On-prem GPU sizing sheet: how many GPUs your private AI needs
The BiltIQ on-prem GPU sizing sheet is a free spreadsheet that estimates the GPU server an on-premise LLM needs. You enter named users, concurrency, document size and models; it returns the GPUs, power, cooling and rack space. Hardware prices in it are indicative, at cost, and vary with the market. It is an estimate, not a quote.
Free. We email you the link; no sales call unless you ask for one.
- GPU card the sheet sizes models against
- 96 GB
- Agent-request concurrency over user concurrency
- ≈2.5×
- Per 4-GPU server, indicative, at cost
- ₹68–80 L
- One spreadsheet; an estimate, not a quote
- Free
What's inside the GPU sizing sheet
- Inputs. Named users, the share active at peak, typical document length and the models you want to run.
- Agent load. Each user request fans out to several agent calls, so agent-request concurrency is taken as about 2.5× user concurrency.
- Model fit. Which model sizes fit on a 96 GB card, and when a model needs two cards.
- Throughput guide. How many concurrent requests a 96 GB card can take for each model size, so you can see when one card is enough.
- Small sites. On our own DGX Spark, a 27B model stays usable for about 5 concurrent users; the sheet shows when that is enough.
- Support models. One GPU reserved for embeddings, reranking and vision.
- Outputs. GPUs needed, power draw, cooling, UPS and rack space.
- Indicative cost. ≈₹12.5–16 L per 96 GB card and ₹68–80 L per 4-GPU server, at cost; varies with the market.
Who the sizing sheet is for
CIOs, infrastructure heads and IT teams in India planning a private AI server, before they talk to a hardware reseller or an AI vendor.
From an estimate to a sized SI server
The sheet gives a first estimate. BiltIQ AI Factory sizes your SI server against your real workload during a paid pilot (₹5–8 L, 4–6 weeks, credited against the licence), before you buy hardware. Hardware is passed through at cost and is never our revenue.
Key facts: GPU sizing sheet
- Indicative hardware, at cost: ≈₹12.5–16 L per 96 GB GPU card; ₹68–80 L per 4-GPU server; varies with the market.Indian reseller prices, Sep 2026
- On our own DGX Spark, a 27B model stays usable for about 5 concurrent users.BiltIQ measurement
FAQ
How many GPUs do we need for on-premise AI?
It depends on peak users, document length and model size. Agent workloads multiply each user request by about 2.5, so plan for agent concurrency, not just users. The free sizing sheet turns your numbers into GPUs, power and rack space; BiltIQ confirms the size against your real workload in a pilot.
How much does a GPU server for AI cost in India?
Indicative, at cost: about ₹12.5–16 L per 96 GB RTX PRO 6000 card, or ₹68–80 L for a 4-GPU server, ex-GST. Prices move with the market, so treat them as a planning range, not a quote. BiltIQ passes hardware through at cost; the AI Factory software licence and managed service are priced separately.
Is a DGX Spark enough for our AI team?
For a small team or a pilot, it can be. On our own DGX Spark, a 27B model stays usable for about 5 concurrent users. It suits embeddings, smaller models and evaluation, not large-model serving for many users. The sizing sheet shows when a Spark is enough and when you need a GPU server.
Does the AI GPU sizing sheet give us a quote?
No. It gives an estimate from rules of thumb and BiltIQ's own measured figures. Hardware prices in it are indicative and at cost, and they vary with the market. For a firm size, BiltIQ measures your real workload during a paid pilot of 4–6 weeks, credited against the licence, before you buy any hardware.
Download the GPU sizing sheet
Or write to [email protected], or call or WhatsApp +91 89868 60088 (messages go straight to our CRM).