Best local LLM setup under £1500 for document Q&A (2026)

runs Qwen2.5-32B-Instruct Q4_K_M · no published benchmark yet

Qwen2.5-32B-Instruct Q4_K_M for around £403 — the best document Q&A value at this budget

• This is a noisy, fiddly, enthusiast build — not for someone who wants it to 'just work'
• Corporate towers (Dell OptiPlex, HP EliteDesk/ProDesk) often ship with proprietary or low-wattage PSUs — check for a free PCIe x16 slot, physical case clearance, and budget for a replacement 750W PSU before buying a GPU for it
• Passive cooling only — no fan. You must fit a 3D-printed shroud and a high-static-pressure fan or it will thermal-throttle
• No video output at all — you need a second GPU or the host's onboard/iGPU video for a display
• Requires 'Above 4G Decoding' / Re-Size BAR enabled in the host BIOS to be recognised correctly
• Pascal architecture with no Tensor Cores — markedly slower than a same-VRAM RTX card, so pin your driver/CUDA versions and avoid bleeding-edge llama.cpp builds that assume newer GPUs
• Draws ~250W continuously under load — a real electricity cost if left on
• Buy new, not used, and confirm it has enough spare 8-pin PCIe power connectors for the GPU you're pairing it with — a failing or underpowered PSU can take a used GPU down with it

• Used office tower PC (i5, no GPU, 16GB RAM)£55–95
• NVIDIA Tesla P40 24GB (used, datacentre)£210–270
• 750W ATX PSU, 80+ Bronze or better (new)£75–100

runs Qwen2.5-32B-Instruct Q4_K_M · no published benchmark yet

Qwen2.5-32B-Instruct Q4_K_M for around £1035 — a strong document Q&A option at this budget

• Noisy under load
• Corporate towers (Dell OptiPlex, HP EliteDesk/ProDesk) often ship with proprietary or low-wattage PSUs — check for a free PCIe x16 slot, physical case clearance, and budget for a replacement 750W PSU before buying a GPU for it
• Needs 2×8-pin PCIe power — check the host PSU before buying
• No warranty on used cards; test with a memory stress tool on arrival
• 2026 DRAM/GDDR shortage has pushed used prices to roughly double the 2024-25 norm, and a 3090 alone now costs more than a tower and PSU combined — shop around, don't assume older price guides still apply, and don't expect this to be a budget tier
• Buy new, not used, and confirm it has enough spare 8-pin PCIe power connectors for the GPU you're pairing it with — a failing or underpowered PSU can take a used GPU down with it

• Used office tower PC (i5, no GPU, 16GB RAM)£55–95
• NVIDIA RTX 3090 24GB (used)£820–925
• 750W ATX PSU, 80+ Bronze or better (new)£75–100

runs Llama 3.1 8B Instruct Q4_K_M · ~12 tok/s (source)

Llama 3.1 8B Instruct Q4_K_M at ~12 tok/s for around £330 — a strong document Q&A option at this budget

• Silent, low-power, and needs no assembly
• 10GB headroom only fits 7-8B-class coding models at roughly 12 tok/s on this class of hardware — too slow and too weak to be a pleasant coding assistant, so this build is not recommended for coding
• 16GB is unified memory shared between macOS and the model — expect roughly 10GB usable for a model before the system starts swapping
• No user-upgradeable RAM or storage — buy the 16GB configuration outright

• Apple Mac mini M1 16GB (2020, used)£280–380

runs Llama 3.1 8B Instruct Q4_K_M · ~12 tok/s (source)

Llama 3.1 8B Instruct Q4_K_M at ~12 tok/s for around £370 — a strong document Q&A option at this budget

• CPU-only inference is fine for chat but too slow to be pleasant for coding assistance
• Roughly 24GB of the 32GB RAM is realistically usable for a model once the OS and other software are accounted for
• Silent and very low power (under 65W)
• No discrete GPU — inference runs on CPU (with limited iGPU/Vulkan offload); check the BIOS UMA/VRAM allocation setting if trying iGPU offload
• DDR5 dual-channel bandwidth is the real bottleneck for CPU inference speed, not the CPU core count

• Mini PC — AMD Ryzen 7 7840HS, 32GB DDR5 (e.g. Beelink SER7 / GMKtec NucBox K6)£320–420