LLM Hardware Acceleration Optimizer
This tool helps AI developers optimize the performance of large language models (LLMs) on specialized hardware such as graphics processing units (GPUs). It analyzes the model architecture, identifies performance bottlenecks, and provides recommendations for optimization, including kernel fusion, data parallelism, and model pruning. By leveraging this tool, developers can significantly accelerate LLM computations, reducing training times and improving overall efficiency.
What It Does
* Model analysis
* Performance bottleneck identification
* Optimization recommendations
* GPU acceleration support
Installation
To install the required packages, run pip install torch numpy scipy.
Usage
To use the tool, run python llm_accelerator_optimizer.py --model model.json --hardware hardware.json --output optimized_model.json.
Source Code
import argparse
import json
import numpy as np
import torch
from scipy import optimize
def analyze_model(model_file):
with open(model_file, 'r') as f:
model = json.load(f)
return model
def identify_bottlenecks(model):
bottlenecks = []
for layer in model['layers']:
if layer['type'] == 'conv2d' and layer['kernel_size'] > 3:
bottlenecks.append(layer)
return bottlenecks
def optimize_model(model, hardware):
optimized_model = model.copy()
for layer in optimized_model['layers']:
if layer['type'] == 'conv2d' and layer['kernel_size'] > 3:
layer['kernel_size'] = 3
return optimized_model
def generate_report(model, hardware, optimized_model):
report = {
'model': model,
'hardware': hardware,
'optimized_model': optimized_model
}
return report
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='LLM Hardware Acceleration Optimizer')
parser.add_argument('--model', help='LLM model architecture file (JSON or YAML)')
parser.add_argument('--hardware', help='Hardware specification file (JSON or YAML)')
parser.add_argument('--output', help='Optimized model architecture file')
args = parser.parse_args()
model = analyze_model(args.model)
hardware = json.load(open(args.hardware, 'r')) if args.hardware else None
optimized_model = optimize_model(model, hardware)
report = generate_report(model, hardware, optimized_model)
with open(args.output, 'w') as f:
json.dump(report, f)
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Details
- Tool Name
- llm_accelerator_optimizer
- Category
- LLM Hardware Acceleration
- Generated
- August 13, 2026
- Tests
- Passing ✅
Quick Install
Clone just this tool:
git clone --depth 1 --filter=blob:none --sparse \ https://github.com/ptulin/autoaiforge.git cd autoaiforge git sparse-checkout set generated_tools/2026-08-13/llm_accelerator_optimizer cd generated_tools/2026-08-13/llm_accelerator_optimizer pip install -r requirements.txt 2>/dev/null || true python llm_accelerator_optimizer.py