Issue Station
Sign In
Tools hereToolbox

litellm - 💡(How to fix) Fix RAM Spike on importing litellm [1 participants]

Official PRs (…)
ON THIS PAGE

Recommended Tools

×6

Utilities matched from this issue’s tags and category — try them while you read without losing context.

GitHub issue graph ai analysis

Paste a GitHub issue URL. We fetch that issue, discover linked issues from bodies/comments/timeline, collect linked pull requests, and produce a structured English report.

The report is written in English Markdown for sharing and archival.

Helpful · Quick feedback

Loading…
GitHub stats
BerriAI/litellm#24398•Fetched 2026-04-08 01:18:00
View on GitHub
Comments
0
Participants
1
Timeline
1
Reactions
0
Author
AbdealiLoKoAbdealiLoKo
Participants
AbdealiLoKoAbdealiLoKo
Timeline (top)
labeled ×1

Code Example

$ /usr/bin/time venv/bin/python -c "import sys"
0.02user 0.00system 0:00.03elapsed 91%CPU (0avgtext+0avgdata 15104maxresident)k
0inputs+0outputs (0major+1724minor)pagefaults 0swaps

$ /usr/bin/time venv/bin/python -c "import numpy"
1.16user 0.02system 0:00.21elapsed 547%CPU (0avgtext+0avgdata 30680maxresident)k
0inputs+0outputs (0major+3966minor)pagefaults 0swaps

$ /usr/bin/time venv/bin/python -c "import litellm"
4.22user 0.82system 0:05.20elapsed 97%CPU (0avgtext+0avgdata 162832maxresident)k
11768inputs+20768outputs (50major+48742minor)pagefaults 0swaps

$ /usr/bin/time venv/bin/python -c "import google.genai"
0.99user 0.11system 0:01.11elapsed 99%CPU (0avgtext+0avgdata 90708maxresident)k
0inputs+0outputs (0major+18901minor)pagefaults 0swaps

$ /usr/bin/time venv/bin/python -c "import openai"
0.80user 0.07system 0:00.87elapsed 99%CPU (0avgtext+0avgdata 54152maxresident)k
0inputs+0outputs (0major+10622minor)pagefaults 0swaps

$ /usr/bin/time venv/bin/python -c "import langgraph"
0.03user 0.00system 0:00.03elapsed 94%CPU (0avgtext+0avgdata 14976maxresident)k
0inputs+0outputs (0major+1723minor)pagefaults 0swaps

$ /usr/bin/time venv/bin/python -c "import langchain"
0.02user 0.01system 0:00.05elapsed 71%CPU (0avgtext+0avgdata 15104maxresident)k
8inputs+8outputs (0major+1744minor)pagefaults 0swaps
RAW_BUFFERClick to expand / collapse

Hi, I've been using the SDKs for my LLMs (openai, google.genai, etc.) so far which I use in a fastapi application I am working on which aims to support multiple LLM providers. Was trying out litellm to see if it can reduce some of my boiler plate code.

I found that when I import litellm my RAM spikes:

  1. sys (python builtin) -> 15MB
  2. litellm -> ~162MB
  3. google.genai -> ~90MB
  4. openai -> ~54MB
  5. langgraph/langchain -> ~15MB

I was expecting litellm to be much lighter considering I didn't even invoke any specific LLM provider as of now. I would guess that the RAM will increase to 162+90 if I use litellm to call a Gemini API ?

Reproducible snippets:

$ /usr/bin/time venv/bin/python -c "import sys"
0.02user 0.00system 0:00.03elapsed 91%CPU (0avgtext+0avgdata 15104maxresident)k
0inputs+0outputs (0major+1724minor)pagefaults 0swaps

$ /usr/bin/time venv/bin/python -c "import numpy"
1.16user 0.02system 0:00.21elapsed 547%CPU (0avgtext+0avgdata 30680maxresident)k
0inputs+0outputs (0major+3966minor)pagefaults 0swaps

$ /usr/bin/time venv/bin/python -c "import litellm"
4.22user 0.82system 0:05.20elapsed 97%CPU (0avgtext+0avgdata 162832maxresident)k
11768inputs+20768outputs (50major+48742minor)pagefaults 0swaps

$ /usr/bin/time venv/bin/python -c "import google.genai"
0.99user 0.11system 0:01.11elapsed 99%CPU (0avgtext+0avgdata 90708maxresident)k
0inputs+0outputs (0major+18901minor)pagefaults 0swaps

$ /usr/bin/time venv/bin/python -c "import openai"
0.80user 0.07system 0:00.87elapsed 99%CPU (0avgtext+0avgdata 54152maxresident)k
0inputs+0outputs (0major+10622minor)pagefaults 0swaps

$ /usr/bin/time venv/bin/python -c "import langgraph"
0.03user 0.00system 0:00.03elapsed 94%CPU (0avgtext+0avgdata 14976maxresident)k
0inputs+0outputs (0major+1723minor)pagefaults 0swaps

$ /usr/bin/time venv/bin/python -c "import langchain"
0.02user 0.01system 0:00.05elapsed 71%CPU (0avgtext+0avgdata 15104maxresident)k
8inputs+8outputs (0major+1744minor)pagefaults 0swaps

Versions being used:

  • Ubuntu 24.04 with WSL2 on Windows 11
  • Python 3.11.14
  • litellm 1.82.6

extent analysis

Fix Plan

To reduce the high memory usage caused by importing litellm, consider the following steps:

  • Optimize imports: Only import the necessary modules from litellm instead of the entire package. This can help reduce memory usage by avoiding the initialization of unnecessary components.
  • Lazy loading: Implement lazy loading for litellm by using the importlib module or a similar mechanism to delay the import until it's actually needed.
  • Memory profiling: Use memory profiling tools like mprof or memory_profiler to identify memory-intensive parts of the litellm package and optimize those areas.

Example code for lazy loading using importlib:

import importlib

class LitellmLoader:
    def __init__(self):
        self.litellm = None

    def load_litellm(self):
        if self.litellm is None:
            self.litellm = importlib.import_module('litellm')

    def use_litellm(self):
        self.load_litellm()
        # Use litellm here
        self.litellm.some_function()

Verification

To verify that the fix worked, measure the memory usage after implementing the above steps using the same method as before:

/usr/bin/time venv/bin/python -c "from your_module import LitellmLoader; loader = LitellmLoader(); loader.use_litellm()"

Compare the memory usage with the original measurement to ensure it has decreased.

Extra Tips

  • Regularly review and optimize your code to prevent similar issues in the future.
  • Consider using a memory-efficient alternative to litellm if the package's memory usage remains high after optimization.
  • Use tools like psutil to monitor memory usage in your application and detect potential issues early.

Vote matrix · Quick signals

Works
Did the solution work? Tap to confirm.
Easy Fix
Was it a quick fix?
Time Saver
Did it save you time?
Blocking
Was it severely blocking?
Common Issue
Are others likely hitting this too?
Flaky / Intermittent
Is it intermittent?
Verified / Reproducible
Can you reproduce it reliably?
Loading…
#api#prompt template#agent execution#callback error#memory management

Still need to ship something?

×6

Another batch ranked right after the header list — different links, same matching logic.

Back to top recommendations

TRENDING