Benchmarking the NVIDIA GeForce RTX 4060 for Machine Learning Workloads

Trading in today’s financial markets isn’t just about intuition and experience—it’s also about data. Whether you’re analyzing price movements, testing trading strategies, or monitoring hundreds of assets, the ability to process data quickly can give you an edge. This is where a Graphics Processing Unit (GPU) like the NVIDIA GeForce RTX 4060 comes in.

While GPUs are often associated with gaming or advanced machine learning, they’re equally valuable for tasks traders encounter daily. Let’s explore how the RTX 4060 can enhance your trading workflow, even if you’re not running complex simulations or handling massive datasets.

What is CUDA?

CUDA (Compute Unified Device Architecture) is NVIDIA’s parallel computing platform and programming model. It allows developers to use NVIDIA GPUs for general-purpose computing—not just rendering graphics but also accelerating tasks like matrix operations, convolutions, and more.

Why Use CUDA for ML?

• Massive Parallelism: Deep learning involves thousands to millions of small operations (like multiplying and adding matrices). CUDA lets you harness the thousands of GPU cores to perform these operations simultaneously.
• Highly Optimized Libraries: Libraries such as cuBLAS, cuDNN, and TensorRT are written on top of CUDA, providing built-in optimizations for matrix multiplication, convolutions, and other essential ML functions.

Why the GPU Matters for Traders

If you’re a trader working with tools like Python, Excel-based analytics, or even custom trading platforms, a GPU can make your life easier by speeding up calculations and improving efficiency. Here’s how:

Optimized Data Processing: Handling high-frequency data? Even without advanced modeling, GPUs accelerate tasks like sorting, filtering, and statistical analysis of large datasets.

Faster Strategy Backtesting: Backtesting is critical to evaluate the success of a trading strategy. For example:

Testing a moving average crossover strategy on years of intraday data can take hours on a CPU.

A GPU reduces this time significantly, allowing you to iterate and refine strategies faster.

Real-Time Market Analysis: If you monitor hundreds of stocks, crypto assets, or forex pairs, real-time analysis requires crunching large amounts of data. GPUs help by speeding up calculations for technical indicators like RSI, Bollinger Bands, or custom formulas.

Advanced Charting and Dashboards: Whether you use TradingView-like platforms or custom dashboards, GPUs make rendering charts, volatility surfaces, and heatmaps smoother and more responsive.

What Makes the RTX 4060 Ideal for Traders?

The NVIDIA GeForce RTX 4060 is a budget-friendly GPU with powerful capabilities. Here’s why it’s great for traders:

Compact Yet Powerful: The RTX 4060 is efficient and doesn’t require a high-end PC setup, making it suitable for home offices or small setups.

Speed at an Affordable Price: While high-end GPUs like the RTX 4090 are overkill for most traders, the RTX 4060 provides excellent performance at a fraction of the cost.

8 GB of VRAM: This is more than enough for most trading-related tasks, such as analyzing intraday data, running backtests, and visualizing charts.

Practical Benefits for Everyday Traders

Here are some practical ways the RTX 4060 can benefit you as a trader:

1. Speeding Up Indicator Calculations: Computing technical indicators across multiple assets becomes much faster. For example, calculating moving averages or Bollinger Bands on years of intraday data can now be done in minutes instead of hours.
2. Quicker Backtesting: Running backtests on your strategies, whether for stocks, crypto, or forex, becomes more efficient, allowing you to try multiple scenarios and refine your approach.
3. Efficient Real-Time Alerts: If you use custom scanners or alerts, a GPU helps you analyze incoming data streams faster, ensuring you never miss a key trading opportunity.
4. Improved Visualizations: Create smoother, interactive dashboards with heatmaps, custom charts, or even real-time market monitors.

Is the RTX 4060 Right for You?

If you’re an independent trader or part of a small team, the RTX 4060 offers a perfect balance of affordability and performance. Whether you’re building custom trading tools, analyzing data, or backtesting strategies, it provides noticeable speed improvements.

For those handling exceptionally large datasets or advanced machine learning models, a GPU with more VRAM (like the RTX 4070 or higher) might be necessary. However, for most traders focused on quick analysis and strategy refinement, the RTX 4060 is more than capable.

2. Setting Up the NVIDIA GeForce RTX 4060 on Windows with Python

Below are key steps to configure your environment so Python can utilize the RTX 4060 for HPC tasks:

1. Install NVIDIA Drivers
   • Make sure you have the latest drivers for your GPU, available via NVIDIA’s download page.
2. Install Python 3.9 (or later)
   • This ensures compatibility with modern scientific libraries like PyTorch, NumPy, SciPy, etc.
3. Install PyTorch with CUDA 12.4
   • Although we often associate PyTorch with deep learning, it is also a fantastic numerical library for HPC tasks.
   • Use the following command to get a build compiled against CUDA 12.4, which the RTX 4060 supports:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124

Verify GPU Availability

import torch
print("CUDA is available:", torch.cuda.is_available())
print("GPU Name:", torch.cuda.get_device_name(0) if torch.cuda.is_available() else "N/A")

3. High-Performance Benchmark Script for Financial Analytics

While the code below references typical machine-learning tasks, the operations tested—matrix multiplication, batch matrix multiplication, FFT, and convolution—are also staples in quantitative finance. For example:

• Matrix multiplication is used in portfolio covariance calculations.
• Batch matrix multiplication can appear in large-scale scenario analyses or factor model computations.
• FFT can be used for certain signal-processing or seasonal decomposition tasks.

Here’s the script we used to benchmark GPU performance:

import torch
import torch.nn as nn
import time
import numpy as np

# Set memory allocation configuration to avoid fragmentation
torch.cuda.set_per_process_memory_fraction(0.8)  # Use only 80% of available memory
torch.backends.cudnn.benchmark = True

class ComplexModel(nn.Module):
    """
    While described here as a 'model' with convolutions,
    you can adapt it for large matrix ops, PDE-based calcs,
    or any other HPC tasks in finance.
    """
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(256 * 32 * 32, 1024)
        self.fc2 = nn.Linear(1024, 512)
        self.fc3 = nn.Linear(512, 10)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))
        x = self.pool(self.relu(self.conv2(x)))
        x = self.pool(self.relu(self.conv3(x)))
        x = x.view(-1, 256 * 32 * 32)
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        return self.fc3(x)

def benchmark_operation(name, operation, *args, warmup=2, iterations=10):
    """
    General benchmarking function:
    - Warms up the GPU
    - Measures average time over multiple iterations
    - Synchronizes CUDA to ensure accurate timings
    """
    try:
        # Warmup
        for _ in range(warmup):
            result = operation(*args)
            if isinstance(result, torch.Tensor) and result.requires_grad:
                result.backward(torch.ones_like(result))
                torch.cuda.synchronize()
                torch.cuda.empty_cache()

        torch.cuda.synchronize()
        
        # Benchmark
        times = []
        for _ in range(iterations):
            torch.cuda.empty_cache()
            start_time = time.perf_counter()
            
            result = operation(*args)
            if isinstance(result, torch.Tensor) and result.requires_grad:
                result.backward(torch.ones_like(result))
                
            torch.cuda.synchronize()
            times.append(time.perf_counter() - start_time)
        
        avg_time = np.mean(times) * 1000  # ms
        std_time = np.std(times) * 1000   # ms
        
        print(f"{name:30} | Avg: {avg_time:8.2f} ms | Std: {std_time:8.2f} ms")
        return avg_time
    except RuntimeError as e:
        print(f"{name:30} | Failed: {str(e)}")
        torch.cuda.empty_cache()
        return None

def print_memory_status():
    print(f"\nGPU Memory Status:")
    print(f"Allocated: {torch.cuda.memory_allocated() / 1024**2:.2f} MB")
    print(f"Reserved:  {torch.cuda.memory_reserved() / 1024**2:.2f} MB")
    print(f"Max Allocated: {torch.cuda.max_memory_allocated() / 1024**2:.2f} MB")

def run_benchmarks():
    print("\nGPU Information:")
    print("-" * 50)
    if torch.cuda.is_available():
        print(f"Device: {torch.cuda.get_device_name()}")
        print(f"CUDA Version: {torch.version.cuda}")
        print(f"cuDNN Enabled: {torch.backends.cudnn.enabled}")
        print(f"Memory Allocated: {torch.cuda.memory_allocated() / 1024**2:.2f} MB")
        print(f"Total GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f} GB")
    else:
        print("CUDA is not available. Running on CPU.")
        return

    device = torch.cuda.current_device()
    torch.cuda.empty_cache()

    print("\nRunning Complex GPU Operations Benchmark")
    print("-" * 50)
    print(f"{'Operation':30} | {'Time':^20} | {'Std Dev':^8}")
    print("-" * 50)

    # 1. Matrix Multiplication
    size = 2048
    matrix1 = torch.randn(size, size, device=device, requires_grad=True)
    matrix2 = torch.randn(size, size, device=device, requires_grad=True)
    benchmark_operation("Matrix Mult (2048x2048)", torch.matmul, matrix1, matrix2)
    print_memory_status()

    # 2. Batch Matrix Multiplication
    batch_size = 16
    batch1 = torch.randn(batch_size, 1024, 1024, device=device, requires_grad=True)
    batch2 = torch.randn(batch_size, 1024, 1024, device=device, requires_grad=True)
    benchmark_operation("Batch Matrix Mult (16x1024)", torch.bmm, batch1, batch2)
    print_memory_status()

    # 3. Complex Model (for HPC test)
    model = ComplexModel().to(device)
    batch = torch.randn(32, 3, 256, 256, device=device, requires_grad=True)
    benchmark_operation("NN/HPC Forward+Backward", model, batch)
    print_memory_status()

    # 4. Convolution Operation
    conv = nn.Conv2d(32, 64, kernel_size=3, padding=1).to(device)
    conv_input = torch.randn(16, 32, 128, 128, device=device, requires_grad=True)
    benchmark_operation("Convolution (16x32x128x128)", conv, conv_input)
    print_memory_status()

    # 5. FFT Operation
    fft_input = torch.randn(512, 512, device=device, requires_grad=True)
    benchmark_operation("2D FFT (512x512)", torch.fft.fft2, fft_input)
    print_memory_status()

    # Final Memory Usage
    print("\nFinal Memory Usage:")
    print("-" * 50)
    print(f"Allocated: {torch.cuda.memory_allocated() / 1024**2:.2f} MB")
    print(f"Reserved:  {torch.cuda.memory_reserved() / 1024**2:.2f} MB")
    print(f"Max Allocated: {torch.cuda.max_memory_allocated() / 1024**2:.2f} MB")

    torch.cuda.empty_cache()

if __name__ == "__main__":
    run_benchmarks()

3.1. Benchmark Results

We reduced the size of certain operations to stay within 8 GB of VRAM. Below are the condensed results on an RTX 4060 (8 GB VRAM):

Operation                      | Time (ms)    | Std Dev (ms)
-------------------------------------------------------------
Matrix Mult (2048x2048)        | Avg: 6.53    | Std: 0.02
Batch Matrix Mult (16x1024)    | Avg: 14.11   | Std: 0.51
NN/HPC Forward+Backward        | Avg: 147.38  | Std: 1.88
Convolution (16x32x128x128)    | Avg: 5.94    | Std: 0.24
2D FFT (512x512)               | Avg: 0.27    | Std: 0.08

During these runs, peak memory usage (~5 GB) occurred while testing the small HPC-like neural net, well within the 8 GB limit. Although we used a “model” here, you can imagine substituting your Monte Carlo or derivative pricing code to see comparable speedups.

4. Memory Management Tips

Partial Allocation

torch.cuda.set_per_process_memory_fraction(0.8)

Reserves only 80% of available memory, giving you headroom to avoid out-of-memory errors.

Cache Clearing

torch.cuda.empty_cache()

Helps to reclaim GPU memory after each computation block.

Mixed Precision

• If your calculations can tolerate half-precision, try it out. This frees up GPU memory for larger batch sizes or more extensive calculations.

Batching

• For extremely large tasks, break them into smaller batches or partial computations. Summaries or partial results can be combined at the end.

Key Takeaways

• Quant Finance Fits GPUs: Many HPC tasks in quantitative finance—like Monte Carlo simulations, PDE-based pricing, and large-scale portfolio optimization—can run significantly faster on GPUs like the RTX 4060.
• Mind Your VRAM: With only 8 GB, you might need to optimize memory usage. However, for many moderate-sized calculations or smaller-scale HFT backtesting, 8 GB is plenty.
• Versatile Python Stack: Tools like PyTorch, NumPy, and libraries supporting CUDA are not just for deep learning; they’re also powerful for HPC tasks relevant to finance.
• Easily Adaptable: The provided benchmark script can serve as a template for your specific HPC routines—simply replace the matrix, convolution, or network forward pass with your financial computations.

If you need to handle massive data or extremely large HPC tasks (e.g., simulating entire markets or extremely large correlation matrices), you might consider a GPU with more VRAM or distributed computing solutions. For most personal or small-office quant use cases, the RTX 4060 is a cost-effective and capable solution.

Happy coding and profitable trading!