One API endpoint. Zero infrastructure code.
Drop-in compatible with standard OpenAI SDKs. Point your existing LangChain, LlamaIndex, or agent loops to Istidlal's high-throughput serverless gateway with zero code refactoring.
npm install openaiimport OpenAI from "openai"; // Initialize OpenAI client pointed to Istidlal Serverless Gatewayconst client = new OpenAI({ baseURL: "https://api.istidlal.com/v1", apiKey: process.env.ISTIDLAL_API_KEY,}); // Endpoint automatically cold-starts GPU & scales down to 0ms when idleconst completion = await client.chat.completions.create({ model: "llama-3.3-70b-instruct", messages: [ { role: "user", content: "Explain quantum GPU interconnects." }, ], temperature: 0.7,}); console.log(completion.choices[0].message.content);Eliminate idle GPU overhead completely
Engineered from silicon up for modern AI workloads. Get dedicated hardware isolation, sub-3s cold starts, and zero idle charges.
Sub-3s Cold Starts from True Zero
Legacy GPU clouds spend 3 to 5 minutes provisioning nodes, pulling container images, and loading weights into VRAM. Istidlal streams cached model layers over NVMe-oF fabrics directly into GPU HBM in under 2.4 seconds.
Zero Waste Scale-to-Zero
Stop paying for inactive GPUs running 24/7 during off-peak hours. When incoming request volume drops to zero, billing drops immediately to exactly zero with zero lingering container reservations.
Hardware Slice Isolation
Powered by NVIDIA Multi-Instance GPU (MIG). Each container runs in physically isolated GPU memory controllers, SM clusters, and cache slices with 100% deterministic QoS and zero noisy neighbors.
Per-Second Micro Billing
Billed strictly for the exact milliseconds your inference kernel executes. No rounding up to the nearest hour, no minimum commitments, and no cluster lock-in contracts.
Pre-Built AI Runtimes
Container images pre-optimized with FlashInfer, PagedAttention, and continuous batching. Spin up leading model engines instantly with maximum tokens-per-second throughput.
Drop-in SDK Compatibility
Zero code refactoring required. Simply point your OpenAI client base URL to Istidlal's high-speed endpoint and keep using your existing LangChain, LlamaIndex, or agent loops.
Serverless Model Performance
| Model Architecture | Hardware Slice | Cold Start Time | Per-Second Rate | Status |
|---|---|---|---|---|
| Llama 3.3 70B Instruct | 40GB Slice | 2.8s | $0.00044/sec | Instant Warm |
| DeepSeek R1 Sliced 32B | 20GB Slice | 1.9s | $0.00022/sec | Instant Warm |
| Mistral NeMo 12B | 10GB Slice | 1.2s | $0.00011/sec | Instant Warm |
| Qwen 2.5 32B Coder | 20GB Slice | 2.1s | $0.00022/sec | Instant Warm |