Private AI Deployments
For organisations where data cannot leave the building — healthcare, legal, finance, government. Full LLaMA stack on your servers.
Meta LLaMA
Meta LLaMA
Open-source LLMs running on your infrastructure
We deploy, fine-tune and serve Meta's LLaMA models on your own servers, VPC or private cloud — full control, no data leaving your infrastructure, no per-token API costs.
Self-hostedYour infrastructure
No data leavesFull privacy
Fine-tunableYour domain data
Models We Deploy
Every LLaMA model, for every use case
Most capable
LLaMA 3.1 405B
Meta's flagship open-source model — competitive with GPT-4 on most benchmarks. Used for complex reasoning and high-quality generation tasks.
Balanced
LLaMA 3.1 70B
Our most-deployed LLaMA model — excellent quality-to-cost ratio for production chat, RAG and content generation on a single A100 GPU.
Fast & lean
LLaMA 3.1 8B
Runs on a single consumer GPU — ideal for high-volume tasks, edge deployments and cost-sensitive workloads where 70B quality isn't required.
Multimodal
LLaMA 3.2 Vision
LLaMA's multimodal models (11B and 90B) that accept text and image inputs — for document understanding and visual AI tasks on-prem.
Code specialist
Code LLaMA
Meta's code-focused fine-tune — optimised for code generation, completion, debugging and code review tasks.
Safety
LLaMA Guard
Meta's content safety model — deployed alongside your LLaMA instance to filter harmful inputs and outputs in production.
What We Build
LLaMA applications for private AI
Cost-Optimised AI
Replace $10K+/month OpenAI bills with a self-hosted LLaMA deployment — amortised over time, no per-token costs.
Domain Fine-Tuning
Fine-tune LLaMA on your proprietary data — legal documents, medical records, product knowledge — for higher accuracy than generic models.
Private RAG Systems
Knowledge bases, document search and Q&A that never send your data to third-party APIs — fully private retrieval-augmented generation.
Edge & On-Prem AI
LLaMA 8B deployed on edge devices, manufacturing equipment or local servers — AI that works without internet connectivity.
Internal Tool AI
HR chatbots, IT helpdesks and internal knowledge tools — powered by LLaMA on your corporate network, never external.
What We Deliver
Every LLaMA deployment includes
✓Hardware spec and cloud instance sizing
✓Model download, quantisation and optimisation (GGUF, AWQ, GPTQ)
✓vLLM or Ollama serving setup with OpenAI-compatible API
✓GPU memory optimisation and batching configuration
✓Fine-tuning pipeline (LoRA / QLoRA) if required
✓RAG pipeline integration with your data sources
✓Load balancing and horizontal scaling setup
✓Monitoring, alerting and model health dashboards
FAQ
Common questions
What hardware do I need to run LLaMA?
LLaMA 8B runs on a single A10G (24GB VRAM). LLaMA 70B needs 4× A100 (80GB) or 2× H100. We spec the hardware based on your throughput requirements and budget.
Can you run LLaMA in our existing AWS/GCP/Azure account?
Yes — we deploy on your existing cloud account using GPU instances (AWS p4d, GCP A100, Azure NC). We don't need to access your account permanently.
How does self-hosted LLaMA compare in quality to GPT-4?
LLaMA 3.1 70B is within 10–15% of GPT-4 on most tasks. LLaMA 3.1 405B matches GPT-4 on many benchmarks. For domain-specific tasks with fine-tuning, it can exceed GPT-4.
Do you handle the fine-tuning or just the deployment?
Both — we can do deployment only, fine-tuning only, or the complete stack from hardware spec through to fine-tuned production model.
Want AI that never leaves your servers?
Tell us your infrastructure, data constraints and use case — we'll design the right LLaMA setup.
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