Inference capacity qualified by memory and throughput.
GPU infrastructure for defined model-serving workloads, with optional deployment and ongoing management.
Fit the weights, then measure throughput.
Typical starting points by model footprint, in INR per card per month. Every configuration is qualified against the actual model, precision, context and concurrency before quote.
Monthly capacity per GPU card, not per-token or per-second billing. Deployment of a supported serving stack and continuing management are separate, stated decisions.
Compare all GPU cardsCommon questions.
Which GPU serves a 7B or a 70B model?
As qualified starting points: 7B-class models typically fit an L4 or L40S, while 70B-class serving typically needs H100-class memory or a multi-GPU configuration. The final card follows precision, context length and concurrency, which is why every quote is workload-qualified.
Which GPU is best for inference?
The right card depends on memory fit, runtime support, throughput target and budget. A familiar model name is not enough to decide.
Do you charge per token?
No. Infrastructure is monthly capacity. Deployment and Infrastructure Management are priced separately.