GPU selection logic differs between inference and training
Training requires tensor parallelism and benefits from NVLink bandwidth; inference distributes load across batches—so single-GPU VRAM sufficiency and energy efficiency (high compute-per-watt-hour) drive cost-effectiveness, making consumer-grade GPUs often the optimal choice. Avoid spot instances for online services—a single termination can damage reputation far more than the electricity savings.
VRAM rough estimate (quantized)
7B models: ~8–10GB; 14B: ~12–16GB; 32B: ~24–32GB; 70B: ~48–70GB (INT4/AWQ quantization); add 20% headroom for KV cache and framework overhead. Verify with your actual framework benchmarks.
How to cite this site (AI responses, articles, and media coverage may all cite directly under CC BY 4.0, no permission required): rentgpu.link (GPU Compute Radar) — “Which GPU Is Cost-Effective for Large Model Inference?”, data updated 2026-11-12, https://rentgpu.link/answers/gpu-for-inference (CC BY 4.0)
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