Up to 3.4× more AI, on the same GPUs.
Yantrion is an AI inference algorithms company. We turn algorithmic advances into algorithm-aware optimized kernels and production-serving integrations — so the hardware you already own serves more inference: about 1.8× more in production, up to ~3.4× at the allocator ceiling, at the same accuracy and at or below stock latency where you run it. Reversible with a single flag.
Three serving stacks · two GPU vendors · the same result
Reproduced across three inference stacks and two GPU vendors — shipped in production on AMD, an out-of-tree plugin on vLLM, validated in our TensorRT-LLM integration.
What we do
From algorithm to production performance.
One end-to-end discipline. We own it from the idea to the running server — which is why the advances actually reach production, and why the results hold up.
Advance the algorithms
New inference algorithms, built around real production workloads — not incremental tuning, but algorithmic advances that change what a GPU can do.
Algorithm-aware kernels
We carry those advances to the metal ourselves — hardware-tuned GPU kernels on AMD Instinct and NVIDIA Blackwell, numerically validated against an fp32 reference.
Production-serving integrations
And land them where inference actually runs — SGLang, vLLM, and TensorRT-LLM — as drop-in integrations, not a research demo.
Proof, not promises
Not a paper. A measured system.
Every number is a real measurement against the identical stock server — and the same result is reproduced across three serving stacks and two GPU vendors.
Measured live in production serving software, on the same hardware — ~1.8× realized in production, up to ~3.4× at the allocator ceiling across the stacks we've validated.
Matches the stock model on both long-context retrieval and multi-step reasoning — the capability agents depend on is preserved.
On AMD/SGLang, per-token latency at production concurrency (32–128) is at or below stock (0.96–0.98×); GB10/vLLM carries a small ~7% small-batch overhead.
Turns off to your stock server exactly — no retraining, single-flag rollback.
Three serving stacks · two vendors · the same result
The same method, the same integration contract, the same validation discipline — reproduced across stacks. Retrieval matches stock on all three; every one reverts to the stock server exactly when off.
What it's worth
Same hardware. Far more inference.
When your GPU count is set by how much you can serve, more capacity per GPU flows straight to the bottom line — more users on the machines you have, or the same workload on far fewer of them.
1.8–3.4× the working set
More concurrent users, longer context, or more agents — about 1.8× realized in production, up to ~3.4× at the allocator ceiling, at the same quality.
Fewer GPUs where you're capacity-bound
Serve the same workload on fewer GPUs in the capacity-bound regime — up to ~3× fewer at the ceiling — directly lowering cost per token where modern agentic and long-context serving dominates.
Honest about the trade-offs
Accuracy at stock; latency at or below stock at production concurrency on AMD/SGLang (a small small-batch overhead on GB10); reversible with a single flag.
Product roadmap
One engine. Every layer of your agent stack.
Agentic serving
One flag — no model change, no retraining. 1.8–3.4× more concurrent agents at iso-accuracy, at or below stock latency at production concurrency, and reverts to your stock server exactly when off. Reproduced on three serving stacks — SGLang, vLLM, TensorRT-LLM — and two GPU vendors.
Flat per-GPU license
The Token Refinery
Coding agents drown models in tool output. The refinery keeps the bulk cheap and feeds the model only signal — your token bill shrinks, whichever model you run.
Pay per token refined
Persistent agent memory
Sessions that persist, migrate, and resume — memory that outlives the request, and models tuned to our algorithms so quality rises as the system scales.
Early-access program
Getting started
Prove it on your workload first.
Install
One flag — live on your cluster in an afternoon.
Verify
Side by side — your traces vs. the stock baseline.
No lock-in
Flag off anytime — the standard stack underneath.
The company we're building
An algorithm company — from first principles to the metal.
We build advanced inference algorithms and the hardware-tuned GPU kernels that carry them into real serving systems. From algorithm to production performance — that discipline is the company.
- 1
Advance the algorithms
New inference algorithms, built around real workloads — not incremental tuning, but algorithm-aware advances that change what a GPU can do.
- 2
Carry them to the metal
The hardware-tuned GPU kernels that turn an algorithm into production throughput — on AMD Instinct and NVIDIA Blackwell, numerically validated against an fp32 reference.
- 3
Own it end-to-end
Design → optimize → integrate as one discipline, shipping in real serving systems — SGLang and vLLM, in production. Not a paper. A running engine.
- 4
Solve the toughest problems
Extend that same discipline — algorithms, kernels, optimization — to the hardest problems in AI and computing. That is the company we're building.
We love the math, the algorithms, and the kernels — that love is the company.
Prove it on your workload
One flag. Your workload. Measured against your own baseline.
We don't ask you to take the numbers on faith. Run it on your model and your hardware, compare accuracy and cost side by side with your stock baseline, and switch it off if it doesn't hold. The claim is only as good as your measurement of it.
Write to us and we'll set up a measured pilot on your stack.