NVIDIA RTX A4500 — the Ampere middle ground with 20 GB and fast memory.

The RTX A4500 is Ampere; the RTX 4000 Ada is Ada Lovelace. The RTX 4000 Ada has newer tensor cores with FP8 support and ~50% higher tensor throughput. But the RTX A4500 has nearly double the bandwidth (640 vs 360 GB/s), which can make it faster for memory-bandwidth-bound inference (long sequences, large KV caches). Pricing is nearly identical ($0.19 vs $0.18/hr). Choose the RTX 4000 Ada for FP8 and compute-bound workloads; the A4500 for bandwidth-bound workloads.

NVIDIA positioned the A4500 as a mid-range professional card between the popular A4000 and A5000. It wasn't available in major cloud providers (no AWS or GCP instance type), so it flew under the radar. On spot markets like Vast.ai and RunPod, it appears from workstation hardware being repurposed — offering genuine value at $0.19/hr that most buyers don't know about.

In INT4, a 27B model needs ~14 GB — fits with 6 GB headroom. In INT8, it needs ~27 GB — exceeds the 20 GB VRAM. In FP16, it needs ~54 GB — far too large. For 27B models with acceptable quality, INT4 quantization on the A4500 works for development. For production 27B serving, the 32 GB RTX 5000 Ada or RTX 5090 is a better choice.

The RTX A4500 supports ECC mode, but enabling it reduces usable VRAM. With ECC enabled, the effective VRAM drops from 20 GB to approximately 17 GB. For inference workloads where data integrity is critical, the ECC trade-off may be worthwhile. For maximum VRAM capacity, disable ECC and rely on the inherent error tolerance of inference (unlike training, a single bit flip in inference rarely produces visibly wrong output).