NVIDIA RTX 4060
3,072
CUDA Cores
8GB
VRAM
272
GB/s
Technical Specifications
3,072
CUDA Cores
1830
Base MHz
2460
Boost MHz
8GB GDDR6
128-bit bus
Performance
15.1
FP32 TFLOPS
30.2
FP16 TFLOPS
115W
TDP
Cloud Availability
0
Available Instances
$0.00/hr
Starting Price
Detailed Specifications
| Architecture | Ada Lovelace (Unknown) |
| Release Date | 2023-06-29 |
| Launch Price | $299.00 |
| Process | 5nm |
| Transistors | 18.9B |
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 GPU
The NVIDIA RTX 4060 was introduced in 2023 on the Ada Lovelace architecture. Its specification combines 3,072 CUDA cores and 96 Tensor Cores with 8GB of GDDR6, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 8GB memory capacity, the RTX 4060 is best evaluated for compact inference, rendering, computer vision, and legacy CUDA workloads. Always size the model, optimizer state, KV cache, and framework overhead together rather than choosing a GPU from core count alone.
GPUvec does not currently have an active hourly cloud listing for the RTX 4060. This is not a zero-dollar offer: it means no comparable provider price is available in the present dataset. Check the provider directory for availability or compare a related GPU with an active listing.
For software planning, the RTX 4060 combines 272 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.
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
Complete Specifications for the NVIDIA RTX 4060
The NVIDIA RTX 4060 uses the Ada Lovelace architecture and combines 3,072 CUDA cores with 8GB of GDDR6. GPUvec records 272 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 Cloud Rental Prices per Hour
GPUvec currently has no comparable active hourly listing for this model; this means pricing is unavailable, not free. 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 the Right GPU for Your AI Workload?
Start with the 8GB memory limit, then evaluate 272 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 |
Compare GPU specifications and cloud instances to find the best GPU for your workload.