Nuomokite NVIDIA RTX 3060 GPU per valandą
The NVIDIA RTX 3060 is a consumer-grade Ampere architecture GPU equipped with 12 GB of VRAM and a 170 W power limit. It delivers about 12.0 TFLOPS of standard floating-point performance and a memory bandwidth of about 318 GB/s, supporting CUDA compute capability 8.6. On ONIE, you can rent the RTX 3060 with per-hour billing, full SSH access, and ready templates for PyTorch/CUDA, vLLM, Ollama, and ComfyUI.
Specifikacijos
VRAM kiekvienam GPU
Architektūra
Compute capability
FP32 skaičiavimas
Atminties pralaidumas
Galios riba
Infrastruktūros teikėjo pateiktos reikšmės dabar siūlomoms mašinoms; tikrasis našumas skiriasi priklausomai nuo mašinos.
Turimos konfigūracijos
Aktualus prieinamumas ir kainos, atnaujinami nuolat. Galutinę kainą patvirtinsite prieš užsakymą.
Įkeliamos aktualios kainos…
Kam tinka RTX 3060
Quantized LLM Inference
Run compact or quantized language models within the 12 GB VRAM envelope using pre-configured Ollama or vLLM templates.
Image Generation with ComfyUI
Execute standard diffusion workflows and image synthesis pipelines that comfortably fit inside 12 GB of memory.
PyTorch Prototyping and Debugging
Develop and test CUDA code on an Ampere card with compute capability 8.6 before scaling workloads to larger hardware.
Small-Scale Model Fine-Tuning
Perform parameter-efficient fine-tuning on modest datasets where about 12.0 TFLOPS of compute is sufficient.
Ar tai tinkamas GPU jums?
Choose the RTX 3060 for development, prototyping, and lightweight inference tasks that require 12 GB of VRAM without the expense of datacenter hardware. Opt for a larger GPU if your models exceed 12 GB or require higher memory bandwidth than about 318 GB/s, or a smaller GPU if you only run minimal scripts with lower memory demands.
Palyginti su panašiais GPU
Dažniausiai užduodami klausimai
Is the RTX 3060 suitable for LLM inference and image generation?
Yes, the 12 GB of VRAM is well-suited for running smaller or quantized language models via Ollama and vLLM, as well as standard diffusion models in ComfyUI. For larger unquantized models that exceed 12 GB, you will need a GPU with a higher memory capacity.
How does this consumer Ampere card compare to larger GPUs?
The RTX 3060 provides a generous 12 GB memory buffer for an entry-level consumer card, alongside about 12.0 TFLOPS of compute and about 318 GB/s of memory bandwidth. Stepping up to higher-tier consumer or workstation GPUs provides faster memory bandwidth and more compute throughput for heavy training.
How do I connect to and set up my RTX 3060 instance?
Every rental on ONIE includes direct SSH access for full command-line control. You can also launch immediately using ready templates for PyTorch/CUDA, vLLM, Ollama, or ComfyUI.
How does billing work for the RTX 3060 on ONIE?
Instances are billed on a per-hour basis while your rental is active. This allows you to run experiments on an Ampere architecture card with compute capability 8.6 and terminate the instance when finished.
Nuomokite RTX 3060 valandomis
Sukurkite paskyrą, pasirinkite konfigūraciją ir prisijunkite per SSH. Mokėjimas nutraukiamas, kai ištrinate GPU.
Nuomoti RTX 3060