Najemite GPU NVIDIA Q RTX 4000 na uro
The NVIDIA Q RTX 4000 is a Turing-based workstation GPU available to rent on ONIE for entry-level AI and graphics workloads. It provides 8 GB of VRAM, about 7.7 TFLOPS of single-precision compute, and a memory bandwidth of about 353 GB/s within a 125 W power profile. Supporting CUDA compute capability 7.5, this instance can be deployed with ready templates for PyTorch/CUDA, vLLM, Ollama, and ComfyUI.
Specifikacije
VRAM na GPU
Arhitektura
Računska zmogljivost
Računska zmogljivost FP32
Pasovna širina pomnilnika
Omejitev moči
Vrednosti, ki jih za trenutno navedene stroje sporoča ponudnik infrastrukture; dejanska zmogljivost se razlikuje glede na stroj.
Razpoložljive konfiguracije
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Za kaj je Q RTX 4000 primeren
Code Prototyping and Debugging
Test and validate PyTorch/CUDA pipelines on an 8 GB workstation GPU before scaling up to larger hardware.
Compact Model Inference
Run small language models through Ollama or vLLM that fit comfortably within 8 GB of VRAM.
Basic Image Generation
Execute standard image generation workflows in ComfyUI that do not exceed the 8 GB memory limit.
Workstation Graphics and Rendering
Utilize the Turing architecture and about 353 GB/s of memory bandwidth for remote rendering and visualization tasks.
Ali je to pravi GPU za vas?
Choose the Q RTX 4000 for lightweight inference, rendering, and code development that fit inside 8 GB of VRAM and compute capability 7.5. Select a larger GPU with more memory and newer architecture for heavy model training or large-scale inference, or pick a smaller GPU for basic display or minimal compute tasks.
Primerjajte s podobnimi GPU-ji
| GPU | VRAM na GPU | Arhitektura | Računska zmogljivost FP32 | Od / uro | Podrobnosti |
|---|---|---|---|---|---|
| Q RTX 4000 | 8 GB | Turing | ≈ 7.7 TFLOPS | — | |
| RTX 2070 | 8 GB | Turing | ≈ 7.7 TFLOPS | — | Podrobnosti |
| GTX 1080 | 8 GB | Pascal | ≈ 7.8 TFLOPS | — | Podrobnosti |
| RTX 2060S | 8 GB | Turing | ≈ 7.5 TFLOPS | — | Podrobnosti |
Pogosta vprašanja
Is the Q RTX 4000 suitable for LLM inference and fine-tuning?
With 8 GB of VRAM and about 7.7 TFLOPS of single-precision performance, it is suitable for inference with compact models using Ollama or vLLM. For larger models or full fine-tuning workloads, you should choose a GPU with more VRAM and higher memory bandwidth than about 353 GB/s.
Can I use this GPU for ComfyUI and image generation?
Yes, ONIE offers a ready ComfyUI template that runs on this Turing workstation GPU. It handles standard generation pipelines that fit into 8 GB of memory, though larger pipelines will require a GPU with a bigger memory allocation.
How do I connect to the instance and what software is pre-installed?
You can connect directly to your running instance via SSH access. ONIE provides pre-configured templates including PyTorch/CUDA, vLLM, Ollama, and ComfyUI so you can start working immediately.
How does billing work for the Q RTX 4000 on ONIE?
Instances are billed on a per-hour basis while your workstation GPU is active. This allows you to run development or rendering sessions and terminate the instance when finished.
Najamite Q RTX 4000 po uri
Ustvarite račun, izberite konfiguracijo in se povežite prek SSH. Obračun se ustavi, ko izbrišete GPU.
Najamite Q RTX 4000