AlphaTango
Use cases/06 Startups

The GPU-limited startup

Can't get more GPUs this quarter? AlphaTango frees the ones you have. In our simulation, a startup's work dropped from 45 GPUs to 10, 4.6× fewer: 4.8× the on-time answers from the same GPUs.

Startups and teams on a fixed GPU allocation
4.8× the on-time answers from the same GPUs

Case 06 / Startups

01Before / after

Every tool at factory settings
45
GPUs in use
Promise kept 9 of 10 days
AlphaTango, whole system
10
GPUs in use
Promise kept 10 of 10 days

Showcase Search simulation, 10 unseen days, two open models on NVIDIA H200 GPUs. 4.8× = 79% lower cost per on-time answer.

02How AlphaTango wins

Local settings. Global goal.

Left: what every tool does at the settings its vendor ships. Middle: the settings AlphaTango changed, tool by tool, each kept only because the whole service got better. Right: the result.

The GPU-limited startup: factory baseline, the settings AlphaTango changed, and the result. Click for more details.

03The story

4.8× the on-time answers from the same GPUs

For: Startups, research labs and internal platforms that cannot get more GPUs this quarter.

The situation

A startup owns a fixed block of NVIDIA H200 GPUs and cannot get more this quarter. It runs a small and a medium open model for chat, fast live chat, questions over documents and coding agents: 48 requests a second on average, with a daily peak 1.5 times the average. At factory settings its work used 45 GPUs on average and still missed the speed promise on 1 of 10 days. Most of those GPUs were holding memory, not doing work.

What AlphaTango does

AlphaTango stores the models with fewer bits, gives the medium model two GPUs per copy and splits the small model's GPUs in half, then tells the autoscaler that each copy now carries more and has the front door send every conversation back to the copy that remembers it. Changed alone, any one of these just moves the bottleneck to the next tool.

The proof

On 10 unseen days the same work needed 10 GPUs instead of 45 (4.6× fewer), at 4.8× lower cost per on-time answer (79% less), and the promise kept on 10 of 10 days, against 9 of 10 for factory settings. For an owner, that is 4.8× the on-time answers from the same GPUs. A cheaper version was rejected because it kept the promise on only 6 of 10 days.

Next step

If you cannot buy more GPUs, let us free the ones you have. Send us a day of traffic to start.

The fine print

The GPUs are priced at rental rates ($2,787 a day down to $576) to show what the same work would cost. Without the fewer-bits change, AlphaTango is still 54% cheaper, with the promise kept on 10 of 10 days.

04Watch

The case in 37 seconds.

Narrated walk-through of the situation, what AlphaTango changes and the result.

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