The $40-50 P102-100 mining card tops the second-hand value chart among 76 GPUs tested: newer isn't always better for local AI.
What this is
This is a Reddit community's GPU selection reference for home servers. llama.cpp (a lightweight tool for running large models on personal computers) ran the tests via Vulkan (a unified interface for invoking different brands of GPUs), using Llama 2 7B (~7 billion parameters) at Q4_0 quantization (a lower-precision format that reduces memory usage).
VRAM is the GPU memory used to hold computational data. Text generation requires continuously reading parameters; in this test set, wide memory bus width helps explain the P102-100's cost advantage.
Industry view
Our take: when budget is constrained, tokens-per-dollar matters more than card generation when comparing value.
But the post relies on AI-assisted recommendations rather than independent re-testing; a single model, fixed quantization, and second-hand price volatility could all shift the rankings. Mining cards also require source verification, power draw, warranty, and compatibility checks.
Impact on regular people
For enterprise IT: Older cards may lower the cost of in-house model experimentation, but budgets shouldn't compare bare-card prices alone — stability and after-sales support matter too.
For individual professionals: Local models may lower the experimentation barrier; for most roles, the payoff still depends on whether they solve a business problem.
For the consumer market: If enterprise demand rises, some older card prices may climb; these cards aren't necessarily suited for daily gaming or office work.