GPU Specs for Hosting

Compare GPU Specs Before Renting Expensive Compute

GPU specs for hosting are the hardware limits behind GPU servers, AI inference nodes, training boxes, and rendering hosts. This catalog compares NVIDIA accelerators by VRAM, memory bandwidth, tensor cores, interface, power draw, and workload fit before a monthly USD bill turns into an expensive mistake.

Expensive Mistakes To Check First

• Dedicated GPU means one customer controls the accelerator. Shared GPU means another workload can steal memory, PCIe bandwidth, or scheduler time.
• VRAM is a hard ceiling. A model that needs more memory will not become cheaper because the provider writes AI optimized on the plan page.
• Egress can dominate the bill. Training checkpoints, rendered frames, and inference logs move real traffic after the GPU has already been paid for.
• Backups and snapshots rarely cover GPU state cleanly. Treat attached storage, object storage, and restore time as separate costs.
• Benchmark rows are useful only after the source is attributable. Use sample benchmark values as a layout signal, not as production ranking evidence.

How To Read The Catalog

NVIDIA A100 80GB uses the Ampere architecture. NVIDIA A100 80GB provides 80 GB of HBM2e memory. NVIDIA A100 80GB exposes 2,039 GB/s of memory bandwidth. That profile fits larger training runs, batch inference, and memory-bound jobs where VRAM matters more than sticker TFLOPS.

NVIDIA L40S 48GB uses the Ada Lovelace architecture. NVIDIA L40S 48GB provides 48 GB of GDDR6 ECC memory. NVIDIA L40S 48GB uses a PCIe 4.0 x16 interface. That profile fits inference, rendering, and mixed AI workloads where a modern PCIe server is easier to rent than an SXM platform.

NVIDIA RTX 4090 24GB uses the Ada Lovelace architecture. NVIDIA RTX 4090 24GB provides 24 GB of GDDR6X memory. NVIDIA RTX 4090 24GB draws 450 W. That profile can be fast per dollar, but consumer-class hosting needs extra caution around remote hands, thermal design, driver support, and replacement guarantees.

NVIDIA A10 24GB uses the Ampere architecture. NVIDIA A10 24GB provides 24 GB of GDDR6 memory. NVIDIA A10 24GB draws 150 W. That profile fits lighter inference, virtual workstations, and predictable multi-tenant hosts better than power-hungry cards with fragile cooling assumptions.

Shortlist By Workload

Choose 80 GB VRAM when the model, batch size, or training checkpoint cannot fit inside 24 GB or 48 GB. Choose ECC memory when silent data errors matter more than the cheapest hourly rate. Choose PCIe cards when migration between providers matters more than peak SXM bandwidth.

For US and UK buyers, compare the monthly USD price with traffic, storage, backups, and minimum contract length in the same spreadsheet. A cheap GPU month stops being cheap when the provider charges separately for outbound traffic, recovery images, or manual intervention after a failed driver update.

Practical Verdict

Use this catalog to reject the wrong GPU before you compare providers. Start with VRAM, memory bandwidth, interface, and power draw. Then verify whether the plan is dedicated, whether the driver stack is managed, and whether the provider publishes real limits for traffic, snapshots, SLA, and support response.