Running Multiple LLMs: My GPU Memory Management Nightmare

🎯 The Goal

I wanted to run multiple LLMs simultaneously: one for code, one for writing, one for analysis. Simple, right?

Wrong. GPU memory management became a nightmare.

The problem: Each LLM needs GPU memory. My RTX 4090 has 24GB. That should be enough, right? Wrong.

💥 What Went Wrong

Problem 1: Memory Fragmentation

Loading and unloading models created memory fragmentation. Eventually, even though I had "enough" memory, allocations failed.

Problem 2: Model Switching Overhead

Switching between models was slow. Loading a model took 30-60 seconds. Not practical for real-time use.

Problem 3: OOM Errors

Out-of-memory errors were frequent. Models would crash mid-inference.

✅ Solutions That Worked

Solution 1: Model Quantization

Using quantized models (4-bit, 8-bit) reduced memory usage significantly:

# 4-bit quantization
from transformers import BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16
)

model = AutoModelForCausalLM.from_pretrained(
    "model-name",
    quantization_config=quantization_config
)

Impact: Reduced memory usage by 75%. Could run 3-4 models instead of 1.

Solution 2: Model Caching

Keep models in memory, don't unload them:

# Cache models in memory
models = {
    'code': load_model('code-llm'),
    'writing': load_model('writing-llm'),
    'analysis': load_model('analysis-llm'),
}

# Use cached models
response = models['code'].generate(prompt)

Impact: No loading overhead. Instant switching between models.

Solution 3: Memory Pooling

Pre-allocate GPU memory pools to avoid fragmentation:

import torch

# Pre-allocate memory
torch.cuda.empty_cache()
torch.cuda.set_per_process_memory_fraction(0.9)

📊 Results

💡 Key Takeaways

• Quantization is essential for running multiple models
• Model caching eliminates switching overhead
• Memory pooling prevents fragmentation
• 24GB VRAM can run 3-4 quantized models
• Plan your memory usage carefully

Running multiple LLMs is possible, but it requires careful memory management. Quantization and caching are your friends.