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Compare GPU classes for LLM inference from VRAM, ECC, workload, cost/latency priority and multi-GPU requirements.
$ need: VRAM + ECC + workload $ candidates: 24 / 32 / 48 / 80 / 96 / 141 GB $ multi-GPU: only with supported parallelism $ benchmark before production
VRAM alone is not enough. ECC, server form factor, throughput, concurrency, model parallelism and 24/7 operation can change the correct choice.
This tool produces a starting estimate for capacity planning; validate production decisions with load tests and real monitoring data.
Change the inputs and calculate again.
A model that fits on one GPU is operationally simpler. Otherwise the runtime must support model parallelism or offload.
RTX 5090 offers 32 GB GDDR7; L40S and RTX 6000 Ada are 48 GB classes; RTX PRO 6000 Blackwell offers 96 GB ECC; H200 offers 141 GB HBM3e.
Long-running production and scientific workloads may prioritize error-correcting memory.
Tensor generation, memory bandwidth, power limits and software kernels differ even at the same VRAM size.
It can be strong for models that fit within 32 GB, especially development and local inference.
L40S and RTX 6000 Ada are 48 GB classes.
RTX PRO 6000 Blackwell offers 96 GB GDDR7 ECC variants.
NVIDIA H200 offers 141 GB HBM3e.
Share your workload, traffic profile and growth target so we can determine the appropriate VPS or GPU server class.
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