Event Laptop Fleet Kit

The amount of VRAM you need depends on the model size, precision format, and fine-tuning method. Full fine-tuning of a 70B-parameter model in FP16 can require 140 GB or more of VRAM, while techniques like LoRA and QLoRA significantly reduce that footprint — sometimes to under 48 GB.

For multi-GPU setups, frameworks like DeepSpeed ZeRO and FSDP allow you to shard model states across GPUs, distributing memory requirements efficiently.

Skorppio workstations come equipped with up to 384 GB of VRAM (4× A6000) or 768 GB (8× A6000) in server configurations, giving you room to fine-tune the largest open models without compromise.

Most Skorppio systems ship within 48 hours of order confirmation, and some configurations are available for next-day delivery depending on your location and inventory. We maintain ready-to-ship inventory specifically for AI and ML workloads so you’re not waiting weeks for provisioning.

Yes. Every Skorppio workstation and server ships with the full CUDA toolkit, compatible NVIDIA drivers, and a clean Ubuntu environment ready for your stack. PyTorch, Hugging Face Transformers, JAX, TensorFlow — all run natively. You get full root access, so you can install and configure anything you need without restrictions.

Our minimum rental period is one week. Monthly rentals are available at a reduced rate, and we offer flexible terms for longer engagements. Whether you need a system for a sprint, a quarter, or an ongoing project, we’ll match the term to your timeline.

Skorppio’s multi-GPU workstations use PCIe 5.0 x16 lanes, delivering up to 128 GB/s of bidirectional bandwidth per GPU. For distributed fine-tuning, frameworks like PyTorch DDP, FSDP, and DeepSpeed handle gradient synchronization and model sharding efficiently over PCIe — no NVLink required for most workloads.

PCIe-based systems are ideal for data-parallel training, LoRA/QLoRA fine-tuning, and inference pipelines where each GPU processes independently or shares lightweight updates. You get the multi-GPU benefit without the premium of NVLink for workloads that don’t require ultra-high inter-GPU bandwidth.