GPU Compute for Model Training and Fine-tuning
Run a representative training pilot, measure memory and step time, then plan the full run.
Who is this for?
For ML teams, research groups and enterprises adapting models to a domain, training vision models or planning larger distributed experiments. Bring an existing training pipeline or specify the setup assistance you need.
Typical workloads
- Parameter-efficient adaptation: LoRA and QLoRA experiments with evaluation.
- Supervised training: vision, language or multimodal models and dataset iterations.
- Distributed runs: larger batches or models, checkpointing and recovery tests.
Recommended GPUs to evaluate
- L40S: assess smaller fine-tuning jobs when supported precision and memory fit. A100 or H100: shortlist for larger training runs.
- H200: consider for memory-heavy workloads. For multi-GPU jobs, quote the complete topology, CPU, storage and interconnect, not just the GPU name.
Final selection depends on your software, memory budget and pilot results. Capacity and the full configuration are confirmed in the quote.
Hugging Face PEFT — LoRA ↗How many GPUs should I start with?
Start a small LoRA or training feasibility test on one GPU if it fits. Evaluate two to four GPUs when measured memory or runtime requires it; an eight-GPU node or multi-node cluster follows a validated scaling test. Full-model training may need a different baseline.
How does the pilot work?
Scope: fix the model, method, sequence length, micro-batch size, dataset sample and evaluation metric.
Agree: confirm framework/container compatibility, provider, data location, checkpoint storage, duration and the AUD quote before activation.
Validate: measure peak memory and step time, complete an evaluation and checkpoint restore, then estimate full-run cost. You decide whether to extend under a new or agreed quote.
Agree the scope, duration, acceptance criteria and price in advance. A pilot is not a free-trial offer. No charge before you accept the quote; the infrastructure provider is disclosed before activation.
How do I request a quote?
Send the base model, training method, dataset size, sequence length, steps or epochs, framework, GPU count if known, region and deadline. Specify whether you need full training or fine-tuning in the form.
Submit the enquiry form. Arvica reviews compatibility and capacity, then provides an AUD quote for you to accept before activation. English and Chinese assistance is available during Australia/APAC business hours.
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