Turn compute into
Tokens efficiently
A self-owned heterogeneous compute base with high-performance inference for leading open-source models and Agent-native optimization.
Continuously lower your cost per million Tokens with deterministic SLA for your applications.
Core Capabilities
From the hardware base to the inference engine, every layer is designed for Token production efficiency.
Heterogeneous Compute Base
NVIDIA serves as the primary tier (B300 with 288GB HBM per card), AMD / domestic GPU / NPU as the secondary tier, and legacy NVIDIA settles naturally into the economy tier. Three clearly divided tiers match hardware to workload characteristics.
Full Model Coverage
Leading open-source models including DeepSeek, Qwen, GLM, Llama, and Kimi, covering MoE and multimodal architectures. Performance baselines and deployment matrices across card types and sizes ensure the best price-performance fit.
Deep Engine Optimization
Deep custom optimization on top of vLLM / SGLang / TensorRT-LLM. PD separation, KV Cache, continuous batching, speculative decoding, and multi-precision quantization work together to maximize throughput and minimize latency.
Technical Highlights
Differentiation built on verifiable engineering depth, not market narrative.
Agent-Native KV Data Plane
Purpose-built for Agent long sessions, high prefix reuse, and burst fan-out. Higher cache hit rates can cut effective compute cost by 80-90%, opening a significant cost gap over general-purpose inference.
Cache blocks are independent of request composition, so any prefix is reusable without per-request allocation.
Supports cache updates after tool-call results and context injection without full recomputation.
HBM → DRAM → NVMe → network storage pool, automatically tiered by access heat.
Cross-node KV is addressable; requests are routed automatically to instances with cache hits.
Cross-Architecture Tiered Scheduling
Routes requests in real time to the most economical hardware tier by request characteristics (context length, SLO, model size), minimizing per-token cost while guaranteeing SLA.
Frontier models · long context · tight SLO, served by NVIDIA B300 series
Regular decode · batch inference on AMD / domestic GPU / NPU
Small-model assistance on legacy NVIDIA that settles naturally
Peak overflow · burst fan-out with on-demand scaling
Cross-Architecture KV Plane
Lets KV state on different vendors' hardware perceive, migrate, and reuse each other. This is an area with no ready-made open-source answer today, and one of the hardest competitive moats to replicate.
A unified KV abstraction supporting cross-hardware storage and restoration
KV migration channels between NVIDIA ↔ AMD ↔ domestic GPU
Quantifies migration cost to decide whether migration beats recomputation
Numerical consistency validation to ensure equivalent output across migration
Multi-Architecture Production Delivery
Teams that can run NVIDIA and non-NVIDIA hardware reliably on the same production system are extremely rare. From software-stack versioning to fault-domain isolation, every step has been production-verified.
Strict control of the driver · runtime · inference-engine version matrix to avoid compatibility risk
Convert weights once and deploy across architectures without duplicated engineering
Cross-architecture output alignment with quantization error within acceptable thresholds
Hardware failures across architectures do not propagate; a single-architecture anomaly never affects the whole service
Inference Engine Stack
From request intake to Token output, every layer has a clear optimization goal.
Contact us about inference solutions
Whether you need API access, private deployment, or deep technical collaboration, our team is ready to help. Tell us your workload characteristics and we will design the optimal solution.