One user, 7B to 14B
32GB of system RAM and 12GB to 16GB of VRAM are a workable entry point. For a new always-on server, 64GB total memory and a 1TB to 2TB SSD provide more flexibility.
Desktop and light API useOne model for several devices
Local AI server hardware for a home LLM
A local AI server makes a language model available on your own network. The priorities are enough fast memory, a supported GPU backend, sustained cooling, spacious storage and secure access—not merely a server-class CPU.
Choose a performance tier
Which local AI server fits your workload?
Model size, simultaneous requests and context length determine hardware needs. Every active session adds KV-cache demand, so a household with several clients needs more headroom than one chat window.
Household, 20B to 32B
64GB of shared memory or a GPU with 24GB to 32GB of VRAM creates room for larger quantizations. Keep capacity available for context and concurrent clients.
Several devices on the LAN70B and development services
128GB unified-memory systems are the compact capacity tier. An RTX 5090 tower is usually faster for models within its 32GB VRAM, but a 70B model normally requires offload.
Large models or several servicesPlan capacity realistically
Memory needs by model and workload
These are practical planning ranges for quantized text models, not a promise for every architecture. Quantization, context window, vision features and runtime all change memory use.
| Workload | Practical hardware | Primary concern |
|---|---|---|
| 7B–14B, one or two sessions | 12–16GB VRAM; 32–64GB RAM | full GPU offload and at least 1TB SSD |
| 20B–32B, several clients | 24–32GB VRAM or 64GB unified memory | headroom for KV cache and long context |
| 65B–70B, one large session | 96–128GB unified memory or GPU/RAM offload | capacity does not guarantee speed; verify the backend |
| 70B plus RAG and automation | 128GB with useful headroom; 2TB SSD or more | model, embeddings, index and other services compete for memory |
Use the PC Finder for an initial calculation and the VRAM guide for quantization, context and offload.
Three complete hardware routes
Specific systems for a local LLM server
Each system solves a different problem. Always confirm the exact memory, storage and GPU version on the destination page; similarly named configurations may be substantially different.
Compact x86 capacity
GMKtec EVO-X2
128GB unified memory, a 2TB SSD and 2.5GbE with Ryzen AI Max+ 395.
Why use it as a server?
The large shared pool can hold many quantized 70B models. A second M.2 slot supports a dedicated model drive, while x86 Windows or Linux keeps the machine useful beyond inference.
Before buying
Confirm the 128GB / 2TB version, available graphics-memory allocation and Radeon support in your chosen runtime. The LPDDR5X memory is soldered.
- CPU / graphics
- Ryzen AI Max+ 395 / Radeon 8060S
- Model memory
- 128GB LPDDR5X, shared
- Storage
- 2TB; second M.2 slot
- Networking
- 2.5GbE, Wi-Fi 7
NVIDIA-native appliance
ASUS Ascent GX10
128GB coherent unified memory, 4TB storage, 10GbE and NVIDIA DGX OS.
Why use it as a server?
The GB10 platform combines a large coherent pool with CUDA-oriented tools, 10GbE and ConnectX-7. It fits local development services and large-model experiments in a compact chassis.
Before buying
This is an Arm/Linux appliance rather than a conventional Windows desktop. Verify application architecture, the 4TB model, warranty and the sealed memory and storage design.
Read the complete GX10 buying guide- Processor / graphics
- NVIDIA GB10 Grace Blackwell
- Model memory
- 128GB LPDDR5X, coherent shared
- Storage
- 4TB M.2 2242 PCIe 5.0
- Networking
- 10GbE, Wi-Fi 7, ConnectX-7
High-throughput GPU tower
HP OMEN 45L GT22-3090
GeForce RTX 5090 with 32GB VRAM, 64GB system RAM and a 2TB NVMe SSD.
Why use it as a server?
CUDA support and high GPU bandwidth favor quick inference for models that fit its 32GB VRAM. The roomy chassis, 2.5GbE and Thunderbolt 4 suit a fixed desk or network-service role.
Before buying
A 70B model usually needs CPU/RAM offload and will not retain full-GPU speed. Check the exact GT22-3090 / B91WJAA#ABA configuration, additional storage options, cooling space, noise, idle draw and your electrical circuit.
- CPU / GPU
- Core Ultra 9 285K / RTX 5090 32GB
- System memory
- 64GB DDR5; four DIMMs installed
- Storage
- 2TB PCIe 4.0 NVMe
- Best fit
- fast 8B–32B inference
* Paid link. We may earn a commission if you buy; your price is unchanged. As an Amazon Associate we earn from qualifying purchases. Verify exact configuration, seller, price and availability on the destination page.
Beyond GPU and memory
Eight features of a useful home AI server
Sustained cooling
A short benchmark is insufficient. Clock speed, temperature and noise after long sessions matter more.
Idle power
An always-reachable system spends much of its time outside inference. Stable low-power states reduce running cost.
2.5 or 10GbE
1GbE is enough for text responses. Faster LAN helps with large model files, NAS data and frequent transfers.
At least 2TB
Several quantizations, embeddings and document indexes can consume hundreds of gigabytes.
Headless restart
Startup, updates and a controlled reboot should work without a monitor or keyboard attached.
Backups
Models can be downloaded again; custom prompts, indexes, configuration and private documents need backup.
Power protection
For important services, a suitable UPS reduces abrupt shutdowns and file-system damage.
Expansion
A second SSD slot and replaceable drives are more valuable to a server than unused display outputs.
Operate it safely on your LAN
Do not expose the model API directly to the internet
Ollama binds to 127.0.0.1:11434 by default. If you enable other devices, treat it like any private server: allow only necessary networks, apply firewall rules, authenticate access and use a VPN for remote access.
Test locally first
Verify the runtime, model and API on the server before making a port reachable from the LAN.
Restrict clients
Allow trusted devices or a dedicated VLAN only. An open API can consume model and system resources.
Audit data paths
Plugins, web search, telemetry and external embedding services may transmit data even when the model is local.
Common questions
Running a local AI server at home
Do I need enterprise server hardware for Ollama?
No. A well-cooled complete PC often makes more sense for a private model service. GPU or unified-memory capacity, runtime support and storage affect LLM use more directly than a server label or many CPU cores.
Can a mini PC run as an LLM server?
Yes. Unified-memory systems with 64GB or 128GB are particularly compact. Check soldered memory, cooling, backend support, networking, storage expansion and idle power.
How many users can a local LLM server support?
A product name alone cannot answer that. Model size, quantization, context, target response speed and simultaneous requests determine capacity. Plan materially more memory headroom for several clients.
Is an RTX 5090 PC or a 128GB mini PC better?
The RTX 5090 prioritizes speed for models inside 32GB VRAM. A 128GB system prioritizes large models and context but is not automatically faster. Our system comparison covers both routes.
Technical primary sources
Verify runtime and networking
The Ollama FAQ documents local binding and network access. LM Studio documents headless operation with llmster. Hardware support changes, so compare your GPU against the current Ollama GPU list before buying.