GPU Cloud · NVIDIA · Turing

Nuomokite NVIDIA Q RTX 4000 GPU per valandą

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.

Specifikacijos

VRAM kiekvienam GPU

8 GB

Architektūra

NVIDIA Turing

Compute capability

7.5

FP32 skaičiavimas

≈ 7.7 TFLOPS

Atminties pralaidumas

≈ 353 GB/s

Galios riba

125 W

Infrastruktūros teikėjo pateiktos reikšmės dabar siūlomoms mašinoms; tikrasis našumas skiriasi priklausomai nuo mašinos.

Turimos konfigūracijos

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Kam tinka Q RTX 4000

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.

Ar tai tinkamas GPU jums?

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.

Palyginti su panašiais GPU

GPUVRAM kiekvienam GPUArchitektūraFP32 skaičiavimasNuo / valandąIšsamiau
Q RTX 40008 GBTuring≈ 7.7 TFLOPS—
RTX 20708 GBTuring≈ 7.7 TFLOPS—Išsamiau
GTX 10808 GBPascal≈ 7.8 TFLOPS—Išsamiau
RTX 2060S8 GBTuring≈ 7.5 TFLOPS—Išsamiau

Dažniausiai užduodami klausimai

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.

Nuomokite Q RTX 4000 valandomis

Sukurkite paskyrą, pasirinkite konfigūraciją ir prisijunkite per SSH. Mokėjimas nutraukiamas, kai ištrinate GPU.

Nuomoti Q RTX 4000

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