24–32 GiB GPU

One GPU, more room

A focused workstation for larger quantized models and local coding experiments.

What to plan for

GPU
A 24 or 32 GiB GPU supported by the runtime. Compare used-card condition and new-card alternatives.
System memory
64 GiB is a planning target for loading models while keeping desktop work available.
Storage
1–2 TB NVMe as a starting capacity, adjusted to your existing projects and model library.
Power & enclosure
Size the supply for the chosen CPU and exact board. Plan for sustained heat and connector clearance.

Your first run

  1. Compare Q4 and Q5 memory requirements for your target model and context.
  2. Verify the model format, runtime version and GPU architecture are supported together.
  3. Measure response latency and output speed with your actual prompts before committing to a workflow.

The tradeoff

A faster 24 GiB card has essentially the same memory fit as another 24 GiB card. Paying for more compute does not solve a capacity limit.

These are component selection criteria, not a verified bill of materials. Check compatibility, current prices, power and cooling before purchasing. The examples assume Q4 weight precision, 8K context, one request and FP16 KV cache.