Local AI buying guide
Compare complete AI computers by the models that fit in fast memory, their storage and networking options, and the tradeoffs they make in speed, footprint, noise, and upgradeability.
The key decision
A 64 to 128 GB shared pool can hold large quantized models. It is compact and capacity-focused, but typically does not match a large discrete GPU's inference speed.
Best fit: capacity-focused 32B to 70BCompare AI mini PCs32 GB of dedicated VRAM and high memory bandwidth are excellent when the model fits entirely on the GPU. 70B usually requires CPU/RAM offloading or stronger quantization.
Best fit: fast 8B to 32B workflowsA specialized NVIDIA system can combine a large unified pool with CUDA tools. In return, plan for an Arm processor, Linux, and a machine designed more for AI development than general Windows desktop use.
Best fit: CUDA-native development and large models
Quick choice by workload
There is no single best local AI computer. Model size, target speed and software support lead to different systems. This shortlist maps each goal to a concrete hardware path.
| Your goal | Suitable hardware path | Specific systems | Why it fits |
|---|---|---|---|
| 7B to 14B, practical entry | 12 to 16GB of dedicated VRAM and at least 32GB RAM | Choose the GPU tier | Useful headroom for many compact models and ordinary context lengths. |
| 20B to 32B, maximum speed | RTX 5090 desktop with 32GB VRAM | HP OMEN 45L RTX 5090 | High memory bandwidth and a broadly supported CUDA path. |
| Large models up to 70B, compact | 128GB unified memory | GMKtec EVO-X2 or Beelink GTR9 Pro | The large shared pool prioritizes model capacity in a small footprint. |
| Professional Windows workstation | Ryzen AI Max+ PRO with workstation support | HP Z2 Mini G1a | Business configuration, internal power supply, Thunderbolt 4 and on-site support. |
| NVIDIA stack and large models | GB10 appliance with 128GB | ASUS Ascent GX10 | CUDA-oriented development and a large memory pool; Arm/Linux must fit the workflow. |
Local AI hardware requirements extend beyond a GPU or unified-memory figure. Runtime support, SSD capacity, cooling, and networking for server use all affect the right purchase; the model guides check them individually.
Specific complete systems
These PCs differ beyond model memory. Storage layout, networking, power delivery, cooling, and memory upgrades determine whether a system works best on a desk, as a home server, or for frequent large-model use.
Best for: large models in a compact PC
Ryzen AI Max+ 395 with a large 128 GB unified memory pool. Read the model guide.
Quantized 70B models, larger local knowledge bases, and a compact single-user model server. Two USB4 ports, Wi-Fi 7, and dual M.2 storage make the small chassis practical for a growing model library.
The LPDDR5X memory is soldered, and unified memory does not match the bandwidth of a large discrete GPU. Confirm the 128 GB / 2 TB option, your maximum UMA allocation, and runtime support.
Best for: large models and fast home networking
Ryzen AI Max+ 395, 128 GB unified memory, 2 TB storage, and dual 10GbE. Read the model guide.
Quantized 70B models, large local knowledge bases, and a compact model server serving several devices over a fast wired network.
The LPDDR5X memory is soldered and unified memory favors capacity over peak discrete-GPU throughput. Confirm the 128 GB / 2 TB listing, maximum UMA allocation, OS support, and Radeon runtime before buying.
Best for: support-led compact workstation
Ryzen AI Max+ PRO 395 with 128 GB unified memory, Windows 11 Pro, and a three-year warranty. Read the model guide.
Large quantized models, local RAG, and professional desks that benefit from Thunderbolt 4, an internal power supply, and on-site workstation support. The large pool leaves room for many 70B configurations while remaining a compact Windows PC.
The soldered memory cannot be upgraded, and Radeon runtime support still needs checking. The included 1 TB SSD is modest for several large model families; compare the final seller price with consumer Ryzen AI Max systems before paying for the workstation support package.
Best for: NVIDIA-native local AI development
Compact GB10 Grace Blackwell appliance with 128 GB coherent unified memory and NVIDIA's AI software stack. Read the full buying guide.
CUDA-native inference, prototyping, fine-tuning experiments, and quantized 70B workloads that benefit from a large shared pool. DGX OS includes an Ubuntu-based NVIDIA environment, while 10GbE and ConnectX-7 support fast networked workflows.
This is an Arm/Linux AI appliance, not a general-purpose Windows gaming PC. Confirm that every required application and model format supports the platform. The configuration described here uses a 4 TB M.2 2242 SSD; confirm that capacity and the warranty region in the selected offer.
Best for: high-end parts in a large chassis
RTX 5090 configuration in a roomy tower with 64 GB of system memory.
Fast 8B to 32B inference in a large, serviceable tower. Thunderbolt 4 and 2.5GbE are useful for external storage or serving models to other devices.
The included 64 GB uses all four DIMM slots (4×16 GB). Reaching the supported 128 GB therefore requires replacing the installed modules. The 2 TB SSD is a useful start, but a larger model library may need another drive.
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Direct comparison
| System | Fast memory | Best fit | Main limitation |
|---|---|---|---|
| GMKtec EVO-X2 | 128 GB shared | compact 70B use and large model capacity | soldered RAM; UMA/runtime support matters |
| Beelink GTR9 Pro | 128 GB shared | 70B capacity and dual-10GbE home serving | soldered RAM; verify OS, network, and runtime support |
| HP Z2 Mini G1a BN8E8UA | 128 GB shared | 70B capacity with Windows and workstation support | 1 TB included; soldered RAM; Radeon runtime |
| ASUS Ascent GX10 | 128 GB coherent shared | CUDA-native development and large-model experiments | Arm/Linux platform; sealed SSD; one-year warranty |
| HP OMEN 45L GT22-3090 | 32 GB GDDR7 | fast 8B–32B in a roomy tower | all DIMM slots occupied; 2 TB SSD |
Still unsure?
Model size and context matter more than a brand name. Our finder makes every assumption visible.