GPU to production-ready AI. No DevOps team required. No cold starts. No infrastructure to manage.
Most inference platforms hand you a GPU and wish you luck. We hand you a complete vertical: the silicon, the runtime, the models, the pipelines. You write the product. We run everything underneath.
This is the only managed inference platform built on infrastructure we own and operate, not capacity rented from a hyperscaler.
OpenAI-compatible endpoint. No cold starts. Models stay warm. Dynamic batching and KV cache optimization run automatically. Deploy in minutes. Scale to any volume.
import openai
client = openai.OpenAI(
api_key="sk_live_...",
base_url="https://api.bleedingedge.ai/v1"
)
response = client.chat.completions.create(
model="deepseek-r1-70b",
messages=[{"role": "user", "content": "..."}]
)No config. No tuning. Every request gets the full engine.
Incoming requests group into intelligent batches before hitting the GPU. Result: multiply inferences per second without adding hardware.
Previously computed context reused across requests. Fewer redundant compute cycles per token. Lower perceived latency at scale.
Multiple models on a single GPU. Dynamic capacity allocation. No wasted silicon on low-demand endpoints.
Token-by-token delivery as the GPU generates. First token perceived immediately, no waiting for full sequence.
Models stay loaded in GPU memory. Request one is as fast as request ten thousand.
VM-level scheduler distributes requests by model type, urgency, and current load. Maximum utilization. No human intervention.
Bring your own weights → Private endpoint in minutes.
Every layer of the stack is instrumented. Token throughput, per-model latency distributions, VRAM utilization, KV cache hit rate, batching efficiency, RAG pipeline performance, vector DB health, all in one view.
Talk to an architect or start building now.