AI Model Optimization & Deployment Services
AI model optimization and deployment services including quantization, inference acceleration, vLLM deployment, Kubernetes workloads, and model monitoring.
Service Overview
Model optimization and deployment is the critical bridge between a trained AI model and a production system that serves users reliably and cost-effectively. At aimodels.in, we specialize in making AI models run faster, cheaper, and more efficiently without sacrificing quality. Our services cover quantization and compression to reduce model size, inference optimization with vLLM and TensorRT for maximum throughput, and Kubernetes-based deployment for elastic scaling. We handle the full spectrum from squeezing models onto edge devices to orchestrating GPU clusters that serve thousands of concurrent requests. Whether you are deploying a fine-tuned LLM, a vision model, or a multi-model system, we architect infrastructure that meets your latency, throughput, and cost targets. We implement monitoring and observability so you can detect performance degradation, track GPU utilization, and optimize spending over time. Every project includes benchmarking before and after optimization, deployment automation, and runbooks for operations teams. Our team has optimized and deployed models for startups and enterprises, handling everything from 7B parameter LLMs to billion-parameter multi-model systems, and we bring that operational rigor to every engagement.
How We Work — Our Process
A structured, transparent engagement model that ensures delivery quality at every step.
Model Profiling & Benchmarking
We profile your model to identify bottlenecks in latency, memory, and throughput, establishing baseline metrics for optimization targets.
1 weekQuantization & Compression
We apply quantization (INT8, INT4), pruning, and knowledge distillation to reduce model size and memory footprint while preserving accuracy.
1-2 weeksInference Engine Selection
We select and configure the optimal inference engine (vLLM, TGI, TensorRT, ONNX Runtime) based on your model type and performance requirements.
1 weekDeployment Architecture
We design the deployment topology including GPU allocation, auto-scaling rules, load balancing, and failover strategies for your traffic patterns.
1-2 weeksKubernetes & Infrastructure Setup
We deploy the model on Kubernetes with GPU scheduling, node pools, horizontal pod autoscaling, and resource quotas for efficient GPU utilization.
2 weeksMonitoring & Observability
We implement monitoring dashboards, alerting, and drift detection to track inference performance, GPU utilization, and model quality over time.
1 weekWhy Choose Us
Our key differentiators that set us apart in the AI services landscape.
Proven Speedup Results
Our optimization techniques consistently deliver 3-5x inference speedups and 60-70% cost reduction while maintaining model accuracy within 1% of the original.
vLLM & TGI Expertise
We are experts in high-performance inference engines including vLLM, Text Generation Inference, and TensorRT-LLM for maximum LLM serving throughput.
Production-Grade Kubernetes
We deploy models on Kubernetes with GPU scheduling, autoscaling, and resource management, giving you elastic infrastructure that scales with demand.
Quantization Without Quality Loss
We apply advanced quantization techniques including GPTQ, AWQ, and SmoothQuant that compress models to INT4 or INT8 with minimal accuracy degradation.
Full Observability Stack
We deploy monitoring with Grafana dashboards, Prometheus metrics, and custom alerts for latency, throughput, GPU utilization, and model quality drift.
Cost-Optimized Scaling
We configure auto-scaling and spot instance integration that keeps GPU costs low during off-peak hours while maintaining capacity for traffic spikes.
What We Offer
Detailed breakdown of each offering within this service category.
Quantization & Compression
Reduce model size and memory usage through quantization, pruning, and distillation techniques that preserve accuracy while cutting inference costs.
- Quantized model with INT4 or INT8 weights
- Accuracy comparison report before and after
- Memory and latency benchmark results
- Deployment-ready compressed model artifacts
Inference Optimization
Optimize inference performance using vLLM, TensorRT-LLM, ONNX Runtime, and custom kernels to maximize throughput and minimize latency.
- Configured inference engine with benchmark results
- Optimized inference server configuration
- Throughput and latency optimization report
- Batching and concurrency tuning documentation
vLLM & TGI Deployment
Deploy LLMs with high-performance serving engines like vLLM and Text Generation Inference, with continuous batching and PagedAttention for maximum throughput.
- vLLM or TGI inference server deployment
- Continuous batching and PagedAttention configuration
- Streaming and batch API endpoints
- Performance benchmarking against baseline
Kubernetes AI Workloads
Deploy and manage AI models on Kubernetes with GPU scheduling, auto-scaling, node pools, and resource management for elastic production infrastructure.
- Kubernetes cluster with GPU node pools
- Helm charts for model deployment
- Horizontal and vertical autoscaling configuration
- Resource quotas and GPU scheduling policies
Model Monitoring & Observability
Comprehensive monitoring for inference performance, GPU utilization, model drift, and quality metrics with dashboards, alerts, and automated retraining triggers.
- Grafana dashboards for inference metrics
- Prometheus metrics collection pipeline
- Alerting rules for latency and error thresholds
- Model drift detection and retraining triggers
Technology Stack
The tools, platforms, and frameworks we use to deliver this service.
| vLLM | High-throughput LLM serving | Expert |
|---|---|---|
| Text Generation Inference | Hugging Face LLM server | Expert |
| NVIDIA TensorRT-LLM | GPU inference optimization | Expert |
| ONNX Runtime | Cross-platform inference | Advanced |
| Kubernetes | Container orchestration | Expert |
| NVIDIA GPUs (A100, H100) | Inference hardware | Expert |
| Triton Inference Server | Multi-model serving | Advanced |
| GPTQ / AWQ / SmoothQuant | Quantization techniques | Expert |
| Grafana + Prometheus | Monitoring and alerting | Advanced |
| Terraform | Infrastructure as code | Advanced |
Use Cases & Industry Applications
Real-world scenarios where this service delivers measurable business impact.
Engagement Timeline & Impact Metrics
Project Timeline
| Phase | Duration | Key Deliverable |
|---|---|---|
| Profiling & Benchmarking | 1 week | Baseline metrics and bottleneck analysis |
| Quantization & Compression | 1-2 weeks | Compressed model with accuracy report |
| Inference & Deployment | 2-3 weeks | Production deployment on Kubernetes |
| Monitoring & Optimization | 1 week | Observability stack with dashboards and alerts |
Business Impact
| Metric | Before AI | After AI |
|---|---|---|
| Inference Latency | 800ms | 200ms |
| Throughput (req/sec) | 50 | 420 |
| GPU Utilization | 30% | 85% |
| Monthly Cost | ₹8.0L | ₹2.8L |
| Model Size | 14GB | 3.5GB |
Our Capabilities
| Capability | Status |
|---|---|
| INT4/INT8 quantization | Available |
| vLLM and TGI deployment | Available |
| Kubernetes GPU scheduling | Available |
| TensorRT optimization | Available |
| Model drift monitoring | Available |
| Auto-scaling and spot instances | Available |
Pricing & Packages
Transparent pricing for every engagement size. All packages include post-delivery support.
| Tier | Price | Timeline | Includes |
|---|---|---|---|
| Starter | ₹59,000 | 2-3 weeks | Single model optimization, quantization, basic deployment |
| Growth | ₹1,49,000 | 4-6 weeks | vLLM deployment, Kubernetes setup, monitoring, autoscaling |
| Enterprise | ₹3,49,000 | 6-10 weeks | Multi-model optimization, GPU clusters, full observability, retraining pipeline |
What Is Included
- Model profiling and baseline benchmarking
- Quantization and compression implementation
- Inference engine selection and configuration
- Kubernetes deployment with GPU scheduling
- Auto-scaling and load balancing setup
- Monitoring dashboards and alerting
- Performance optimization report
- 30 days post-launch support and tuning
If your optimized model does not achieve the agreed speedup and cost reduction targets within 30 days of deployment, we provide free optimization iterations until it does.
Book a Free Consultation
Speak with our AI experts about your specific requirements. We will assess your needs, recommend the right approach, and provide a detailed proposal within 48 hours.
Book Your Free Consultation →Frequently Asked Questions
How much speedup can I expect from model optimization?
Most models see 3-5x inference speedup after our optimization pipeline, which includes quantization, inference engine tuning, and deployment configuration. The exact improvement depends on your model architecture, hardware, and current deployment, which we assess during the profiling phase.
Does quantization affect model accuracy?
With modern techniques like GPTQ, AWQ, and SmoothQuant, accuracy loss is typically under 1% compared to the original FP32 model. We benchmark accuracy before and after quantization on your test data and only ship if the quality loss is within acceptable thresholds.
What is vLLM and why is it better for LLM serving?
vLLM is a high-throughput inference engine that uses PagedAttention for efficient memory management and continuous batching to maximize GPU utilization. It typically delivers 3-10x higher throughput than naive serving approaches, making it the standard for production LLM deployment.
Can you deploy models on our existing Kubernetes cluster?
Yes. We deploy on your existing Kubernetes infrastructure, configuring GPU node pools, scheduling policies, and resource quotas. We provide Helm charts and manifests that integrate with your CI/CD pipeline for automated model deployments and updates.
How do you handle GPU cost optimization?
We implement auto-scaling that adds or removes GPU nodes based on traffic, integrate spot instances for non-critical workloads, configure GPU sharing for multi-tenant efficiency, and use quantization to run on fewer or cheaper GPUs. Together these typically reduce GPU costs by 60-70%.
What monitoring do you set up for deployed models?
We deploy Grafana dashboards showing inference latency, throughput, error rates, GPU utilization, memory usage, and model quality metrics. We configure Prometheus for metrics collection and set up alerts for latency spikes, error thresholds, and GPU health issues.
Can you optimize models for edge devices?
Yes. We optimize models for edge deployment using ONNX Runtime, TensorRT, and hardware-specific toolkits for NVIDIA Jetson, mobile NPUs, and other edge accelerators. We apply aggressive quantization and pruning to fit models within edge memory and power constraints.
Do you support multi-model serving on shared GPU infrastructure?
Yes. We deploy multi-model serving with Triton Inference Server or vLLM that runs multiple models on shared GPUs with efficient memory management. This maximizes GPU utilization when you have several models with varying traffic patterns.
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