GPU Cluster Rental — For AI, ML and HPC Workloads
NVIDIA H100, A100 and L40S GPU cluster rental. Scalable GPU infrastructure for AI model training, LLM training, simulation and rendering workloads.
A GPU cluster is computing infrastructure built for high memory bandwidth and massive parallelism — the profile that artificial intelligence, deep learning, large language models (LLM) and parallel computing workloads demand. Mevasis provides enterprise GPU cluster rental in Türkiye.
Why a GPU Cluster?
Modern AI and scientific computing workloads need capacity beyond what traditional CPU-based infrastructure delivers:
- Parallel processing: GPUs run thousands of cores concurrently, accelerating matrix computation
- High memory bandwidth: HBM3 technology delivering 3+ TB/s of memory access
- NVLink interconnect: low-latency, high-bandwidth links between GPUs
- Tensor Core acceleration: hardware units purpose-built for AI/ML workloads
GPU Models and Use Cases
NVIDIA H100 SXM5 (80 GB HBM3)
The highest-performing data centre GPU currently available:
- 3.35 TB/s HBM3 memory bandwidth
- 4th Gen Tensor Core with FP8 support
- NVLink 4.0 — 900 GB/s GPU-to-GPU bandwidth
- Transformer Engine, purpose-built for LLMs
Ideal workloads: GPT-4 class LLM training, large-scale molecular dynamics, fluid dynamics simulation
NVIDIA A100 80 GB
A mature and cost-effective option on the Ampere architecture:
- 2 TB/s HBM2e memory bandwidth
- 3rd Gen NVLink — 600 GB/s
- Multi-Instance GPU (MIG): partitions into 7 independent instances
Ideal workloads: mid-scale LLM training, genomics analysis, CFD simulation
NVIDIA L40S (48 GB GDDR6)
A PCIe-based, more affordable option:
- GDDR6 memory — well suited to high-capacity inference
- Ada Lovelace architecture
- FP8 and FP16 Tensor Core support
Ideal workloads: LLM inference, render pipelines, mid-scale training
Cluster Configurations
| Configuration | GPU | Memory | NVLink | Use case |
|---|---|---|---|---|
| Entry (2 nodes) | 2× HGX H100 (16× H100) | 1.28 TB | Yes | LLM fine-tuning |
| Mid (4 nodes) | 4× HGX H100 (32× H100) | 2.56 TB | Yes | 30–70B model training |
| Large (8+ nodes) | 8+ HGX H100 (64+× H100) | 5+ TB | Yes | GPT-4 class training |
Interconnect Infrastructure
Cluster performance is set not only by GPU power but by how fast data moves between GPUs. In Mevasis GPU clusters:
- InfiniBand NDR400 (400 Gb/s): high-speed interconnect between GPU nodes
- NVLink 4.0: 900 GB/s between GPUs inside the same node
- RDMA: direct GPU-to-GPU memory transfer that bypasses the CPU
With this foundation, gradient synchronisation does not become the bottleneck in distributed training.
Software Environment
The software stack shipped with the cluster:
CUDA 12.x + cuDNN 9.x
NCCL 2.x (multi-GPU communication library)
PyTorch 2.x / TensorFlow 2.x
SLURM with GPU resource management (MIG, GPU partitioning)
Singularity/Apptainer container support
DCGM Exporter + Grafana GPU monitoring
Where It Is Used
AI companies: LLM pre-training and fine-tuning, diffusion model training, embedding model development
Universities and research: computational chemistry (DFT, MD), protein structure prediction, climate modelling
Pharma and biotech: genomics analysis, drug discovery, molecular simulation
CFD and engineering: GPU-accelerated fluid dynamics, structural analysis
To rent a Mevasis GPU cluster, request a quote or have our team assess your workload.
Frequently Asked Questions
NVIDIA H100 SXM5 (80GB), A100 80GB, L40S (48GB) and V100 are available. Fill in the quote form for current capacity.
Hourly, monthly and long-term contract options all exist. The hourly model is billed per GPU-hour consumed.
It depends on model size. A 7B-parameter model typically runs on 2–4x A100; a 70B+ model on 8–16x H100. The Mevasis team sizes the deployment by analysing your workload.
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