GPU Compute
Dedicated NVIDIA GPU infrastructure for AI training, inference, and rendering, configured around your workload rather than a fixed instance menu.
Choose your GPU
Every build is provisioned to spec. GPU count, host CPU and RAM, and storage are all tailored to your workload while we scope the deployment.
The gold standard for large-scale LLM training and high-throughput inference.
Request a quoteA proven workhorse for training, fine-tuning, and general HPC workloads.
Request a quoteOptimized for inference, graphics, and mixed AI/rendering pipelines.
Request a quoteCost-efficient GPU power for fine-tuning, rendering, and dev/test workloads.
Request a quoteCompare GPU models
A side-by-side view of what each card is built for. Final builds, including GPU count, host CPU and RAM, and storage, are configured to your workload.
| Spec | NVIDIA H100 | NVIDIA A100 | NVIDIA L40S | NVIDIA RTX 4090 |
|---|---|---|---|---|
| VRAM | 80GB HBM3 | 40GB / 80GB HBM2e | 48GB GDDR6 | 24GB GDDR6X |
| Ideal workload | Large-scale LLM training & high-throughput inference | Training, fine-tuning & general HPC | Inference, graphics & mixed AI/rendering pipelines | Fine-tuning, rendering & dev/test workloads |
| Interconnect | High-speed multi-GPU interconnect available | High-speed multi-GPU interconnect available | Multi-GPU configurations available | Single- and multi-card configurations available |
Built for serious AI and rendering workloads
Enterprise-grade infrastructure underpinning every GPU deployment.
No hypervisor sits between your workload and the hardware, which eliminates virtualization overhead and tenant contention for compute or memory bandwidth.
From short-term burst capacity to long-term reserved deployments, terms are structured around your project timeline, not a rigid contract.
Multi-GPU builds are networked for fast GPU-to-GPU communication, keeping distributed training and multi-card inference from bottlenecking on I/O.
GPU builds deploy into any metro in our footprint, with the same private networking and DDoS protection as the rest of our fleet.
What runs on Parallax GPU Compute
From research to production, dedicated GPU access removes the variability of shared-tenant performance.
Fine-tune open-weight language models on dedicated, uncontended GPU memory and compute.
Serve production inference traffic with consistent latency, free from other tenants competing for the same card.
GPU-accelerated rendering pipelines for studios and creators who need raw compute on demand.
Simulation, modeling, and research workloads that need dedicated double- or single-precision throughput.
Train and evaluate vision models against large image and video datasets without shared-tenant slowdowns.
Request a GPU quote
GPU builds depend on hardware availability, term length, and configuration, so every request is scoped individually. Submit your requirements and our team will follow up with options.
Every build is scoped individually around GPU model, quantity, host configuration, and term length.