Hugging Face & Open-Source AI services

For many focused tasks (classification, extraction, RAG answers, summarisation) current open models are close enough, especially after light fine-tuning. For the hardest reasoning tasks hosted frontier models still lead. We run a side-by-side test on your workload so the decision is based on numbers, not opinion.

It depends on model size and concurrency. A quantised 7–8B model runs well on a single mid-range GPU; 70B-class models need multiple high-memory GPUs. We size hardware from your expected load and often start on rented GPUs before you buy. Serving frameworks such as vLLM raise throughput considerably through batching, so a single GPU often serves more concurrent users than teams expect.

Usually little at 8-bit and modest at 4-bit for most tasks, with large savings in memory and cost. We measure quality before and after on your evaluation set and only ship quantised models when the difference is within the limits you agree.

Isolated networking, authentication on the model gateway, encrypted storage, audit logging and no outbound calls from the serving stack. We also pin model versions and verify checksums so what runs is what was tested. Access to the model API is logged per user and application, which also gives you the usage data needed for capacity planning and chargeback.

Yes. Training runs on your infrastructure or in an isolated environment we control, data is scoped and deleted per agreement, and resulting weights belong to you. We document exactly what data was used and how. Fine-tuning data is reviewed for PII and sensitive content before training, and we can apply redaction or synthetic substitution where a field must not be memorised.