| .. | ||
| ollama_experiment_results.json | ||
| ollama_paper_figure.png | ||
| ollama_spacetime_experiment.py | ||
| ollama_spacetime_results.png | ||
| ollama_sqrt_n_relationship.png | ||
| ollama_sqrt_validation.png | ||
| README.md | ||
| test_ollama.py | ||
| visualize_results.py | ||
LLM Space-Time Tradeoffs with Ollama
This experiment demonstrates real space-time tradeoffs in Large Language Model inference using Ollama with actual models.
Experiments
1. Context Window Chunking
Demonstrates how processing long contexts in chunks (√n sized) trades memory for computation time.
2. Streaming vs Full Generation
Shows memory usage differences between streaming token-by-token vs generating full responses.
3. Multi-Model Memory Sharing
Explores loading multiple models with shared layers vs loading them independently.
Key Findings
The experiments show:
- Chunked context processing reduces memory by 70-90% with 2-5x time overhead
- Streaming generation uses O(1) memory vs O(n) for full generation
- Real models exhibit the theoretical √n space-time tradeoff
Running the Experiments
# Run all experiments
python ollama_spacetime_experiment.py
# Run specific experiment
python ollama_spacetime_experiment.py --experiment context_chunking
Requirements
- Ollama installed locally
- At least one model (e.g., llama3.2:latest)
- Python 3.8+
- 8GB+ RAM recommended