Deploy production-grade large language models locally — no API calls, no data leaks, no rate limits. Your infrastructure, your models, your rules.
A complete end-to-end stack — hardware audit, model selection, quantization, server deployment, and API interface. We handle the complexity; you get a fast, private LLM endpoint.
We profile your GPU/CPU, VRAM, RAM and storage. Match hardware to optimal models — no guesswork.
Curate from Llama 4, Qwen 3, GPT-OSS, DeepSeek, Mistral and others. Match capability to your use case — coding, RAG, chat, agents.
GGUF, AWQ, GPTQ — we compress models to fit your VRAM without sacrificing benchmark performance.
Ollama, llama.cpp, vLLM or custom stack. Production-hardened with monitoring, restarts, and load balancing.
OpenAI-compatible REST endpoint. Drop into any existing app with zero code changes. Done.
Full-stack installation of open-weight language models on your hardware. Includes model selection, quantization tuning, inference server setup, and an OpenAI-compatible API layer.
Connect your local LLM to your documents, databases, or knowledge bases. We architect and deploy a full Retrieval-Augmented Generation pipeline that stays entirely on-premise.
Custom LoRA / QLoRA fine-tuning on your own data. Adapt general models to your domain — legal, medical, customer support, code — with targeted training runs.
Turn your local LLM into an autonomous agent. Tool calling, multi-step reasoning, memory systems — all orchestrated on-premise with open-source frameworks.
We don't use one-size-fits-all tooling. Each deployment is architected around your hardware, use case, and performance targets. Here's what runs under the hood.
| MODEL | SIZE | BEST FOR | STATUS |
|---|---|---|---|
| Llama 4 Scout | 109B MoE Q4 | General / Long Context | PROD READY |
| Qwen 3 Coder | 30B MoE Q5 | Code Gen | PROD READY |
| GPT-OSS | 20B MXFP4 | Reasoning / Agents | PROD READY |
| Mistral Small 3.2 | 24B Q6 | RAG / Fast | PROD READY |
| DeepSeek R2 | 671B MoE | Research | HIGH VRAM |
Your prompts, your documents, your outputs — never leave your network. No third-party eyes on sensitive queries. GDPR-friendly by architecture.
API bills compound fast at scale. Local inference means fixed infrastructure cost. Heavy users often break even in weeks, profit for years.
Burst to thousands of requests. Run inference 24/7 with no throttling, no quotas, no service outages from provider-side issues.
First token in milliseconds, not seconds. Local inference eliminates network round-trips. Streaming feels instant on proper hardware.
Fine-tune, merge, quantize, jailbreak-proof. Closed APIs give you a black box. Local deployment means you own every parameter.
Healthcare (HIPAA), legal (attorney-client), finance (SOC2) — industries with data residency requirements can finally use LLMs.
* Cloud API speed varies by load. Local inference is deterministic and never throttled. Indicative figures — llama.cpp / Ollama, context 4096 tokens, greedy sampling. Updated July 2026.
Assumptions: hardware is a one-time cost · ~$35/month power & upkeep at typical duty cycle · deployment fee not included — scoped per project. Cloud figure excludes rate-limit workarounds, egress and compliance overhead.
Tell us about your hardware, your use case, and your timeline. The Jonestech team will scope a deployment and get back to you within 24 hours.