Închiriați GPU NVIDIA RTX 3080 pe oră
The NVIDIA RTX 3080 is an Ampere-architecture consumer GPU equipped with 10 GB of VRAM. It delivers about 29.5 TFLOPS of standard floating-point compute and a memory bandwidth of about 656 GB/s. On ONIE, you can deploy this GPU with per-hour billing, SSH access, and ready templates.
Specificații
VRAM per GPU
Arhitectură
Capacitate de calcul
Calcul FP32
Lățime de bandă a memoriei
Limită de putere
Valorile sunt raportate de furnizorul de infrastructură pentru mașinile listate în prezent; performanța reală variază de la o mașină la alta.
Configurații disponibile
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Pentru ce este potrivit RTX 3080
Compact LLM Inference
Run quantized language models that fit within 10 GB of VRAM using pre-configured Ollama or vLLM templates.
Image Generation Workflows
Execute diffusion pipelines in ComfyUI leveraging about 656 GB/s of memory bandwidth for steady image output.
PyTorch Model Prototyping
Develop and test deep learning code on an Ampere card with compute capability 8.6 before scaling up.
CUDA Compute Tasks
Accelerate custom workloads that utilize about 29.5 TFLOPS of standard floating-point performance.
Este GPU-ul potrivit pentru dumneavoastră?
Choose the RTX 3080 for prototyping, image generation, and small-scale inference where 10 GB of VRAM and strong Ampere compute are sufficient. Opt for a GPU with more memory if your models or batch sizes exceed 10 GB, or select a lower-spec consumer card for lightweight scripts that do not require about 29.5 TFLOPS.
Comparați cu GPU-uri similare
Întrebări frecvente
Is the RTX 3080 suitable for LLM inference and image generation?
Yes, its 10 GB of VRAM and about 656 GB/s of memory bandwidth handle standard image generation in ComfyUI and quantized language models via Ollama or vLLM. Larger unquantized models will require a GPU with a higher memory capacity.
What software templates are available for the RTX 3080 on ONIE?
You can launch instances with ready templates for PyTorch/CUDA, vLLM, Ollama, and ComfyUI. The card supports compute capability 8.6 for broad compatibility with modern machine learning frameworks.
When should I step up from the RTX 3080 to a larger GPU?
The RTX 3080 provides strong consumer-class compute at about 29.5 TFLOPS within a 380 W power envelope, but it is limited to 10 GB of VRAM. If your training batches or model weights cause out-of-memory errors, a workstation or datacenter GPU with a larger memory buffer is the appropriate choice.
How do billing and access work on ONIE?
Instances are billed on a per-hour basis while active. You receive full SSH access to manage your environment alongside any web interfaces provided by your chosen template.
Închiriați RTX 3080 pe oră
Creați un cont, alegeți o configurație și conectați-vă prin SSH. Facturarea se oprește când ștergeți GPU-ul.
Închiriați RTX 3080