What Is a Local LLM — And Should Your Business Run One?

A practical guide to local LLM hardware, costs, and when it makes sense to deploy AI privately

Quick Answer: A local LLM (large language model) is an AI model that runs entirely on your own hardware — no cloud connection, no third-party data access, no per-query costs. For businesses that handle sensitive data or run high inference volumes, a properly configured local LLM workstation delivers private, fast AI processing for approximately $3,000–$3,500 in hardware. The most practical entry point for most small and mid-sized businesses is a single NVIDIA RTX 4090 paired with 64 GB of DDR5 RAM and 2 TB of NVMe storage, capable of running 7B to 13B parameter models at useful speeds.

AI adoption is accelerating across every industry — but for many businesses, handing sensitive client data to a third-party cloud AI raises real questions about privacy, compliance, and cost at scale. Local LLMs offer an alternative: AI that runs entirely on hardware your company owns and controls.

This guide covers what a local LLM actually requires from a hardware standpoint, what it realistically costs, and how to decide whether a managed local AI setup is right for your business. According to IBM research, 92% of companies plan to increase AI investment in the coming years — and a growing share of that investment is moving toward on-premises or hybrid deployments where data never leaves the building.

Whether you are a business owner evaluating options or an IT manager tasked with deploying private AI infrastructure, this guide covers what you need to know before spending a dollar on hardware. For businesses that want this handled end-to-end, Be Structured’s outsourced IT support services include AI infrastructure planning and deployment as part of our all-inclusive managed services.

A local LLM workstation is a high-performance computer designed to run, fine-tune, and develop large language models locally.

What Is a Local LLM?

A local LLM is a large language model — the same category of AI that powers ChatGPT, Claude, and Google Gemini — that runs on hardware you own rather than on a cloud provider’s infrastructure. The model weights are stored locally, inference happens on your GPU, and no query data is transmitted to an external server.

This distinction matters for three reasons:

• Privacy: Sensitive documents, client data, and internal communications never leave your network.
• Cost: Once the hardware is purchased, there are no per-token or per-query fees. High-volume use cases that would cost thousands per month in cloud API fees become essentially free at the marginal level.
• Control: Your team decides which model runs, when it updates, and who can access it.

The trade-off is infrastructure. Local LLMs require meaningful upfront hardware investment, ongoing IT management, and an acceptance that the most capable frontier models are not available for local deployment. The open-source alternatives are capable and improving rapidly, but a capability gap with frontier cloud models still exists for the most complex reasoning tasks. Businesses evaluating this trade-off may also want to consider hybrid cloud solutions that combine local inference for sensitive workloads with cloud AI for tasks where data sensitivity is lower.

What Hardware Does a Local LLM Actually Require?

The single most important specification for local LLM deployment is GPU VRAM. LLM weights must be loaded into memory to run. For practical inference speeds, that memory needs to be on the GPU — if a model spills into system RAM or NVMe storage, inference speed drops by 10–50x, rendering most interactive use cases impractical.

Model size determines VRAM requirement. Here is how the two map to each other:

Minimum Hardware Requirements by Model Size

For most businesses exploring local LLM deployment, the 7B–13B parameter range on a single RTX 4090 is the practical sweet spot. It delivers genuinely capable inference — useful for document Q&A, drafting support, code assistance, and structured data extraction — at a hardware cost justifiable without a server-room buildout.

Local LLM workstations feature GPUs, substantial VRAM, and fast processors to handle complex AI tasks privately.

The Full Component List for a Local LLM Workstation

Beyond the GPU, every other component in the build should be sized to support it:

• GPU: The primary investment. Prioritize VRAM above clock speed or CUDA core count. For most business deployments, the RTX 4090 (24 GB) is the optimal single-card choice.
• CPU: A modern 12–16 core processor (AMD Ryzen 9 or Intel Core i9) is sufficient. LLM inference is not CPU-bound once the model is loaded.
• System RAM: Match or exceed GPU VRAM. 64 GB DDR5 is the recommended baseline for a 13B model workstation.
• Storage: NVMe Gen 4 SSD, minimum 2 TB. A 70B model in 4-bit quantization is roughly 40 GB; a collection of several models fills storage quickly.
• Motherboard: PCIe Gen 4 or Gen 5 support is essential for multi-GPU builds. Verify PCIe slot configuration before purchasing.
• Power Supply (PSU): Size for peak GPU draw plus 30% headroom. A single RTX 4090 draws up to 450W under load; dual-GPU builds may require a 1,200W+ PSU.
• Cooling: Air cooling is sufficient for single-GPU workstations in a standard office environment. Multi-GPU builds benefit from all-in-one liquid cooling on the CPU.
• Case: A full-tower with multiple 140mm fan mounts and clearance for large GPU cards — some RTX 4090s exceed 340mm in length.

Deploying local LLM infrastructure also intersects with your network. Secure local AI access requires proper network segmentation and access controls. Be Structured’s network security services ensure the workstation’s inference endpoint is only reachable by authorized users and devices.

Local LLM Workstation vs. AI Server: What Is the Difference?

A local LLM workstation is a single-user or small-team system designed for interactive use — a developer running inference locally, a business analyst processing documents, or a team using an LLM for drafting support. It sits under a desk or in a tower, connects to standard office power, and is managed like any other PC.

An AI server is a rack-mounted, multi-user system designed for concurrent inference requests, high availability, and sustained throughput. It requires dedicated power circuits, active cooling, and server-room infrastructure — and the cost reflects it.

AI Workstation vs. AI Server: Key Differences

For the majority of businesses evaluating local LLM deployment for the first time, a workstation is the right starting point. A server becomes relevant when concurrent users, uptime requirements, or model size push beyond what a workstation can reasonably support.

Can Your Business Run ChatGPT-Level AI Locally?

ChatGPT and Claude are frontier models with hundreds of billions of parameters. Running those exact models locally is not currently feasible for any individual or small business. But the open-source model ecosystem has advanced significantly, and for focused business use cases, the gap is narrower than most people expect.

What a single RTX 4090 workstation can handle today:

• Mistral 7B and derivatives: High-quality writing, summarization, and Q&A
• LLaMA 3 8B: Meta’s latest small-generation model with strong general performance
• Phi-3 Medium: Microsoft’s efficiency-optimized model that punches above its parameter count
• DeepSeek Coder 6.7B: Competitive with cloud models for code completion and review — see Meta’s LLaMA model documentation for a broader overview of open-source model families available for local deployment.

For internal document Q&A, code assistance, drafting support, and structured data extraction, a well-chosen 7B or 13B model running on a capable workstation handles the workload effectively. For open-ended reasoning, complex multi-step tasks, or broad general knowledge queries, frontier cloud models still hold a meaningful capability advantage.

The right framing: a local LLM is not a replacement for frontier cloud AI in every scenario. It is the right tool for specific use cases where data privacy, cost at volume, or offline availability are the primary drivers. Gartner projects that more than 80% of enterprises will have used generative AI APIs or deployed GenAI-enabled applications by 2026 — with on-device and private deployment growing as a share of that total.

AI workstations aren’t just PC upgrades but part of a new infrastructure layer that intersects with your network and IT management workflows.

How Much Does Local LLM Infrastructure Cost?

The hardware cost spectrum for a local LLM workstation ranges from approximately $1,400 for an entry-level build to over $6,800 for a high-end dual-GPU configuration:

Local LLM Workstation Build Cost by Tier

A few important context notes:

• GPU prices fluctuate. RTX 4090 pricing has been volatile — verify current street pricing before finalizing a budget.
• Enterprise GPU options (NVIDIA RTX 6000 Ada at ~$6,800, or A100s at $10,000+) offer higher VRAM and production-grade reliability, but push total system cost to $15,000–$25,000.
• These figures are hardware only. Add software configuration, inference framework setup, IT deployment time, and network integration costs.
• For organizations deploying multiple workstations, managed procurement and configuration typically reduces total cost of ownership compared to managing deployments individually.

Cloud API vs. local LLM cost: At low-to-moderate usage volumes, cloud AI APIs are typically more cost-effective when total costs are factored in. A mid-tier workstation ($3,000–$5,000) can break even against cloud API costs in 6–18 months at high inference volumes. The calculation shifts decisively in favor of local deployment when data privacy requirements make cloud AI unsuitable regardless of cost. For a broader look at how to evaluate IT spending decisions like this, see our guide to IT budget optimization for Los Angeles businesses.

How to Set Up a Local LLM for Your Business: Step-by-Step

The following steps apply to workstation-class deployments — single systems for individual or small-team use, not production server infrastructure.

1. Define the use case before selecting hardware. The most common overspending mistake is buying for a theoretical maximum rather than the actual workload. Identify the specific tasks the LLM will handle, the number of concurrent users (if any), and the minimum acceptable inference speed. This determines the model size, which determines the GPU.
2. Select your GPU based on VRAM, not marketing claims. Filter GPU options by VRAM first. For a 7B model, 16 GB is sufficient. For a 13B model, 24 GB is the practical minimum. For anything larger, you are moving into dual-GPU or enterprise GPU territory.
3. Build every other component around the GPU. CPU, RAM, storage, PSU, and case all flow from the GPU choice. Avoid over-investing in CPU or RAM while under-investing in VRAM — the GPU is the bottleneck in nearly every inference scenario.
4. Choose your inference framework before purchasing. Ollama is the most accessible option for most business users — it handles model downloading, quantization, and API exposure with minimal configuration. vLLM is better suited for multi-user or production deployments. llama.cpp maximizes performance on consumer hardware.
5. Install and configure the OS for AI workloads. Linux (Ubuntu 22.04 LTS) is the recommended OS for LLM inference due to superior GPU driver support and inference framework compatibility. Windows works for most consumer GPU setups but adds friction. Ensure NVIDIA drivers, CUDA toolkit, and Python environment are correctly installed before attempting model inference.
6. Download and test your first model. Start with a quantized version of your target model — 4-bit quantization significantly reduces VRAM requirements with minimal quality loss. Run a baseline inference test to verify the setup before integrating with any business workflows.
7. Secure the inference endpoint. If the workstation will be accessed by other users on the network, implement API authentication, restrict access to authorized IP addresses or VPN users, and enable audit logging. An unsecured local inference endpoint is a data and security risk. This step should involve your IT team or a managed services provider — Be Structured’s managed IT services in Los Angeles include endpoint security and network access controls as standard.
8. Integrate with business applications and establish a maintenance routine. Connect the inference API to the tools and workflows that will use it. Schedule regular model updates, driver updates, and framework updates. Monitor GPU temperature and VRAM utilization under typical workloads to establish a performance baseline.

Build, Buy, or Managed: Which Approach Is Right for Your Business?

Not every business has the in-house technical expertise to build and maintain a local LLM workstation. Here is how the three main deployment paths compare:

Local LLM Deployment Options Compared

For businesses without a dedicated IT team, a managed AI workstation deployment — where an IT partner handles procurement, configuration, security hardening, and ongoing support — typically delivers the best outcome. Be Structured provides all-inclusive outsourced IT support that covers AI infrastructure as part of a broader managed services engagement, including network setup, server configuration, and security.

It is also worth thinking about local LLM infrastructure in the context of broader IT planning. When a company expands, relocates, or adds headcount, AI infrastructure requirements should be part of the IT conversation from the start. For Los Angeles businesses specifically, our network installation services ensure the physical infrastructure that supports local AI workstations is properly designed and documented from day one.

Frequently Asked Questions: Local LLM for Business

What is a local LLM and how is it different from ChatGPT?

A local LLM is a large language model that runs entirely on hardware your business owns — no internet connection required, no data transmitted to a third-party server. ChatGPT and similar cloud AI services process your queries on external servers. A local LLM gives your business full control over data privacy and eliminates per-query costs, but requires upfront hardware investment and IT setup. The models available for local deployment are generally smaller and less capable than frontier cloud models, though the gap has narrowed significantly in 2025–2026.

What is the best GPU for running a local LLM?

For most small and mid-sized businesses, the NVIDIA RTX 4090 with 24 GB of VRAM is currently the best consumer GPU for local LLM deployment — it offers the highest VRAM available in a single consumer card at a price below enterprise alternatives (currently around $1,800). For businesses needing to run models larger than 13B parameters on a single card, the NVIDIA RTX 6000 Ada (48 GB VRAM) is the next tier at approximately $6,800. Enterprise options like the A100 and H100 are cost-justified only for production or multi-user deployments starting at $10,000 per card.

Can a standard business PC run a local LLM?

A standard office PC can technically run smaller LLMs (3B–7B parameters) using system RAM if no discrete GPU is present, but performance will be extremely slow — typically 1–5 tokens per second — which is impractical for interactive business use. A dedicated GPU with at least 8–16 GB of VRAM is the minimum for usable inference speeds. A workstation purpose-built for local LLM inference will outperform a standard PC by 10–50x on the same model.

How much does it cost to run a local LLM for a small business?

Hardware costs range from approximately $1,400 for an entry-level build (capable of 3B–7B models) to $3,300 for a mid-tier RTX 4090 workstation (capable of 7B–13B models) to $6,800+ for a high-end dual-GPU configuration. These are hardware-only figures — add IT setup, configuration, and ongoing maintenance costs. A mid-tier workstation typically breaks even against cloud AI API costs in 6–18 months at high inference volumes.

Is a local LLM secure for sensitive business data?

A properly configured local LLM is significantly more secure than cloud AI for sensitive data because no information is transmitted outside your network. However, 'local' does not automatically mean 'secure' — the inference endpoint must be protected with API authentication, restricted network access, and audit logging. An improperly secured local LLM workstation on a business network can expose sensitive data to internal threats or network vulnerabilities. For businesses handling regulated data (HIPAA, legal, financial), a managed deployment with proper security hardening is strongly recommended over a self-configured setup.

What software is needed to run a local LLM?

The minimum software stack for a local LLM workstation includes: an NVIDIA GPU driver, the CUDA toolkit, and an inference framework. Ollama is recommended for most business users due to its straightforward setup and built-in model management. vLLM is better suited for multi-user or production deployments. llama.cpp maximizes performance on consumer hardware. Python and relevant libraries (transformers, PyTorch) are useful for custom integrations but are not required to run inference through Ollama.

What is the difference between a local LLM workstation and an AI server?

A local LLM workstation is a single-user or small-team system — typically a tower PC with one or two consumer or prosumer GPUs — designed for interactive, desktop use. An AI server is a rack-mounted, multi-user system designed for concurrent inference requests, high availability, and sustained throughput, requiring dedicated power circuits, active cooling, and server-room management. For most businesses starting out with local AI, a workstation is the appropriate first step. A server becomes relevant when production demand exceeds what a workstation can support.

The Right Local LLM Setup Starts With the Right Plan

A well-configured local LLM workstation delivers genuine business value: private AI inference, no per-token costs, and full control over the models your team works with. But the difference between a deployment that performs and one that sits underutilized comes down to matching the hardware to the actual workload — and ensuring the surrounding infrastructure is ready to support it.

Be Structured helps Los Angeles businesses plan and deploy local LLM infrastructure that is properly integrated from day one. From network installation to ensure secure local AI access, to managed IT services that cover the full technology stack — our all-inclusive team assesses your current infrastructure, recommends the right build tier, and manages deployment so your team can focus on what the AI actually does for your business.

Contact Be Structured today to schedule an AI infrastructure assessment.