Production AI Inference on Dedicated GPUs

Shared APIs (OpenAI, Anthropic, Together AI) run your requests on multi-tenant infrastructure — you pay per token, face rate limits, and share GPU time. Dedicated GPU inference gives you your own GPU server. You get fixed monthly cost, no rate limits, predictable latency, and full control over your model and data. Cost crossover occurs at roughly 150,000–300,000 requests per month depending on model size.

vLLM is the default for all LLM inference (Llama, Mistral, DeepSeek, Qwen). It uses PagedAttention for memory-efficient KV cache management and continuous batching for high throughput. For non-LLM workloads (vision models, ONNX exports, speech), we use NVIDIA Triton. TGI and Ollama are available on request for HuggingFace-native models and dev/test environments.

Once deployed, vLLM stays warm — no cold starts on subsequent requests. The initial model load at startup (one-time per deployment) takes 30–120 seconds depending on model size. A 7B model loads in under 30 seconds. A 70B model loads in 60–120 seconds. For high-availability setups, we keep a warm standby node to eliminate restart latency entirely.

On a single A100 80GB with vLLM at batch size 8: a 7B model (FP16) achieves roughly 2,000–4,000 tokens/second. A 70B model (FP16) achieves roughly 400–800 tokens/second. H100 SXM provides approximately 2x the throughput of A100 for the same model due to higher memory bandwidth. Actual throughput depends on prompt length, context window, and concurrency — we benchmark your specific workload during onboarding.

Yes. Beyond LLM text inference, we deploy: embedding models (BGE, E5, Nomic-embed) for RAG pipelines, vision models (LLaVA, InternVL, Qwen-VL) for multimodal inference, and Whisper variants for speech-to-text. NVIDIA Triton handles multi-framework workloads (ONNX, TensorRT). Contact us with your model type and we'll scope the right hardware and runtime.

All inference runs in our Mumbai datacenter within Indian jurisdiction. Your prompts and completions stay on your dedicated GPU server — we collect only infrastructure metrics (GPU utilization, container health). We sign a Data Processing Agreement confirming no prompt data is logged, no data leaves India, and no data is used for model training. Air-gapped on-premise deployments available for highest-sensitivity requirements.

Our Bangalore NOC monitors all deployments around the clock. systemd restarts vLLM within 10 seconds on process crash. If the restart fails (OOM, CUDA error), the NOC investigates and intervenes. Median time from alert to resolution is under 15 minutes. Scale-tier clients get a dedicated on-call engineer with direct phone access.