NVIDIA RTX 4060 Ti
4,352
CUDA Cores
16GB
VRAM
288
GB/s
Technical Specifications
4,352
CUDA Cores
2310
Base MHz
2535
Boost MHz
16GB GDDR6
128-bit bus
Performance
22.1
FP32 TFLOPS
44.2
FP16 TFLOPS
165W
TDP
Cloud Availability
1
Available Instances
$0.10/hr
Starting Price
Detailed Specifications
| Architecture | Ada Lovelace (Unknown) |
| Release Date | 2024-05-23 |
| Launch Price | $499.00 |
| Process | 5nm |
| Transistors | 22.9B |
AI Features
Gen 4
Tensor Cores
Disabled
Transformer Engine
Not Supported
Flash Attention
Physical Specifications
Dimensions
9.0in
Length
3.9in
Width
2-slot
Height
About RTX 4060 Ti GPU
The NVIDIA RTX 4060 Ti was introduced in 2024 on the Ada Lovelace architecture. Its specification combines 4,352 CUDA cores and 136 Tensor Cores with 16GB of GDDR6, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 16GB memory capacity, the RTX 4060 Ti is best evaluated for quantized language-model inference, image generation, and medium-sized training jobs. Always size the model, optimizer state, KV cache, and framework overhead together rather than choosing a GPU from core count alone.
GPUvec currently tracks 1 cloud listing for the RTX 4060 Ti, with the lowest observed hourly rate at $0.10. Prices and availability can change by provider, region, and instance configuration, so verify the final rate before starting a workload.
For software planning, the RTX 4060 Ti combines 288 GB/s of memory bandwidth, CUDA compute capability 8.9, and a 5nm manufacturing process. Confirm the minimum CUDA, PyTorch, TensorFlow, or driver version required by your application before renting or purchasing hardware.
The 16GB RTX 4060 Ti is unusual because it prioritizes memory capacity over bandwidth. It can hold models that do not fit on 8GB or 12GB cards, but its 288 GB/s bandwidth means memory-intensive jobs may run more slowly than on higher-tier Ada GPUs.
The 16GB RTX 4060 Ti should be benchmarked with memory-bound workloads before selection. Its larger memory pool can avoid CPU offload, while 288 GB/s bandwidth can limit token or image throughput. This makes it a different trade-off from faster 12GB Ada cards rather than a simple higher-or-lower tier choice.
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External Resources
Learn more about GPUs from these authoritative sources:
Official CUDA programming guide
NVIDIA GPU Specifications →Official NVIDIA GPU specs
TechPowerUp GPU Database →Comprehensive GPU specifications
CUDA Compute Capability Guide →GPU compute capability reference
What You Need to Know About the RTX 4060 Ti
Complete Specifications for the NVIDIA RTX 4060 Ti
The NVIDIA RTX 4060 Ti uses the Ada Lovelace architecture and combines 4,352 CUDA cores with 16GB of GDDR6. GPUvec records 288 GB/s memory bandwidth alongside architecture, release date, process node, accelerator features, and physical specifications so the page can be evaluated from source data instead of model-name assumptions.
Compare NVIDIA RTX 4060 Ti Cloud Rental Prices per Hour
GPUvec currently tracks 1 cloud listing, with an observed starting rate of $0.10 per hour. Provider rates can vary by region, host configuration, billing model, and availability. Use the provider directory and related-GPU links to compare an available alternative when this exact accelerator is not listed.
Is the NVIDIA RTX 4060 Ti the Right GPU for Your AI Workload?
Start with the 16GB memory limit, then evaluate 288 GB/s memory bandwidth, compute features, and software compatibility for your workload. Model weights, KV cache, activations, optimizer state, and framework overhead all consume memory. Compare this page with related accelerators rather than assuming that a higher core count alone guarantees a better training or inference result.
Top GPUs for Training and Inference
| Category | Rank 1 | Rank 2 | Rank 3 |
|---|---|---|---|
| Best for Training | NVIDIA H200 | NVIDIA H100 | NVIDIA B200 |
| Best for Inference | NVIDIA A40 | NVIDIA A100 | NVIDIA A10 |
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