Part of Open-Source AI
On-prem LLM deployment
Private LLM platforms in your data centre or VPC with access control, monitoring and upgrade paths.
In one paragraph
On-prem LLM deployment: Private LLM platforms in your data centre or VPC with access control, monitoring and upgrade paths. Open-source AI services deploy and adapt models from Hugging Face and the wider ecosystem (Llama, Mistral, Qwen, Whisper and others) on your own infrastructure. We fine-tune with LoRA, quantise for cheaper inference, optimise GPUs and deliver on-prem, private-cloud and edge deployments for organisations that need data residency, cost control at scale or offline operation.
How on-prem llm deployment fits into open-source ai
Data that cannot leave your boundary
Run LLMs and RAG entirely inside your VPC or data centre for regulated or sensitive workloads.
Cut inference cost at scale
Replace high-volume hosted calls with quantised open models on your own GPUs.
Offline and edge operation
Field, factory and vehicle deployments that work without connectivity.
Domain-specialised models
Fine-tune open models for your vocabulary, formats and tasks.
How we deliver
- 1
Requirements and model shortlist
Clarify data, latency, quality and hardware constraints; shortlist open models to test.
- 2
Evaluation
Benchmark shortlisted models on your tasks against a hosted baseline, including quantised variants.
- 3
Fine-tune and optimise
LoRA or full fine-tuning where it helps; quantisation and serving optimisation for cost.
- 4
Deploy privately
Serve with vLLM or TGI on your GPUs, VPC or edge devices behind a secure, compatible API.
- 5
Operate
Monitoring, upgrades and capacity planning as models and usage evolve.
Frequently asked
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.
Related work
Outcomes, not demos.
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