Agents that optimize inference across the whole stack.
Our agents find the bottleneck — scheduling, decoding, kernels, memory pressure or hardware fit — then measure their way to the fastest configuration for your traffic and keep measuring after it ships.
Real performance is not won at a single layer.
A faster kernel behind a bad scheduler is wasted. A better quantization recipe on the wrong GPU is wasted. The wins compound only when engine, kernels, precision, decoding and hardware are searched together, against the outcome you actually care about.
Scheduling & batching
Continuous batching, chunked prefill, prefix caching and KV-cache tiering, tuned to your real arrival curve and prompt lengths instead of a benchmark’s.
Decoding
Speculative decoding with drafts retrained per endpoint, FP8 / FP4 / MXFP4 precision recipes, and prefill/decode disaggregation where the crossover actually pays.
Kernels
Fused attention, MoE dispatch and decode GEMMs written for the exact shapes your model produces, in CUDA, HIP or Triton depending on the silicon.
Hardware fit
Tensor-parallel degree, replica layout and accelerator choice decided by measured tokens per dollar, not by what was in the rack.
Find it. Try many paths. Ship the measured winner.
The loop runs against production traffic for as long as the endpoint exists. Nothing ships on a hunch.
Profile the live endpoint
The agent replays held-out traffic and attributes latency and throughput loss to scheduling, decoding, kernels, memory pressure or hardware fit, per request class and per percentile.
Generate and measure candidates
Candidate configurations across engine, batching, decoding, quantization, kernels and hardware are built, run against the same replay, and scored on your SLOs. Losers are discarded; the reasons are kept.
Verify, then roll out
The fastest candidate must pass the correctness gate. It goes out shadow, then canary, then full, behind the same URL. It becomes the baseline the next search has to beat.
What the agents are allowed to change.
Serving engines
vLLM, SGLang and TensorRT-LLM as interchangeable back ends. Scheduler policy, KV-cache layout, prefix-cache sizing and chunking are all search dimensions.
Kernels
Custom CUDA, HIP and Triton kernels for attention, MoE routing and low-bit GEMMs, generated and benchmarked per architecture and per shape, and verified against the reference before they enter the search space.
Decode strategies
Speculative decoding, FP8 and FP4 quantization, mixed per-layer precision, batching policy, expert sharding for MoE models and prefill/decode split.
The right accelerator is an empirical question.
The same workload is measured across accelerators and the answer changes every hardware generation. We run wherever tokens per dollar is highest — in your cloud today, on partner clouds as dedicated capacity comes online.
NVIDIA
H100, B200 and B300. NVFP4 and FP8 recipes, custom decode kernels, TensorRT-LLM and vLLM back ends.
AMD
MI300X, MI350X and MI355X. FP4 and FP8 recipes, HIP kernels, MoE dispatch tuned for Infinity Fabric topologies.
Next up
New accelerators are brought up as customer demand and access allow. Ask us about the silicon you are evaluating.
Correctness first. Always.
Faster and wrong is worse than slow. Every candidate configuration clears four gates before it can serve a single production request.
Numerical fidelity
Logit and hidden-state deltas against a BF16 reference on a fixed prompt set. Tolerances are per model, set from the reference’s own run-to-run noise.
Task-level evals
Accuracy on public benchmarks plus your own eval set if you give us one. The accuracy delta budget is an SLO you set, and it defaults to tight.
SLO replay
Held-out production traffic is replayed at recorded arrival times. TTFT, inter-token latency and p99 must hold at the recorded concurrency.
Shadow run
The candidate serves a mirrored slice of live traffic without returning to users. Only then does it canary, then take the URL.
Kernel and recipe results are directional, from internal benchmarks, and will differ per workload.
Your workload, optimized.
Bring your model and real traffic. Our agents profile the full serving stack and deploy only verified improvements.