Dense 31B LLMs stress GPU tradeoffs

The practical bottleneck here is usually memory behavior at long context, not just parameter count, so “more TFLOPS” is not automatically “faster.” For dense 31B-class models, a single card with enough VRAM often ages better than a split setup unless the inference stack scales cleanly across GPUs.

• –64K context makes KV cache the real constraint, especially once you move past toy quantizations
• –Dual 16GB cards can work, but pipeline parallelism tends to be backend-sensitive and can erase much of the theoretical gain
• –24GB is the floor for comfortable use, but 32GB buys noticeably more flexibility for context growth and higher quants
• –A dual-7900 XTX style build is attractive on paper for throughput, but only if the software stack keeps utilization high across both cards
• –If the goal is fewer compromises, a single high-VRAM GPU is usually the safer buy than chasing bargain multi-GPU scaling