[{"data":1,"prerenderedAt":180},["ShallowReactive",2],{"svc-detail-open-source-ai":3,"sub-work-open-source-ai":142,"ill-services\u002Fopen-source-ai":179},{"slug":4,"short":5,"title":6,"tagline":7,"answer":8,"summary":9,"icon":10,"subServices":11,"useCases":57,"benefits":73,"process":79,"tools":95,"industries":110,"faq":116,"relatedWork":132,"relatedServices":134,"seo":139},"open-source-ai","Open-Source AI","Hugging Face & Open-Source AI services","Llama, Mistral, Qwen and friends, deployed privately, tuned to your needs.","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.","Open-source LLM deployment, Hugging Face fine-tuning, LoRA training, quantisation, GPU optimisation, local and edge AI, and private AI infrastructure, for teams that need control over data, cost and models.","cube",[12,16,20,24,28,32,36,40,45,49,53],{"slug":13,"name":14,"text":15},"open-source-llm-deployment","Open-source LLM deployment","Serve Llama, Mistral, Qwen and other open models with vLLM or TGI behind an OpenAI-compatible API.",{"slug":17,"name":18,"text":19},"hugging-face-fine-tuning","Fine-tuning Hugging Face models","Adapt language, vision and speech models from the Hub to your domain and tasks with measured gains.",{"slug":21,"name":22,"text":23},"lora-training","LoRA & QLoRA training","Parameter-efficient fine-tuning that adapts large models on modest GPUs without full retraining.",{"slug":25,"name":26,"text":27},"model-quantisation","Model quantisation","GPTQ, AWQ and GGUF quantisation to cut memory and cost while keeping quality within agreed limits.",{"slug":29,"name":30,"text":31},"local-ai-deployment","Local AI deployment","Run models on workstations, servers and laptops with Ollama, llama.cpp or containers; no data leaves the machine.",{"slug":33,"name":34,"text":35},"edge-ai","Edge AI","Optimised models on Jetson, mobile and embedded hardware for real-time, offline inference.",{"slug":37,"name":38,"text":39},"gpu-optimisation","GPU optimisation","Batching, KV caching, tensor parallelism and scheduling to get more throughput per GPU dollar.",{"slug":41,"name":42,"text":43,"page":44},"on-prem-llm-deployment","On-prem LLM deployment","Private LLM platforms in your data centre or VPC with access control, monitoring and upgrade paths.",true,{"slug":46,"name":47,"text":48},"private-ai-infrastructure","Private AI infrastructure","End-to-end private stacks: model serving, vector store, gateway and observability under your control.",{"slug":50,"name":51,"text":52},"open-source-model-evaluation","Open-source model evaluation","Side-by-side testing of open and hosted models on your tasks for quality, latency and cost.",{"slug":54,"name":55,"text":56},"speech-vision-open-models","Open speech & vision models","Whisper, open TTS, CLIP and detection models deployed and tuned for private use.",[58,61,64,67,70],{"title":59,"text":60},"Data that cannot leave your boundary","Run LLMs and RAG entirely inside your VPC or data centre for regulated or sensitive workloads.",{"title":62,"text":63},"Cut inference cost at scale","Replace high-volume hosted calls with quantised open models on your own GPUs.",{"title":65,"text":66},"Offline and edge operation","Field, factory and vehicle deployments that work without connectivity.",{"title":68,"text":69},"Domain-specialised models","Fine-tune open models for your vocabulary, formats and tasks.",{"title":71,"text":72},"Vendor independence","Keep the option to move between providers and models without rewriting your product.",[74,75,76,77,78],"Full control over data, models and cost","Lower per-token cost at high volume","Works offline and at the edge","No dependency on a single vendor","Quality measured against hosted models on your tasks",[80,83,86,89,92],{"step":81,"text":82},"Requirements and model shortlist","Clarify data, latency, quality and hardware constraints; shortlist open models to test.",{"step":84,"text":85},"Evaluation","Benchmark shortlisted models on your tasks against a hosted baseline, including quantised variants.",{"step":87,"text":88},"Fine-tune and optimise","LoRA or full fine-tuning where it helps; quantisation and serving optimisation for cost.",{"step":90,"text":91},"Deploy privately","Serve with vLLM or TGI on your GPUs, VPC or edge devices behind a secure, compatible API.",{"step":93,"text":94},"Operate","Monitoring, upgrades and capacity planning as models and usage evolve.",[96,97,98,99,100,101,102,103,104,105,106,107,108,109],"Hugging Face","vLLM","Text Generation Inference","Ollama","llama.cpp","PEFT","PyTorch","NVIDIA CUDA","TensorRT-LLM","Kubernetes","Docker","AWS","Azure","GCP",[111,112,113,114,115],"finance","healthcare","saas-technology","manufacturing","professional-services",[117,120,123,126,129],{"q":118,"a":119},"Are open-source models good enough compared with GPT or Claude?","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.",{"q":121,"a":122},"What hardware do we need to run an LLM on-prem?","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.",{"q":124,"a":125},"What does quantisation cost in quality?","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.",{"q":127,"a":128},"How do you keep private deployments secure?","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.",{"q":130,"a":131},"Can you fine-tune with our data safely?","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.",[133],"enterprise-rag-knowledge-base",[135,136,137,138],"ai-fine-tuning","mlops-ai-infrastructure","rag-systems","ai-integration",{"title":140,"description":141},"Open-Source AI & On-Prem LLM Deployment | AIYatch","Hugging Face and open-source AI services: Llama and Mistral deployment, LoRA fine-tuning, quantisation, GPU optimisation, edge and on-prem private AI.",[143,161],{"path":144,"title":145,"description":146,"industry":147,"services":148,"results":149,"cover":159,"coverAlt":160},"\u002Fwork\u002Fon-prem-llm-regulated-insurer","On-premises open-source LLM platform for a regulated insurer","A private LLM stack (open-weight models, GPU serving, guardrails and observability) deployed inside the client's data centre so no policy data leaves their network.","Finance & Fintech",[4,136,137],[150,153,156],{"value":151,"label":152},"0","documents or prompts leaving the network",{"value":154,"label":155},"~1,200","internal users onboarded in the first quarter",{"value":157,"label":158},"~70%","lower cost per 1M tokens vs. the API quote","\u002Fcovers\u002Fwork\u002Fon-prem-llm-regulated-insurer.svg","Abstract navy cover with a rising cyan course line",{"path":162,"title":163,"description":164,"industry":165,"services":166,"results":168,"cover":178,"coverAlt":160},"\u002Fwork\u002Flegal-contract-extraction-fine-tuning","Fine-tuned LLM for clause extraction from commercial contracts","A domain-adapted model that pulls parties, dates, obligations and unusual clauses out of contracts with accuracy a general-purpose model could not reach at the same cost.","SaaS & Technology",[135,167,4],"nlp",[169,172,175],{"value":170,"label":171},"0.91 → 0.96","field-level F1 vs. the prompted frontier model",{"value":173,"label":174},"~85%","lower inference cost per contract",{"value":176,"label":177},"40+","clause types extracted","\u002Fcovers\u002Fwork\u002Flegal-contract-extraction-fine-tuning.svg","\u003Csvg xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"0 0 640 480\" role=\"img\" aria-hidden=\"true\">\u003Cg fill=\"none\" stroke=\"currentColor\" stroke-width=\"5\" stroke-linecap=\"round\" stroke-linejoin=\"round\">\u003Cpath d=\"M176 170L320 90L464 170\"\u002F>\u003Cpath d=\"M190 158V390H450V158\"\u002F>\u003Crect x=\"270\" y=\"225\" width=\"100\" height=\"100\" rx=\"6\"\u002F>\u003Crect x=\"298\" y=\"253\" width=\"44\" height=\"44\" rx=\"3\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"295\" y1=\"225\" x2=\"295\" y2=\"217\"\u002F>\u003Cline x1=\"295\" y1=\"325\" x2=\"295\" y2=\"333\"\u002F>\u003Cline x1=\"270\" y1=\"250\" x2=\"262\" y2=\"250\"\u002F>\u003Cline x1=\"370\" y1=\"250\" x2=\"378\" y2=\"250\"\u002F>\u003Cline x1=\"320\" y1=\"225\" x2=\"320\" y2=\"217\"\u002F>\u003Cline x1=\"320\" y1=\"325\" x2=\"320\" y2=\"333\"\u002F>\u003Cline x1=\"270\" y1=\"275\" x2=\"262\" y2=\"275\"\u002F>\u003Cline x1=\"370\" y1=\"275\" x2=\"378\" y2=\"275\"\u002F>\u003Cline x1=\"345\" y1=\"225\" x2=\"345\" y2=\"217\"\u002F>\u003Cline x1=\"345\" y1=\"325\" x2=\"345\" y2=\"333\"\u002F>\u003Cline x1=\"270\" y1=\"300\" x2=\"262\" y2=\"300\"\u002F>\u003Cline x1=\"370\" y1=\"300\" x2=\"378\" y2=\"300\"\u002F>\u003Ccircle cx=\"320\" cy=\"275\" r=\"8\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003Cline x1=\"96\" y1=\"150\" x2=\"96\" y2=\"350\" stroke-width=\"3.5\"\u002F>\u003Cpath d=\"M96 180C96 220 140 210 140 250C140 290 96 280 96 320\" stroke-width=\"3.5\"\u002F>\u003Ccircle cx=\"96\" cy=\"180\" r=\"7\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003Ccircle cx=\"140\" cy=\"250\" r=\"7\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003Ccircle cx=\"96\" cy=\"320\" r=\"7\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003Cg stroke-width=\"3.5\">\u003Crect x=\"530\" y=\"228\" width=\"44\" height=\"35.2\" rx=\"8\"\u002F>\u003Cpath d=\"M537.9 228A14.1 14.1 0 0 1 566.1 228\"\u002F>\u003Cline x1=\"537.9\" y1=\"228\" x2=\"537.9\" y2=\"226\"\u002F>\u003Cline x1=\"566.1\" y1=\"228\" x2=\"566.1\" y2=\"226\"\u002F>\u003Ccircle cx=\"552\" cy=\"245.6\" r=\"4\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>",1792973685943]