VERIFICATIONChecked against current official documentation on 2026.08.04; hardware-specific performance is not generalized.

30-SECOND SUMMARY

What to take away

  • Start with VRAM capacity and runtime support.
  • Include PSU, case, cooling, motherboard, RAM, and storage in total cost.
  • Run a sustained load and the real model before purchase when possible.
BUYING 01

Verify used hardware

A real load matters more than a listing.

  1. 01
    IDENTIFY

    Model and VRAM

  2. 02
    FIT

    Power, space, cooling

  3. 03
    STRESS

    Heat and errors

  4. 04
    MODEL

    Run the intended GGUF

Use it this way Include warranty and full-system cost.
SECTION 01

Work backward from the model

Define model size, context, concurrency, and memory headroom. Training or image generation may require different software support.

SECTION 02

Cross-check identity

Request the exact model, VRAM, serial information, ports, and power connectors. Compare compute capability and runtime support with official sources.

SECTION 03

Calculate system cost

Check physical clearance, PSU rating and connectors, slots, CPU, RAM, storage, cooling, and noise. Avoid unsafe adapter chains.

SECTION 04

Test under load

Inspect artifacts, fan noise, temperature, errors, and throttling, then run the intended GGUF model for 20–30 minutes. Document warranty and return terms.

  • Serial and seals
  • VRAM identity and errors
  • Fans, temperature, noise
  • No shutdown under load
  • Real model loading and speed
  • Warranty and return terms
FAQ

Frequently asked questions

Should every mining GPU be avoided?

No. Verify temperature, errors, fans, and sustained stability instead of relying on history alone.

Is VRAM all that matters?

No. Software support, bandwidth, power, cooling, and system RAM matter too.

What should I request in person?

Exact identification, a load test, the intended model run, and purchase evidence.

Primary sources

Check the original documentation for version-specific details.

Ollama hardware support NVIDIA CUDA GPUs llama.cpp repository

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