require loading large amounts of data into RAM. If the model size exceeds the available system memory, Python raises a MemoryError. This often happens when attempting to load very large models like llama-70b on machines with limited RAM.
Detection
Monitor system memory usage before and during model loading; catch MemoryError exceptions to log and alert when memory limits are exceeded.
Causes & fixes
Attempting to load a very large Ollama model (e.g., llama-70b) on a machine with insufficient RAM.
Switch to a smaller model variant compatible with your system's memory capacity, such as llama-13b or llama-7b.
Running multiple memory-intensive processes alongside Ollama model loading, reducing available RAM.
Close unnecessary applications or processes to free up memory before loading the model.
Using a 32-bit Python interpreter which limits addressable memory space.
Use a 64-bit Python interpreter to allow access to more system memory.
Code: broken vs fixed
import ollama
# This line triggers MemoryError if model too large
response = ollama.chat(model="llama-70b", messages=[{"role": "user", "content": "Hello"}])import ollama
# Changed model to smaller variant to avoid MemoryError
response = ollama.chat(model="llama-13b", messages=[{"role": "user", "content": "Hello"}])
print(response)Workaround
Catch MemoryError exceptions and fallback to a smaller model dynamically or queue the request for processing on a machine with more memory.
Prevention
Architect your system to detect available memory before model loading and select models accordingly; consider deploying large models on dedicated high-memory servers.