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The NVIDIA GeForce RTX 3090 Ti represents the absolute peak of Ampere consumer GPUs, featuring the fully-enabled GA102 die with 10,752 CUDA cores and 24GB of faster GDDR6X memory. Released as a halo product, it delivers approximately 10% more performance than the RTX 3090. For CUDA developers, the RTX 3090 Ti offers the maximum Ampere performance with 24GB VRAM - valuable for large model training. While no longer in production, excellent used prices make it attractive for workloads that benefit from 24GB memory without needing Ada features. This guide covers the RTX 3090 Ti's specifications, CUDA optimization strategies, and practical considerations for this legacy flagship.
| Architecture | Ampere (GA102) |
| CUDA Cores | 10,752 |
| Tensor Cores | 336 |
| Memory | 24GB GDDR6X |
| Memory Bandwidth | 1,008 GB/s |
| Base / Boost Clock | 1560 / 1860 MHz |
| FP32 Performance | 40 TFLOPS |
| FP16 Performance | 80 TFLOPS |
| L2 Cache | 6MB |
| TDP | 450W |
| NVLink | Yes |
| MSRP | $1,999 |
| Release | March 2022 |
This code snippet shows how to detect your RTX 3090 Ti, check available memory, and configure optimal settings for the Ampere (GA102) architecture.
import torch
import pynvml
# Check if RTX 3090 Ti 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 Ti Memory: 24GB - Optimal batch sizes
# Architecture: Ampere (GA102)
# CUDA Cores: 10,752
# Memory-efficient training for RTX 3090 Ti
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 Ti
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 Ti: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 1,380 | 10% faster than 3090 |
| BERT-Large Inference (sentences/sec) | 1,850 | 10% faster than 3090 |
| Stable Diffusion (512x512, sec/img) | 4.5 | 8% faster than 3090 |
| LLaMA-7B Inference (tokens/sec) | 52 | 10% faster than 3090 |
| cuBLAS SGEMM 8192x8192 (TFLOPS) | 38 | 95% efficiency |
| Memory Bandwidth (GB/s measured) | 960 | 95% efficiency |
| Use Case | Rating | Notes |
|---|---|---|
| Deep Learning Training | Excellent | 24GB handles large models |
| ML Inference | Good | Solid but lacks FP8 of Ada |
| Scientific Computing | Excellent | Strong FP32/FP64 for simulations |
| Video Processing | Good | NVENC but no AV1 encode |
| Multi-GPU Training | Excellent | NVLink for dual-GPU |
| Large Language Models | Good | 24GB fits 7B-13B models |
For used purchases at good prices, yes if you need 24GB VRAM. The combination of 24GB and NVLink is unique in consumer GPUs. For new purchases, RTX 4080/4090 are better choices.
RTX 4080 is 15-20% faster with better efficiency (320W vs 450W) and FP8 support. However, 3090 Ti has 24GB vs 16GB VRAM and NVLink. Choose based on memory needs.
Yes, RTX 3090 Ti supports NVLink for connecting two cards. This doubles memory to 48GB and increases compute, useful for large model training. Ensure adequate power supply (1000W+).
NVIDIA recommends 850W minimum, but 1000W is safer for sustained CUDA workloads. Use quality PCIe power cables and ensure proper power delivery.
Much faster, 24GB, no NVLink
Faster, efficient, but 16GB
10% slower, cheaper used
Datacenter, 80GB HBM2e
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