Is the 40 GB A100 SXM4 suitable for LLM inference and fine-tuning?

Yes, its 40 GB of VRAM and about 1313 GB/s of memory bandwidth make it well-suited for serving and parameter-efficient fine-tuning of medium-sized language models. You can deploy models quickly using the provided vLLM, Ollama, or PyTorch/CUDA templates.

How does this 40 GB SXM4 model compare to other datacenter GPUs?

The SXM4 variant operates at a 400 W thermal design power, offering sustained datacenter performance on the Ampere architecture with compute capability 8.0. If your workload requires more than 40 GB of memory to avoid model sharding, consider stepping up to a higher-capacity Ampere or newer generation datacenter GPU.

How does billing work for the A100 SXM4 on Cloudj?

Instances are billed on a per-hour basis while your rental is active. This allows you to run intensive training or batch inference jobs and terminate the instance when finished.

How do I connect to and set up my A100 SXM4 instance?

You get direct SSH access to the machine as well as ready-to-use templates for PyTorch/CUDA, vLLM, Ollama, and ComfyUI. Simply select your preferred template at launch to start working immediately.

GPU Cloud · NVIDIA · Ampere

Rent NVIDIA A100 SXM4 GPU by the hour

The NVIDIA A100 SXM4 is an Ampere-architecture datacenter GPU equipped with 40 GB of VRAM and a 400 W power envelope. It delivers about 1313 GB/s of memory bandwidth and about 15.6 TFLOPS of single-precision compute at compute capability 8.0. Rent it on Cloudj with per-hour billing, direct SSH access, and ready templates including PyTorch/CUDA, vLLM, Ollama, and ComfyUI.

Rent A100 SXM4 All GPUs

Specifications

VRAM per GPU

40 GB

Architecture

NVIDIA Ampere

Compute capability

FP32 compute

≈ 15.6 TFLOPS

Memory bandwidth

≈ 1313 GB/s

Power limit

400 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 A100 SXM4 is good for

LLM Inference and Serving

Run language models that fit within 40 GB of VRAM using pre-configured vLLM or Ollama templates with high memory throughput.

Deep Learning Training

Train and fine-tune neural networks in PyTorch/CUDA on an Ampere datacenter architecture with about 1313 GB/s of memory bandwidth.

Batch Image Generation

Generate high-resolution diffusion outputs and run complex workflows in ComfyUI without hitting consumer-grade memory limits.

Scientific Computing

Execute memory-intensive simulations and data processing workloads that benefit from compute capability 8.0 and SXM4 form-factor power limits.

Is it the right GPU for you?

Choose the 40 GB A100 SXM4 when your training or inference workloads exceed consumer GPU memory limits and require datacenter memory bandwidth. Opt for a higher-VRAM datacenter GPU if your unquantized models exceed 40 GB, or select a smaller workstation or consumer card for lightweight inference and development.

Compare with similar GPUs

GPU	VRAM per GPU	FP32 compute	Power limit
NVIDIA A100 SXM4	40 GB	≈ 15.6 TFLOPS	400 W
NVIDIA A100 PCIE	40 GB	≈ 15.6 TFLOPS	250 W	Details →
NVIDIA A40	45 GB	≈ 29.9 TFLOPS	300 W	Details →
NVIDIA L40S	45 GB	≈ 73.3 TFLOPS	350 W	Details →
NVIDIA Tesla P40	24 GB	≈ 9.4 TFLOPS	250 W	Details →

Frequently asked questions

Rent the A100 SXM4 by the hour

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

Rent A100 SXM4 All GPUs