GPU Inference Optimization. Engineering Drawing Automation.
Two products. Measured results.
Built on & Optimized for
Why Yantrion
Kernel-level work
Custom CUDA and Triton kernels. Architecture-specific tuning. No wrappers, no configs.
Numbers, not claims
Before/after benchmarks on your hardware, your models, your workload. You verify everything.
4–6 week sprints
Audit in 1 day. Production results in a month. No long engagements.
What We Do
Inference Optimization
Faster inference. Lower GPU bill.
Custom CUDA/Triton kernels, KV-cache quantization, attention tuning, and batching strategy applied to your stack. Typical outcome: 2–7x throughput, 30–60% lower GPU spend.
Engineering Copilot
Your Engineers Are Drawing the Same Diagrams Over and Over
Our AI copilot lives inside AutoCAD and generates single-line diagrams, electrical schematics, and engineering drawings from specifications — in minutes, not hours.
What We Optimize
Constrained Decoding
Custom kernels for grammar-aware, structured output generation
Tool Calling
Optimized function-calling pipelines with speculative decoding
Attention / MLA
Architecture-specific attention kernels and flash attention tuning
KV-Cache
FP8 quantization, prefix caching, page allocation
Batching & Scheduling
Chunked prefill, continuous batching, speculative decoding
Multi-GPU / Tensor Parallel
TP=2/4/8 across NCCL/GLOO, sync overhead reduction
Free inference audit. Real numbers.
We benchmark your stack and report current vs. achievable tokens/sec, top 3 optimizations, and estimated GPU cost savings. Takes 1 day. No commitment.