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The NVIDIA GeForce RTX 3090 remains a compelling choice for CUDA developers seeking 24GB of VRAM at an accessible price point. Built on the Ampere architecture with 10,496 CUDA cores, it delivers strong compute performance for machine learning and scientific computing workloads. For CUDA developers, the RTX 3090's 24GB GDDR6X memory is its standout feature, matching the RTX 4090 in capacity while being available at lower prices, especially in the used market. The 3rd generation Tensor Cores support TF32, FP16, and INT8 operations, though they lack the FP8 support of newer Ada Lovelace GPUs. This guide covers the RTX 3090's specifications, CUDA optimization strategies, benchmark results, and practical tips for maximizing performance in your GPU kernels.
| Architecture | Ampere (GA102) |
| CUDA Cores | 10,496 |
| Tensor Cores | 328 |
| Memory | 24GB GDDR6X |
| Memory Bandwidth | 936 GB/s |
| Base / Boost Clock | 1395 / 1695 MHz |
| FP32 Performance | 35.6 TFLOPS |
| FP16 Performance | 71.2 TFLOPS |
| L2 Cache | 6MB |
| TDP | 350W |
| NVLink | Yes |
| MSRP | $1,499 |
| Release | September 2020 |
This code snippet shows how to detect your RTX 3090, check available memory, and configure optimal settings for the Ampere (GA102) architecture.
import torch
import pynvml
# Check if RTX 3090 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")
# RTX 3090 Memory: 24GB - Optimal batch sizes
# Architecture: Ampere (GA102)
# CUDA Cores: 10,496
# Memory-efficient training for RTX 3090
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Ampere (GA102)
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 3090
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 3090: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 1,280 | Baseline reference |
| BERT-Large Inference (sentences/sec) | 1,520 | Still competitive for inference |
| Stable Diffusion (512x512, sec/img) | 4.3 | Handles SDXL with 24GB VRAM |
| LLaMA-7B Inference (tokens/sec) | 48 | Full model fits in 24GB |
| cuBLAS SGEMM 8192x8192 (TFLOPS) | 32.8 | 92% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 875 | 93% of theoretical peak |
| Use Case | Rating | Notes |
|---|---|---|
| Deep Learning Training | Good | 24GB VRAM handles large models, raw speed behind RTX 40 series |
| ML Inference | Good | Strong inference but lacks FP8 of newer GPUs |
| Scientific Computing | Good | Solid FP32/FP64 performance for simulations |
| Multi-GPU Training | Excellent | NVLink support makes it unique among consumer GPUs |
| Large Language Models | Good | 24GB handles 7B-13B models fully loaded |
| Budget ML Workstation | Excellent | Best value for 24GB VRAM in used market |
Yes, especially used. The 24GB VRAM is valuable for large models, and prices have dropped significantly. If you need maximum performance, RTX 4090 is better, but RTX 3090 offers excellent value for budget-conscious researchers.
Yes, RTX 3090 supports NVLink with 112.5 GB/s bandwidth per direction. This enables efficient multi-GPU training with PyTorch or TensorFlow, though NVLink bridges are becoming harder to find.
RTX 4080 is 30-40% faster with better Tensor Cores (FP8), but has only 16GB VRAM. Choose RTX 3090 if you need 24GB for large models; choose RTX 4080 for raw speed on models that fit in 16GB.
RTX 3090 has CUDA Compute Capability 8.6 (Ampere). This supports all Ampere features including TF32 Tensor Core ops, async memory copies, and hardware acceleration for sparse operations.
RTX 3090 is approximately 40-50% slower than RTX 4090 in most CUDA workloads. The gap is larger for inference due to missing FP8, but smaller for memory-bound workloads where the similar bandwidth helps.
45% faster, same 24GB VRAM, newer architecture
35% faster but only 16GB VRAM
Datacenter GPU with 40/80GB HBM2e
20% slower but much cheaper, 10/12GB VRAM
Ready to optimize your CUDA kernels for RTX 3090? Download RightNow AI for real-time performance analysis.