llm-d Open Source Framework
The Hidden Tax on Your GPU Fleet
Inference costs do not scale linearly. As your GPU fleet grows, coordination overhead and scheduling waste create a cost cliff that silently destroys your margins. llm-d eliminates it.
Where Your Money Goes
Every hour your fleet runs, idle GPU capacity silently burns through your budget. Watch the money float away. This is happening right now on every over-provisioned cluster.
The Cost Cliff
Cost per token climbs as GPU count increases. The red zone is where scaling your fleet starts costing more per unit of work. Every dollar added returns fewer results.
How Your Budget Shrinks
Four compounding mechanisms that eliminate the GPU cost cliff and keep unit economics flat at scale.
Eliminate Redundant Spend
Every request is analyzed for overlap with work already done. If the computation exists in cache, you do not pay for it again.
Maximize Fleet Utilization
Requests are matched to the GPU that can serve them cheapest, the one with the right data already loaded in memory.
Right-Size Your Hardware
Expensive compute GPUs handle compute-heavy work. Cheaper memory GPUs handle memory-heavy work. No GPU sits idle doing the wrong job.
Savings Compound at Scale
More users means higher cache reuse. Unlike standard routing where costs spike at scale, llm-d gets cheaper per request as you grow.
Where Every Dollar Goes
GPU compute is only 72% of the invoice. Here is the full monthly cost your finance team needs to see.
How Does Your Cost Compare?
Estimated monthly cost for 70B model on 32x H100 GPUs at 100 QPS across major providers and deployment strategies.
Stop Overpaying for Inference
A ready-to-send summary you can drop into any budget review. Deploy llm-d and turn every GPU dollar into more inference throughput.
llm-d is open source, CNCF Sandbox. Built for platform teams who refuse to leave GPU performance on the table.