48 GB
RTX 6000 Ada
A workstation-class PCIe option for development and models that fit its memory budget. Check server support before specifying a multi-GPU build.
View RTX optionsGPU hardware for inference
Choose for your model and traffic, then quote the exact build.
01 / Size the workload
Model fit comes first
GPU memory must hold the model and leave room for context, concurrent requests, and runtime overhead. The right system also depends on the latency and throughput you need at your expected traffic level.
What to include
- Model and weight precision or quantization
- Typical and maximum context length
- Concurrent users or requests per second
- Latency target, budget, and delivery location
A benchmark from your own serving stack is the best way to validate final sizing.
02 / Narrow the hardware
GPU families to compare
Memory figures are per GPU. Multi-GPU memory is not a single shared pool; model parallelism and interconnect affect how much is usable.
80 GB
H100 PCIe
Datacenter PCIe hardware with more memory per GPU. Confirm the host server, cooling, and interconnect for the exact card quoted.
View H100141 GB
H200 SXM
More HBM per GPU for memory-heavy serving. SXM requires a compatible server platform, so quote the complete system.
View H200180 GB
B200 SXM
An HGX system-class option for large deployments. Size and quote it as a compatible server configuration, including power and cooling.
View B20003 / Get the actual offer
Quote the complete server
We check current supply for the requested card or system. A usable quote identifies the exact configuration, price, delivery estimate, and warranty terms. Share a target date if timing matters.
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