Llama 3 8B inference / SDXL / ComfyUI
- GPU sizing starting point
- RTX 4090 24GB
- What to validate
- Precision, context length, batch size and peak VRAM.
LUCKVM / GPU COMPUTE
Cloud GPU Server Rental: NVIDIA RTX 4090, A10, A100 & H100 (Hourly/Monthly)
Choose GPU capacity, memory and deployment region for image generation, model inference and training. Match your configuration to the workload you actually run.
CUDAPyTorchTensorFlowComfyUI
COMPUTE OPTIONS
Find the GPU for your workload
Start with the task and memory requirements, then compare software support, resources and billing.
IMAGE GENERATION & RENDERING
RTX 4090
24GB VRAMFor ComfyUI, Stable Diffusion, LoRA experiments and 3D rendering.
Image generation / Fine-tuningExplore configurationsPRODUCTION INFERENCENVIDIA A10 / A30
Data center GPUsFor persistent inference services, computer vision and enterprise AI applications.
Inference APIs / Computer visionExplore configurationsLARGE MODELS & DISTRIBUTED COMPUTENVIDIA A100 / H100
High-memory and multi-GPU optionsFor larger models, training and scientific computing. Check memory and interconnect requirements for your workload.
Model training / Scientific computingExplore configurationsGPU model, memory, GPU count, regional availability and billing depend on the selected product and order.
DEPLOYMENT CHECKLIST
Ready for your first production workload
A practical handover checklist, from sizing the instance to moving live traffic.
Budget VRAM and topology
Start with model size, precision, context and batch size. For multi-GPU jobs, verify device count, allocation and the actual interconnect.
Validate the software stack
Check driver, CUDA and framework compatibility. Run a small inference or rendering job before importing the full dataset.
Measure useful throughput
Record time to first token, tokens per second, peak VRAM or batch render time on representative inputs. Include retries in job estimates.
Plan persistence and billing
Confirm charges when stopped, released or retaining disks. Export models, workflow files and outputs before terminating the instance.
GPU workload and VRAM matcher
Start with your model and workload, then validate memory usage, concurrency and the delivered software environment.
70B inference / parameter-efficient tuning
- GPU sizing starting point
- A100 80GB / H100 80GB or multiple GPUs
- What to validate
- Quantization, KV cache and parallelism; 80GB does not guarantee every 70B workload fits.
70B full fine-tuning / distributed training
- GPU sizing starting point
- 8 × A100 80GB / H100 cluster sizing
- What to validate
- Optimizer state, checkpointing, CPU offload and actual GPU interconnect.
Hardware and allocation
Hardware reference: RTX 4090 24GB GDDR6X; RTX 3090 24GB; A10 24GB; A30 24GB; A100 40GB/80GB HBM2e; H100 80GB HBM3. Available models, allocation and topology depend on the selected configuration.
Software and delivery
Specify Ubuntu 22.04/24.04, NVIDIA driver, CUDA, cuDNN, PyTorch and TensorFlow versions when ordering. Verify the delivered image with nvidia-smi and a minimal inference run. The report’s RTX 4090 cost reference is $1.20/hour; confirm the actual hourly offer before estimating a job.
FROM ORDER TO LAUNCH
Plan your purchase and acceptance checks
Before ordering
Confirm resources, renewal costs, payment options and delivery timing. Request a product-specific test IP and check routes from your users’ networks.
After provisioning
Match CPU, RAM, storage, IPs and OS to your order. Verify login, remote management access and connectivity.
Before going live
Run a representative workload and test backup recovery. Save your results and open a support ticket if the delivered configuration differs.