Pym Labs is the performance engine for AI inference and training: a control plane that continuously decides how every workload should run, so every model performs as if the best infrastructure team in the world were tuning it, all the time.
Open-source engines like vLLM and SGLang now serve enormous production traffic, and techniques once considered specialized (continuous batching, paged KV caching, optimized attention kernels, low-precision inference) are becoming standard. That's real progress. But engines provide the mechanisms for running a model; they don't decide the best way to combine those mechanisms for your workload.
Most important inference optimizations are workload-dependent. And the correct answer changes over time as models change, traffic shifts, engines improve, and hardware turns over.
With Pym, make every model run as if the best infrastructure team in the world were optimizing it, continuously.
Today, that work is done by hand. At frontier labs and the largest AI companies, specialized infrastructure teams profile workloads, benchmark configurations, tune kernels and serving policies, manage heterogeneous fleets, and continuously validate model quality. Most companies can't maintain a team like that, so most AI workloads run much closer to generic configurations than to their practical optimum. The cost shows up everywhere:
The closest analogy is the query optimizer in a database. A user specifies the result they want; the optimizer chooses an execution plan based on the query, the data, the hardware, and the state of the system. AI infrastructure largely lacks the equivalent. Pym continuously profiles real workloads, searches the available configuration space, evaluates candidate configurations, and adapts as the workload, and the infrastructure underneath it, changes.
Pym observes production traces and builds a model of the workload itself: whether it's compute-bound, memory-bandwidth-bound, cache-sensitive, latency-constrained, throughput-constrained, or some mix. It measures behavior at the request and token level, not just synthetic benchmarks or fleet-wide averages.
Before optimizing a workload, understand what is actually limiting it.
Pym searches across engine, hardware, precision, parallelism, batching, cache policy, speculative decoding, and prefill/decode topology, optimizing not for the highest benchmark throughput, but for the best configuration under a workload's actual economic, latency, reliability, and quality constraints. That search includes composing memory and cache systems like LMCache, Mooncake, and HiCache, training workload-specific speculative decoding models on a customer's own traffic, and searching across FP8, MXFP, and mixed-precision formats. Offline search identifies promising configurations; live traffic adds information; the system re-evaluates when the model, workload, hardware, or software changes.
Pym operates across inference engines rather than requiring customers to standardize on one. A common deployment specification can be translated into vLLM, SGLang, TensorRT-LLM, vLLM-Omni, and future runtimes. We don't believe inference converges to a single engine. Different engines stay better in different regimes, and those advantages shift over time. The more durable abstraction isn't another universal engine. It's the system that decides which engine to use.
Many of the most valuable optimizations aren't perfectly lossless. Quantization can alter outputs. Precision changes can create small divergences. Different execution paths can shift model behavior in ways that are hard to infer from system metrics alone. For some applications that's irrelevant. For others it's unacceptable. Before an optimization ships, the customer defines the quality boundary it has to stay inside.
Can we run this workload more efficiently?
Did doing so change anything that matters?
We view deployment location as a secondary question. The product is the optimization and verification system. A customer gets the same intelligence whether the underlying accelerators belong to Pym, a hyperscaler, or the customer itself.
Not a benchmark. Not a recommendation engine. A closed loop that decides, verifies, and adapts to how AI infrastructure actually runs, continuously.
If you invest in AI infrastructure, or you run inference or training at a scale where every point of GPU utilization matters, we'd like to talk.