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, Mistral, Qwen, Phi 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 |
|---|---|---|---|
| Qwen3 27B | 27B dense | General / Chat | PROD READY |
| Gemma 4 | 26B A4B MoE | Fast / Apple Silicon | PROD READY |
| Qwen3-Coder / Devstral | 24–32B | Code Gen | PROD READY |
| Llama 4 Scout | 109B MoE · 17B active | Long Context / RAG | PROD READY |
| Phi-4 / gpt-oss | 14–20B | Low-VRAM Reasoning | PROD READY |
| DeepSeek V4 | Large MoE | Research / Reasoning | 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.
* Hosted API speed varies with model, load and region, and every request adds a network round-trip. Local figures are typical community-reported llama.cpp / Ollama results at Q4; we measure yours during the hardware audit.
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.