Model Quantization & Hardware Inference
Akshora AI Labs converts unoptimized model weights into TensorRT engines using INT8 and FP4 quantization, cutting inference costs by 60% or more and maximizing throughput per dollar. The optimized models run on AWS, RunPod or local GPUs and are served with TensorRT-LLM or vLLM, with CUDA tuning where needed.
What's included
- TensorRT engine conversion with INT8 and FP4 quantization
- Inference cost reduction of 60% or more
- LLM serving with TensorRT-LLM and vLLM
- CUDA tuning for throughput per dollar
- Deployment on AWS, RunPod or local GPUs
Common questions
How much can quantization reduce inference cost?
Akshora AI Labs reports inference cost reductions of 60% or more by converting weights to TensorRT engines with INT8 and FP4 quantization. The actual saving depends on your model, hardware and accuracy tolerance, which is measured during calibration.
Which GPUs and clouds are supported?
Optimized engines can run on AWS, RunPod or your own local GPUs, and on edge NVIDIA Jetson devices for computer vision workloads.
Other services
Custom Edge Computer Vision & RTSP
Multi-camera RTSP video analytics on NVIDIA Jetson for PPE enforcement, intruder zoning and ANPR, built with YOLOv10 and DeepStream at sub-85ms latency.
WhatsApp & Vernacular Voice Agents
Autonomous WhatsApp and voice agents that handle orders, bookings and KYC in Hindi, Hinglish and regional Indian languages using LiveKit and Whisper.
Deterministic Document & GST AI
Fine-tuned LayoutLM and OCR that parse Indian GST invoices, bilties and transport receipts at 99.4% precision and sync to Tally Prime XML and Zoho.
Private VPC RAG & Vector Engines
Self-hosted RAG with Qdrant or Milvus and quantized Llama 3 models inside your private VPC, with zero cloud egress for sensitive documents.
Custom Enterprise Platforms
Tailor-made internal tools, CIMS, client portals and real-time telemetry consoles built with Next.js 14, FastAPI and Docker for enterprise operations.