Run enterprise AIfor up to 70% lower cost.
Score Labs SDK runs LLMs on CPU, GPU and hybrid CPU/GPU infrastructure. It lowers inference costs, supports more simultaneous users, and keeps your data inside infrastructure you control.
LLMs running on Score today
On the hardware and clouds you already run
up to 80%+ lower projected monthly inference costCustomer result. Projected monthly inference spend on Score, self-hosted, against the customer's previous hosted-API spend for the same record-processing workload. Accuracy judged against the original model on the same record set.
A medical records AI platform moved from a hosted API to Score.
2.7× more completed coding tasks per hour under peak loadPI coding agent, 100K-token context, Gemma 4-31B self-hosted. Scaled from 1× to 4× concurrent load on each platform: H100-SXM completion time rose from 142s to 1,434s; 2S Xeon 6960P running Score rose from 311s to 529s. Tasks per hour compared at 4× load.
Same coding agent, same 100K context, four times the concurrent load, one two-socket server.
3.9× more tokens per dollar in the named hardware comparisonQwen3-235B at 64K context, measured throughput. 8×H100-SXM: 132 TPS, $14.40/h, 33,000 tokens/$. 5× 2S Gen6 Xeon: 156 TPS, $4.40/h, 127,636 tokens/$. Assumes H100-SXM $320K/8 GPU/10.2 kW amortized over 3 years, Gen6 Xeon $35K/2 sockets/1.56 kW over 6 years, PUE 1.56, $0.14/kWh.
Five two-socket Xeon servers matching the throughput of one 8×H100 node, all costs included.
Customers, partners and ecosystem providers
IntelMicrosoft AzureRun the LLMs you want, on the hardware you have, all in your control.
Score sits between your applications and your servers. Applications call it the way they call any AI service; Score runs the model, optimizes the work, and routes each task to the right place.
Runs LLMs
The same LLMs you would run anywhere else, at full precision, with no swapping and no downgrading.
Uses CPUs, GPUs, or both
Each task goes to the hardware that suits it. Most work runs well on the servers you already buy.
Optimized compute
We use methods that dramatically reduce the compute required to run large tasks.
Keeps data inside your environment
On-premises or in your cloud account.
Real workloads, real results.
Score Labs Impact
“Score Labs showed us that we did not have to trade quality for control or cost. We achieved better results than our previous model approach while reducing projected inference expense by more than 80%. Just as important, we now have a path to keep sensitive information within infrastructure we control and continuously improve the system around the needs of our customers.”
Founder & CEO, a medical records AI platform
Benchmark your workload.
Send us a real workload. We run it on your models, your context lengths and your concurrency, and hand you the numbers either way.