Is the 16 GB Tesla V100 suitable for LLM inference and fine-tuning?

The 16 GB of VRAM and about 780 GB/s of memory bandwidth make the Tesla V100 suitable for inference on smaller or quantized language models and parameter-efficient fine-tuning. For larger models that do not fit into 16 GB of memory, you should select a GPU with a higher VRAM capacity.

What software templates and connection methods are available on Cloudj?

Every Tesla V100 instance includes direct SSH access for custom configuration and command-line workflows. You can also launch ready templates including PyTorch/CUDA, vLLM, Ollama, and ComfyUI to start working immediately.

How does the Volta architecture affect framework compatibility?

The Tesla V100 supports CUDA compute capability 7.0, which is compatible with standard deep learning frameworks like PyTorch. However, workloads that explicitly require features introduced in newer compute capabilities will need a more recent GPU architecture.

How does billing work for renting the Tesla V100?

Cloudj uses straightforward per-hour billing for all Tesla V100 rentals. You only pay for the time your instance is active, allowing you to run experiments or batch jobs and terminate the server when finished.

GPU Cloud · NVIDIA · Volta

Rent NVIDIA Tesla V100 GPU by the hour

The NVIDIA Tesla V100 is a Volta-architecture datacenter GPU equipped with 16 GB of high-bandwidth VRAM and a 300 W thermal design power. It delivers about 12.5 TFLOPS of single-precision compute and about 780 GB/s of memory bandwidth, making it a dependable option for memory-intensive scientific computing and deep learning tasks. On Cloudj, you can deploy the Tesla V100 with per-hour billing, full SSH access, and pre-configured templates.

Rent Tesla V100 All GPUs

Specifications

VRAM per GPU

16 GB

Architecture

NVIDIA Volta

Compute capability

FP32 compute

≈ 12.5 TFLOPS

Memory bandwidth

≈ 780 GB/s

Power limit

300 W

Values reported by the infrastructure provider for machines currently listed; real performance varies by machine.

Available configurations

Live stock and prices, updated continuously. The final price is confirmed before you order.

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What the Tesla V100 is good for

Compact LLM Inference

Serve quantized or smaller language models using Ollama or vLLM templates while taking advantage of about 780 GB/s of memory bandwidth.

Deep Learning Prototyping

Train and validate neural network architectures in PyTorch with 16 GB of VRAM and compute capability 7.0.

Image Generation Workflows

Run diffusion pipelines and custom generation graphs through pre-installed ComfyUI templates.

Scientific Simulation and Compute

Execute bandwidth-bound datacenter workloads that benefit from Volta architecture and about 12.5 TFLOPS of single-precision performance.

Is it the right GPU for you?

The 16 GB Tesla V100 suits researchers and developers who need high memory bandwidth for small-to-medium models and scientific workloads without paying for newer datacenter hardware. Choose a smaller consumer GPU for basic development with lower memory needs, or step up to a newer generation GPU with larger VRAM if your models exceed 16 GB or require newer architectural features beyond compute capability 7.0.

Compare with similar GPUs

GPU	VRAM per GPU	FP32 compute	Power limit
NVIDIA Tesla V100	16 GB	≈ 12.5 TFLOPS	300 W
NVIDIA L4	22 GB	≈ 24.2 TFLOPS	72 W	Details →
NVIDIA Tesla P40	24 GB	≈ 9.4 TFLOPS	250 W	Details →
NVIDIA A100 PCIE	40 GB	≈ 15.6 TFLOPS	250 W	Details →
NVIDIA A100 SXM4	40 GB	≈ 15.6 TFLOPS	400 W	Details →

Frequently asked questions

Rent the Tesla V100 by the hour

Create an account, choose a configuration and connect over SSH. Billing stops when you delete the GPU.

Rent Tesla V100 All GPUs