Choose memory
before speed.
For inference, a faster GPU is not useful if the model and workload cannot fit. Start with usable VRAM, then evaluate throughput, latency, software support, and price.
Test your workload →VRAM
NVIDIA T4
Budget inference. Turing architecture.
VRAM
NVIDIA L4
Efficient inference. Ada Lovelace architecture.
VRAM
RTX 4090
Local performance. Ada Lovelace architecture.
VRAM
NVIDIA L40S
Inference sweet spot. Ada Lovelace architecture.
VRAM
NVIDIA A100 80GB
Mature datacenter. Ampere architecture.
VRAM
NVIDIA H100
High throughput. Hopper architecture.
VRAM
NVIDIA H200
Long-context memory. Hopper architecture.
VRAM
NVIDIA B200
Frontier systems. Blackwell architecture.
VRAM
AMD MI300X
Large HBM capacity. CDNA 3 architecture.
Hourly figures on profile pages are sample planning inputs, not live quotes or benchmark promises. Availability, interconnect, region, framework version, and provider configuration all affect a production decision.
Capacity first.
Then the system.
- 01Keep operating headroom
Targeting every advertised gigabyte leaves no room for allocator behavior, kernels, graph capture, or workload variance.
- 02Model the peak workload
Use intended context and concurrent sequences, not only the empty model weight footprint.
- 03Validate the whole stack
Tensor parallelism, bandwidth, quantization support, runtime version, and provider topology influence real results.