Local LLM Setup Service — Set Up for You

✓ Your data stays on your hardware ✓ Right-sized model & quantization ✓ OpenAI-compatible API ✓ Speed benchmark included

Quick answer

What is a local (self-hosted) LLM? 

A local LLM is an open-weight language model, such as Llama, Qwen, Mistral, Gemma, DeepSeek or OpenAI's gpt-oss, running on your own computer, server or private cloud instead of a vendor's API. Prompts and data never leave your environment.

What does this service include? 

Codersarts engineers check your hardware and use case, pick a model and quantization that fit, install Ollama (simple, single machine) or vLLM (high-throughput serving), enable the GPU, expose an OpenAI-compatible API, and benchmark speed. Optional extras: a chat interface (Open WebUI), authentication and RAG.

How long and how much? 

Starter from $99 (2 days, Ollama with one model on one machine). Standard $199 (3 days, GPU server with API and benchmark). Advanced $399 (5 days, production setup with auth, load balancing and chat UI).

Is this your problem?

• Company policy, clients or regulators don't allow sending data to external AI APIs

• Your OpenAI or Claude bills keep climbing as usage grows

• You installed a model locally, but it's painfully slow or crashes

• The GPU isn't being used, or the model doesn't fit in memory

• You don't know which model or quantization suits your hardware and task

• Your team wants a private ChatGPT-style interface, not command-line tools

Why DIY local LLM setups disappoint

• Wrong model size for the hardware. As a rule of thumb, weights need about 2 GB of VRAM per billion parameters at FP16 and about 0.5 GB at 4-bit, plus 15–20% overhead and KV cache for each concurrent user (RunPod GPU sizing guide).

• Wrong runtime for the job. Ollama is the simplest way to run models on one machine. vLLM is built for many concurrent users. In one benchmark on an H100 with 32 concurrent requests, vLLM delivered about 1,450 tokens/second against about 320 for Ollama (Spheron).

• GPU not detected. Driver, CUDA and runtime version mismatches silently fall back to the CPU.

• No API or access control. A model running in a terminal can't serve your apps or team securely.

• No benchmark. Without measuring tokens per second and quality on your tasks, you don't know if the setup is good enough.

We size, install, tune and benchmark, so the model is fast, private and usable by your apps and team.

Ollama or vLLM: which do we use?

Both expose OpenAI-compatible APIs, so your apps can switch between local and cloud models with minimal code changes. We can also use llama.cpp, LM Studio or SGLang when they fit better.

Which model fits your hardware? (guide)

OpenAI states that gpt-oss-20b runs with 16 GB of memory and gpt-oss-120b runs on a single 80 GB GPU, both released under the Apache 2.0 license (August 2025). Exact fit depends on context length, concurrency and quantization. We confirm with a benchmark.

What we do

1. Scope check: use case, data sensitivity, number of users, hardware or cloud budget.

2. Model selection: shortlist open-weight models for your task and language, and pick a quantization that fits your memory.

3. Install & configure: set up Ollama or vLLM, NVIDIA/AMD drivers and CUDA, or Apple Metal.

4. GPU tuning: confirm GPU use and tune context length, batch size and concurrency.

5. API: expose an OpenAI-compatible endpoint with API keys and network restrictions.

6. Benchmark: measure tokens/second, time-to-first-token and quality on your sample prompts.

7. Extras (per package): Open WebUI chat interface, authentication, load balancing, RAG connection.

8. Handover: a guide to updating models, restarting services and monitoring usage.

Packages

Add-ons: Priority delivery (+50%) · Extra model (+$29) · Chat UI (Open WebUI) (+$49) · RAG connection (+$79) · 30-min walkthrough call (+$25) · 14-day support (+$39)

Final scope and price are confirmed after a free 30-minute scope check. Hardware or cloud GPU costs are separate. We can recommend and set up a cloud GPU if you don't have one.

Use cases

Local LLM vs cloud AI API

Many teams use both: a local model for sensitive or high-volume work, and a cloud model for the hardest tasks. We can set up routing between them.

What you get

• A running local LLM on your hardware or private cloud

• An OpenAI-compatible API endpoint, secured (Standard+)

• A benchmark report: speed and quality on your prompts

• A chat interface for your team (Advanced or add-on)

• Setup, update and troubleshooting guides

• Proof of done: live demo of the model answering your prompts, with GPU usage shown

What we need from you

Required (the delivery clock starts once we have these):

• Access to the machine or server (SSH or remote session), or a cloud account

• Hardware details: GPU model and VRAM, RAM, OS

• Use case and 10–20 sample prompts

• Number of expected users or requests

• What success looks like (speed, quality, privacy)

Optional:

• Preferred models (Llama, Qwen, Mistral, Gemma, DeepSeek, gpt-oss)

• Network and security requirements (VPN, air-gapped)

• Whether you need an NDA

How it works

1. Free scope check (30 min): hardware, use case, runtime and package.

2. Install & tune: model, runtime, GPU and API setup.

3. Benchmark & prove: you see speed and quality on your prompts.

4. Review & revise: adjust within your package's revisions, then hand over.

Why Codersarts Tasks

• One accountable team: a managed Codersarts engineering team, not trial-and-error forum fixes.

• Right-sized, not oversized: model, quantization and runtime matched to your hardware and workload.

• Measured: every setup comes with a benchmark.

• Room to grow: add RAG, agents, fine-tuning or a full private AI platform later.

Related tasks

• Set Up CUDA GPU Environment: GPU not detected?

• Build RAG Chatbot on Your Docs: private Q&A over your files

• Reduce LLM API Cost: route some traffic to local models

• Integrate OpenAI or Claude API into Your App

• Fine-Tune a Small LLM

Bigger than one task? Plan a private AI platform →

FAQ

What does it cost to set up a local LLM? Setup starts at $99 for Ollama with one model on one machine. Standard ($199) sets up a secured GPU server with an API and benchmark, and Advanced ($399) adds production serving, a chat UI, auth and monitoring. Hardware or cloud GPU costs are separate.

What hardware do I need to run an LLM locally? It depends on model size and precision. A rough guide is about 2 GB of VRAM per billion parameters at FP16 and about 0.5 GB at 4-bit, plus overhead for context and users. A 16 GB machine can run ~7–8B models, or gpt-oss-20b, and an 80 GB GPU can run gpt-oss-120b.

Ollama or vLLM: which should I use? Ollama for simple setups on one machine, including Mac. vLLM for serving many users with higher throughput on GPU servers. Both offer OpenAI-compatible APIs.

Is a local LLM as good as ChatGPT or Claude? Leading open models are strong for many business tasks such as summarizing, extraction, Q&A and drafting, but frontier cloud models still lead on the hardest reasoning. We benchmark on your prompts so you can decide.

Is my data really private? Yes. With a local or private-cloud setup, prompts and documents are processed on your infrastructure. We add API keys, network restrictions and optional user accounts.

Can my existing apps use the local model? Usually yes, with minimal changes, because Ollama and vLLM expose OpenAI-compatible APIs. Often you only change the base URL and model name.

Can it run on a Mac? Yes. Ollama supports Apple Silicon with GPU acceleration. vLLM is better suited to NVIDIA/AMD GPU servers.

Can you set it up in the cloud instead of on-premise? Yes. We can deploy on a private GPU instance in AWS, GCP, Azure or GPU clouds, so the data stays in your account.

Can you connect it to our documents? Yes. Advanced includes a basic RAG connection, and Build RAG Chatbot on Your Docs covers full document Q&A.

Which open models do you work with? Llama, Qwen, Mistral, Gemma, DeepSeek, gpt-oss and others available in Ollama or on Hugging Face. We pick based on your task, language, license and hardware.

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