YC W26 startup IonRouter has launched an inference API built on its custom IonAttention engine, targeting teams frustrated by the trade-off between speed and cost in AI deployment. Founded by Veer and Suryaa, the company addresses a common pain point: existing providers force users to choose between expensive always-on GPUs or DIY solutions requiring vLLM configuration and dealing with cold starts.

IonRouter's approach centers on a C++ inference runtime specifically optimized for NVIDIA Grace Hopper (GH200) hardware. Rather than treating GH200 as a compatibility target, IonRouter exploits the architecture's 900 GB/s coherent CPU-GPU link, 452GB of LPDDR5X memory, and 72 ARM cores. The engine delivers three key innovations: hardware cache coherence for zero-cost dynamic parameters, immutability-driven KV block writeback reducing eviction stalls from 10ms to under 0.25ms, and phantom-tile attention scheduling cutting attention time by over 60% at small batch sizes.

Benchmarks show IonRouter achieving 588 tokens per second on VLM workloads versus Together AI's 298, though p50 latency currently sits at 1.46 seconds compared to 0.74 seconds for competitors. Pricing follows a per-token model with no idle costs: GPT-OSS-120B costs $0.02 in / $0.095 out, while Qwen3.5-122B runs $0.20 in / $1.60 out. The API requires just one line of code change to existing OpenAI client implementations.