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The NVIDIA GeForce RTX 4090 represents the pinnacle of consumer GPU performance, built on the Ada Lovelace architecture. With 16,384 CUDA cores and 24GB of GDDR6X memory, it delivers unprecedented compute power for CUDA developers working on machine learning, scientific computing, and real-time graphics. For CUDA developers, the RTX 4090 offers exceptional value compared to datacenter GPUs. Its 82.6 TFLOPS of FP32 performance rivals the A100 in many workloads, while costing a fraction of the price. The 4th generation Tensor Cores provide 1.32 PFLOPS of FP8 performance, making it ideal for inference and mixed-precision training. This guide covers the RTX 4090's specifications, CUDA optimization strategies, benchmark results, and practical tips for maximizing performance in your GPU kernels.
| Architecture | Ada Lovelace (AD102) |
| CUDA Cores | 16,384 |
| Tensor Cores | 512 |
| Memory | 24GB GDDR6X |
| Memory Bandwidth | 1,008 GB/s |
| Base / Boost Clock | 2235 / 2520 MHz |
| FP32 Performance | 82.6 TFLOPS |
| FP16 Performance | 165.2 TFLOPS |
| L2 Cache | 72MB |
| TDP | 450W |
| NVLink | No |
| MSRP | $1,599 |
| Release | October 2022 |
This code snippet shows how to detect your RTX 4090, check available memory, and configure optimal settings for the Ada Lovelace (AD102) architecture.
import torch
import pynvml
# Check if RTX 4090 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")
# RTX 4090 Memory: 24GB - Optimal batch sizes
# Architecture: Ada Lovelace (AD102)
# CUDA Cores: 16,384
# Memory-efficient training for RTX 4090
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Ada Lovelace (AD102)
torch.backends.cudnn.allow_tf32 = True
# Check available memory
pynvml.nvmlInit()
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
info = pynvml.nvmlDeviceGetMemoryInfo(handle)
print(f"Free memory: {info.free / 1024**3:.1f} GB / 24 GB total")
# Recommended batch size calculation for RTX 4090
model_memory_gb = 2.0 # Adjust based on your model
batch_multiplier = (24 - model_memory_gb) / 4 # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for RTX 4090: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 1,850 | 45% faster than RTX 3090 |
| BERT-Large Inference (sentences/sec) | 3,200 | 2.1x faster than RTX 3090 |
| Stable Diffusion (512x512, sec/img) | 2.8 | 55% faster than RTX 3090 |
| LLaMA-7B Inference (tokens/sec) | 85 | Matches A100 40GB |
| cuBLAS SGEMM 8192x8192 (TFLOPS) | 78.5 | 95% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 945 | 94% of theoretical peak |
| Use Case | Rating | Notes |
|---|---|---|
| Deep Learning Training | Excellent | Best consumer GPU for training, limited only by 24GB VRAM for large models |
| ML Inference | Excellent | FP8 Tensor Cores deliver datacenter-class inference performance |
| Scientific Computing | Excellent | Exceptional FP32/FP64 performance for simulations |
| Video Processing | Excellent | Dual NVENC with AV1 support for professional workflows |
| Multi-GPU Training | Fair | No NVLink - limited to PCIe for multi-GPU communication |
| Large Language Models | Good | 24GB handles 7B-13B models, larger require multi-GPU or quantization |
The RTX 4090 is exceptional for CUDA development. With 16,384 CUDA cores, Compute Capability 8.9, and 24GB VRAM, it handles most development workloads. The large L2 cache and high memory bandwidth make it excellent for profiling and optimizing kernels before deploying to datacenter GPUs.
The RTX 4090 achieves 70-90% of A100 40GB performance in most ML tasks at 1/6th the cost. The A100 advantages include 40/80GB HBM2e memory, NVLink for multi-GPU, and ECC memory. For single-GPU workloads under 24GB, the RTX 4090 offers better value.
The RTX 4090 has CUDA Compute Capability 8.9 (Ada Lovelace architecture). This supports all modern CUDA features including FP8 Tensor Core operations, hardware-accelerated async copies, and thread block clusters.
Yes, but with limitations. The 24GB VRAM handles training models up to ~7B parameters with gradient checkpointing. For larger models, you need quantization (QLoRA), model parallelism across multiple GPUs, or datacenter GPUs with more memory.
NVIDIA recommends a minimum 850W PSU. The RTX 4090 has a 450W TDP with transient spikes up to 600W. Use a quality PSU with a single 16-pin 12VHPWR connector or 3x 8-pin adapters for stable operation during CUDA workloads.
70% of 4090 performance at $400 less, 16GB VRAM
Previous gen, 24GB VRAM, available used at good prices
Datacenter GPU with 40/80GB HBM2e and NVLink
Latest datacenter GPU, 4x faster for transformer inference
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