Local AI buying guide

Complete systems for local LLMs

Compare complete systems by the models that fit in fast memory, their storage and networking options, and the tradeoffs they make in speed, footprint, noise, and upgradeability.

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The key decision

Large memory pool or maximum GPU bandwidth?

Unified memory: fit more model

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 direction: capacity-focused 32B to 70B

Discrete GPU: maximize speed

32 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 direction: fast 8B to 32B workflows

Specific complete systems

Which configuration fits your daily use?

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

GMKtec EVO-X2

Ryzen AI Max+ 395 with a large 128 GB unified memory pool.

128 GB

Good for

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.

Limits

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.

CPU / graphics: Ryzen AI Max+ 395 / Radeon 8060S
Memory: 128 GB LPDDR5X, shared
Storage expansion: 2× M.2 PCIe 4.0; 2 TB included
Networking: 2.5GbE, Wi-Fi 7
Ports: 2× USB4, HDMI, DisplayPort, SD reader
Size / power: 193 × 186 × 77 mm; external 230 W adapter

Best for: fast high-end inference

Corsair Vengeance i8300

RTX 5090 desktop with 64 GB system memory and generous SSD capacity.

32 GB

Good for

Fast coding assistants, RAG, and local chat with 8B to 32B models that fit in 32 GB of VRAM. Its two NVMe drives provide 6 TB total, enough to keep several model families and project data on fast local storage.

Limits

70B models generally require slower system-memory offload. The included 64 GB is installed as 2×32 GB; confirm available DIMM slots and the supported maximum before planning a memory upgrade.

CPU / graphics: Core Ultra 9 285K / RTX 5090 32 GB
Memory: 64 GB DDR5 (2×32 GB)
Storage: 2 TB + 4 TB NVMe
Cooling / power: 360 mm CPU liquid cooler; 1200 W Gold PSU
Networking: 5GbE, Wi-Fi 7
Exact model: CS-9060020-NA

Best for: high-end parts in a large chassis

HP OMEN 45L

RTX 5090 configuration in a roomy tower with 64 GB of system memory.

32 GB

Good for

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.

Limits

The included 64 GB uses all four DIMM slots (4×16 GB). Reaching the supported 128 GB therefore requires replacing the installed modules, and the 2 TB SSD offers less room than the Corsair configuration.

CPU / graphics: Core Ultra 9 285K / RTX 5090 32 GB
Memory: 64 GB DDR5-5600 (4×16 GB); up to 128 GB
Storage: 2 TB PCIe 4.0 NVMe
Power: 1200 W 80+ Gold PSU
Networking: 2.5GbE, Wi-Fi 6E
Exact model: GT22-3090 / B91WJAA#ABA

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Direct comparison

Choose by model size and practical priorities

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
Corsair Vengeance i8300	32 GB GDDR7	fast 8B–32B plus 6 TB model storage	70B requires offload
HP OMEN 45L GT22-3090	32 GB GDDR7	fast 8B–32B in a roomy tower	all DIMM slots occupied; 2 TB SSD

Still unsure?

Calculate your memory need first

Model size and context matter more than a brand name. Our finder makes every assumption visible.

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