NVIDIA NIM Deployment Strategies for Agentic AI Production

NVIDIA Inference Microservices (NIM) represents a critical component of the NVIDIA AI platform and features prominently in the NCP-AAI certification exam. As organizations move agentic AI systems from prototypes to production, the ability to deploy, optimize, and scale AI models efficiently becomes paramount. This comprehensive guide covers everything you need to know about NVIDIA NIM deployment for NCP-AAI exam success and real-world agentic AI applications.

What is NVIDIA NIM?

Core Concept

NVIDIA Inference Microservices (NIM) is a set of optimized, containerized microservices that simplify the deployment of AI models in production environments. NIM packages together:

1. Optimized Inference Engine: TensorRT-LLM or Triton Inference Server
2. Pre-configured Runtime: CUDA libraries, dependencies, and drivers
3. Model Artifacts: Pre-optimized or customizable model weights
4. API Endpoints: RESTful APIs for easy integration
5. Deployment Tooling: Docker containers, Kubernetes manifests, Helm charts

Why NIM Matters for Agentic AI:

• Rapid Deployment: From model selection to production in minutes (not weeks)
• Performance Optimization: TensorRT delivers 3-5x faster inference vs. unoptimized
• Consistency: Same API across different models and hardware
• Enterprise Features: Security, monitoring, multi-tenancy out of the box
• Cost Efficiency: Optimized GPU utilization reduces infrastructure costs by 40-60%

NCP-AAI Exam Coverage

NIM appears across multiple exam domains:

Estimated NIM-Related Questions: 10-15 out of 60-70 total questions (15-20%)

NIM Architecture Fundamentals

NIM Types and Use Cases

NVIDIA offers several NIM variants for different AI workloads:

1. LLM NIMs (Language Models)

• Purpose: Serve large language models for agentic AI reasoning
• Examples: Llama 3, Mistral, Mixtral, Nemotron
• Use Cases: Agent reasoning, planning, decision-making, natural language interfaces
• Optimization: TensorRT-LLM, FP8 quantization, PagedAttention

2. Embedding NIMs

• Purpose: Generate vector embeddings for RAG and semantic search
• Examples: NV-Embed-v2, E5-Mistral
• Use Cases: Knowledge retrieval, document search, similarity matching
• Optimization: Batched encoding, cached embeddings

3. Reranker NIMs

• Purpose: Rerank retrieved documents for improved RAG quality
• Examples: BGE-reranker, NVIDIA Reranker
• Use Cases: Two-stage RAG pipelines, search quality improvement
• Optimization: Cross-encoder acceleration

4. Multimodal NIMs

• Purpose: Process images, audio, video alongside text
• Examples: CLIP, Flamingo, multimodal LLMs
• Use Cases: Vision agents, multimodal understanding, content generation
• Optimization: Vision transformer (ViT) acceleration

5. Domain-Specific NIMs

• Purpose: Specialized models for industries (healthcare, finance, etc.)
• Examples: BioNeMo for drug discovery, FinBERT for finance
• Use Cases: Domain-specific agentic AI applications
• Optimization: Domain-tuned, compliant with industry regulations

NIM Architecture Components

┌─────────────────────────────────────────────────┐
│           NVIDIA Inference Microservice         │
├─────────────────────────────────────────────────┤
│  Application Layer                              │
│  ├─ RESTful API (OpenAI-compatible)             │
│  ├─ gRPC API (high performance)                 │
│  └─ WebSocket (streaming)                       │
├─────────────────────────────────────────────────┤
│  Orchestration Layer                            │
│  ├─ Request routing and load balancing          │
│  ├─ Batching and queueing                       │
│  ├─ Caching and memoization                     │
│  └─ Monitoring and telemetry                    │
├─────────────────────────────────────────────────┤
│  Inference Engine                               │
│  ├─ TensorRT-LLM (optimized LLM serving)        │
│  ├─ Triton Inference Server (multi-framework)   │
│  └─ Custom CUDA kernels                         │
├─────────────────────────────────────────────────┤
│  Model Layer                                    │
│  ├─ Quantized models (FP8, INT8, INT4)          │
│  ├─ Optimized model graphs                      │
│  └─ Model artifacts and weights                 │
├─────────────────────────────────────────────────┤
│  Hardware Abstraction                           │
│  ├─ CUDA runtime                                │
│  ├─ cuBLAS, cuDNN libraries                     │
│  └─ Multi-GPU support                           │
└─────────────────────────────────────────────────┘

Deploying NIMs for Agentic AI

Deployment Pattern 1: Single-Agent Single-NIM

Architecture:

Agent Application → LLM NIM → Response

When to Use:

• Simple agents with single LLM requirement
• Prototyping and development
• Low-traffic applications (<100 requests/min)

Deployment Example:

# Pull NIM container
docker pull nvcr.io/nvidia/nim/meta/llama-3.1-70b-instruct:latest

# Run NIM with GPU
docker run -d \
  --gpus all \
  --name llm-nim \
  -p 8000:8000 \
  -e NIM_API_KEY=your-api-key \
  nvcr.io/nvidia/nim/meta/llama-3.1-70b-instruct:latest

# Test NIM
curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta/llama-3.1-70b-instruct",
    "messages": [{"role": "user", "content": "Explain agentic AI"}],
    "max_tokens": 500
  }'

Configuration Options:

• NIM_MAX_CONCURRENT_REQUESTS: Control concurrency (default: 64)
• NIM_CACHE_SIZE_GB: Set KV cache size (default: auto)
• NIM_TENSOR_PARALLEL: Multi-GPU tensor parallelism (for large models)

Deployment Pattern 2: Multi-Agent RAG Pipeline

Architecture:

Query → Agent Orchestrator
         ├─ Embedding NIM (query encoding)
         ├─ Vector Database
         ├─ Reranker NIM (context refinement)
         └─ LLM NIM (response generation)

When to Use:

• RAG-based agents
• Knowledge-intensive applications
• Production systems with 100-10K requests/min

Deployment Example (Docker Compose):

version: '3.8'
services:
  embedding-nim:
    image: nvcr.io/nvidia/nim/nvidia/nv-embed-v2:latest
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    ports:
      - "8001:8000"
    environment:
      - NIM_MAX_CONCURRENT_REQUESTS=128
      - NIM_BATCH_SIZE=32  # Batch embeddings for efficiency

  reranker-nim:
    image: nvcr.io/nvidia/nim/nvidia/nv-reranker:latest
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    ports:
      - "8002:8000"

  llm-nim:
    image: nvcr.io/nvidia/nim/meta/llama-3.1-70b-instruct:latest
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 2  # 70B model benefits from 2 GPUs
              capabilities: [gpu]
    ports:
      - "8003:8000"
    environment:
      - NIM_TENSOR_PARALLEL=2
      - NIM_MAX_TOKENS=2048

Agent Code Integration:

import requests

class RAGAgent:
    def __init__(self):
        self.embedding_nim = "http://localhost:8001"
        self.reranker_nim = "http://localhost:8002"
        self.llm_nim = "http://localhost:8003"

    def query(self, user_query: str) -> str:
        # 1. Embed query
        query_embedding = self._embed(user_query)

        # 2. Retrieve from vector DB
        documents = self._retrieve(query_embedding)

        # 3. Rerank documents
        reranked_docs = self._rerank(user_query, documents)

        # 4. Generate response with LLM
        response = self._generate(user_query, reranked_docs)

        return response

    def _embed(self, text: str):
        response = requests.post(
            f"{self.embedding_nim}/v1/embeddings",
            json={"input": text, "model": "nv-embed-v2"}
        )
        return response.json()["data"][0]["embedding"]

    def _rerank(self, query: str, documents: list):
        response = requests.post(
            f"{self.reranker_nim}/v1/rerank",
            json={
                "query": query,
                "documents": documents,
                "top_n": 5
            }
        )
        return response.json()["results"]

    def _generate(self, query: str, context: list):
        prompt = f"Context:\n{context}\n\nQuestion: {query}\nAnswer:"
        response = requests.post(
            f"{self.llm_nim}/v1/chat/completions",
            json={
                "model": "meta/llama-3.1-70b-instruct",
                "messages": [{"role": "user", "content": prompt}]
            }
        )
        return response.json()["choices"][0]["message"]["content"]

Deployment Pattern 3: Multi-Agent Swarm

Architecture:

Orchestrator Agent
 ├─ Research Agent (LLM NIM 1)
 ├─ Analysis Agent (LLM NIM 2)
 ├─ Code Agent (Code LLM NIM)
 └─ Summarization Agent (LLM NIM 3)

When to Use:

• Complex multi-agent workflows
• Specialized agents for different tasks
• High-throughput, parallel agent execution

Kubernetes Deployment:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: llm-nim-pool
spec:
  replicas: 4  # Pool of LLM NIMs for agent swarm
  selector:
    matchLabels:
      app: llm-nim
  template:
    metadata:
      labels:
        app: llm-nim
    spec:
      containers:
      - name: llm-nim
        image: nvcr.io/nvidia/nim/meta/llama-3.1-70b-instruct:latest
        resources:
          limits:
            nvidia.com/gpu: 2
        ports:
        - containerPort: 8000
        env:
        - name: NIM_TENSOR_PARALLEL
          value: "2"
---
apiVersion: v1
kind: Service
metadata:
  name: llm-nim-service
spec:
  selector:
    app: llm-nim
  ports:
  - protocol: TCP
    port: 8000
    targetPort: 8000
  type: LoadBalancer  # Distributes agent requests across NIM replicas

Benefits:

• Load balancing across multiple NIM instances
• Fault tolerance (if one NIM fails, others continue)
• Horizontal scaling (add replicas as agent demand increases)

NIM Optimization Strategies

1. Quantization for Performance

Quantization Levels:

NIM Quantization Configuration:

docker run -d \
  --gpus all \
  -p 8000:8000 \
  -e NIM_QUANTIZATION=fp8 \  # Enable FP8 quantization
  nvcr.io/nvidia/nim/meta/llama-3.1-70b-instruct:latest

NCP-AAI Exam Tip: Know when to use which quantization level based on latency/quality trade-offs.

2. Batching and Throughput Optimization

Static Batching:

• Waits for N requests before inference (reduces GPU idle time)
• Pros: Maximum GPU utilization
• Cons: Higher latency for first requests in batch

Dynamic Batching:

• Waits up to T milliseconds, then processes whatever requests arrived
• Pros: Balances latency and throughput
• Cons: More complex to configure

Continuous Batching (PagedAttention):

• Processes requests as they arrive, dynamically batching at token level
• Pros: Best of both worlds (low latency + high throughput)
• Cons: Requires PagedAttention support (vLLM, TensorRT-LLM)

NIM Configuration:

docker run -d \
  --gpus all \
  -p 8000:8000 \
  -e NIM_MAX_BATCH_SIZE=32 \
  -e NIM_BATCH_TIMEOUT_MS=50 \  # Dynamic batching: wait up to 50ms
  nvcr.io/nvidia/nim/meta/llama-3.1-70b-instruct:latest

3. KV Cache Optimization

KV Cache Basics:

• Stores key-value tensors from previous tokens (avoids recomputation)
• Critical for long-context agents (multi-turn conversations, large RAG contexts)

Sizing KV Cache:

KV Cache Size (GB) = (2 × layers × heads × head_dim × max_tokens × batch_size × 2 bytes) / 1e9

Example (Llama 3.1 70B, FP16):
= (2 × 80 × 64 × 128 × 4096 × 32 × 2) / 1e9
≈ 40 GB

NIM Configuration:

docker run -d \
  --gpus all \
  -p 8000:8000 \
  -e NIM_CACHE_SIZE_GB=40 \  # Reserve 40GB for KV cache
  -e NIM_MAX_TOKENS=4096 \   # Support up to 4K token contexts
  nvcr.io/nvidia/nim/meta/llama-3.1-70b-instruct:latest

PagedAttention for KV Cache:

• Memory-efficient KV cache management
• Reduces memory waste by 20-40%
• Automatically enabled in TensorRT-LLM NIMs

4. Multi-GPU Deployment

Tensor Parallelism:

• Splits model layers across multiple GPUs
• Use Case: Large models (>40B parameters) that don't fit on single GPU
• Pros: Enables serving very large models
• Cons: Inter-GPU communication overhead

Pipeline Parallelism:

• Different layers on different GPUs (sequential processing)
• Use Case: Very deep models, limited inter-GPU bandwidth
• Pros: Minimal communication overhead
• Cons: Lower GPU utilization (sequential processing)

Hybrid Parallelism:

• Combines tensor + pipeline parallelism
• Use Case: Massive models (100B+ parameters) on GPU clusters

NIM Multi-GPU Configuration:

# Tensor parallelism across 4 GPUs
docker run -d \
  --gpus '"device=0,1,2,3"' \
  -p 8000:8000 \
  -e NIM_TENSOR_PARALLEL=4 \
  nvcr.io/nvidia/nim/meta/llama-3.1-405b-instruct:latest

NIM Monitoring and Observability

Key Metrics to Monitor

1. Latency Metrics

• Time to First Token (TTFT): How fast agent gets first response token
  • Target: <500ms for interactive agents
• Inter-Token Latency (ITL): Time between subsequent tokens
  • Target: <50ms for streaming responses
• Total Request Latency: End-to-end request time
  • Target: <2s for 100-token responses

2. Throughput Metrics

• Requests per Second (RPS): Total request handling capacity
• Tokens per Second (TPS): Token generation throughput
  • Target: >1000 TPS for production systems
• Effective Batch Size: Average number of concurrent requests processed

3. Resource Utilization

• GPU Utilization: Percentage of GPU compute used
  • Target: >70% for cost efficiency
• GPU Memory: Current vs. available memory
  • Monitor: Avoid OOM errors
• KV Cache Hit Rate: Percentage of cache hits (for multi-turn agents)
  • Target: >50% for conversational agents

4. Quality Metrics

• Error Rate: Percentage of failed requests
  • Target: <0.1%
• Timeout Rate: Requests exceeding max latency
  • Target: <1%
• Hallucination Rate: (Requires LLM-as-judge or human eval)

NIM Monitoring Tools

1. NVIDIA Triton Metrics (Built-in)

# Access Prometheus metrics endpoint
curl http://localhost:8000/metrics

# Key metrics:
# - nv_inference_request_success (successful requests)
# - nv_inference_request_duration_us (latency)
# - nv_gpu_utilization (GPU usage)
# - nv_gpu_memory_used_bytes (memory consumption)

2. Prometheus + Grafana Dashboard

# docker-compose.yml
version: '3.8'
services:
  llm-nim:
    image: nvcr.io/nvidia/nim/meta/llama-3.1-70b-instruct:latest
    # ... (config from above)

  prometheus:
    image: prom/prometheus:latest
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
    ports:
      - "9090:9090"

  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=admin

prometheus.yml:

scrape_configs:
  - job_name: 'nim'
    static_configs:
      - targets: ['llm-nim:8000']
    metrics_path: '/metrics'
    scrape_interval: 15s

3. NVIDIA NeMo Observability (Enterprise)

• End-to-end agent workflow tracing
• Automatic latency breakdown (retrieval, reranking, generation)
• Cost tracking (GPU-hours, token usage)
• A/B test analytics

NIM Security and Compliance

Authentication and Authorization

API Key Authentication:

# Set API key during NIM deployment
docker run -d \
  --gpus all \
  -p 8000:8000 \
  -e NIM_API_KEY=your-secure-api-key \
  nvcr.io/nvidia/nim/meta/llama-3.1-70b-instruct:latest

# Client request with API key
curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Authorization: Bearer your-secure-api-key" \
  -H "Content-Type: application/json" \
  -d '{"model": "...", "messages": [...]}'

OAuth 2.0 Integration:

• Integrate NIM with enterprise identity providers (Okta, Azure AD)
• Role-based access control (RBAC)
• Audit logs for compliance

Network Security:

• Deploy NIMs in private VPCs (no public internet access)
• Use API gateways with rate limiting and DDoS protection
• Enable TLS/SSL for all NIM endpoints

Data Privacy

1. On-Premises Deployment

• Deploy NIMs in private data centers (no cloud dependency)
• Data never leaves organizational boundary
• Use Case: Healthcare (HIPAA), finance (PCI-DSS), government

2. Encrypted Communication

# Generate TLS certificates
openssl req -x509 -nodes -days 365 -newkey rsa:2048 \
  -keyout /certs/nim-key.pem \
  -out /certs/nim-cert.pem

# Deploy NIM with TLS
docker run -d \
  --gpus all \
  -p 8443:8443 \
  -v /certs:/certs \
  -e NIM_SSL_CERT=/certs/nim-cert.pem \
  -e NIM_SSL_KEY=/certs/nim-key.pem \
  nvcr.io/nvidia/nim/meta/llama-3.1-70b-instruct:latest

3. Request Logging Controls

• Disable logging of user inputs (PII protection)
• Enable audit logs without content (compliance)
• Data retention policies (auto-delete after N days)

NIM Cost Optimization

GPU Selection Strategy

NCP-AAI Exam Focus: Match GPU to model size and throughput requirements.

Cost-Saving Techniques

1. Spot Instances / Preemptible VMs

• 60-90% cost savings vs. on-demand
• Use Case: Batch processing, non-critical agents
• Risk: Instances can be terminated (need graceful shutdown)

2. Model Sharing (Multi-Tenancy)

• Single NIM serves multiple agents/tenants
• Savings: 50-70% reduction in infrastructure cost
• Implementation: Namespace isolation, request routing by tenant ID

3. Auto-Scaling

# Kubernetes Horizontal Pod Autoscaler
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: llm-nim-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: llm-nim-pool
  minReplicas: 2  # Always have 2 NIMs running
  maxReplicas: 10  # Scale up to 10 during peak traffic
  metrics:
  - type: Resource
    resource:
      name: gpu
      target:
        type: Utilization
        averageUtilization: 70  # Scale up if GPU >70% utilized

4. Request Caching

• Cache LLM responses for identical queries
• Savings: 30-50% reduction in inference cost for repetitive queries
• Implementation: Redis cache with query hash as key

NCP-AAI Exam Preparation: NIM Focus Areas

High-Priority Topics (70% of NIM questions)

1. Deployment Patterns (30%)

• Single-agent vs. multi-agent architectures
• RAG pipeline with multiple NIMs
• Kubernetes deployment and scaling
• Docker vs. Kubernetes trade-offs

2. Optimization Techniques (25%)

• Quantization levels and use cases
• Batching strategies (static, dynamic, continuous)
• KV cache sizing and PagedAttention
• Multi-GPU deployment (tensor parallelism)

3. Monitoring and Troubleshooting (15%)

• Key latency metrics (TTFT, ITL, total latency)
• GPU utilization optimization
• Debugging OOM errors
• Performance bottleneck identification

4. NVIDIA Platform Integration (10%)

• NIM + NeMo integration
• TensorRT-LLM optimizations
• Triton Inference Server features

Sample Exam Questions (Practice)

Question 1: Your agentic AI system uses a 70B parameter LLM deployed via NIM on a single A100 80GB GPU. Users report high latency (5+ seconds) for responses. GPU utilization is only 40%. What optimization would MOST likely improve latency?

A) Enable FP8 quantization B) Increase NIM_MAX_BATCH_SIZE C) Enable tensor parallelism across 2 GPUs D) Reduce KV cache size

Correct Answer: B Explanation: Low GPU utilization (40%) indicates insufficient batching. Increasing batch size allows the GPU to process more requests concurrently, improving throughput and reducing per-request latency. FP8 (A) wouldn't help with low utilization. Tensor parallelism (C) is for models that don't fit on one GPU (70B fits on A100 80GB). Reducing KV cache (D) would hurt quality.

Question 2: You're deploying a multi-agent RAG system with embedding NIM, reranker NIM, and LLM NIM. Which NIM benefits MOST from batching optimization?

A) Embedding NIM B) Reranker NIM C) LLM NIM D) All benefit equally

Correct Answer: A Explanation: Embedding NIMs process many short texts (queries + documents) and benefit dramatically from batching (10-50x throughput improvement). Reranker (B) also benefits but to a lesser extent. LLM NIM (C) uses continuous batching (less impacted by batch size config). They don't benefit equally (D).

Question 3: Your organization requires on-premises LLM deployment with no data leaving the network. Which NIM deployment approach is MOST appropriate?

A) Use NVIDIA hosted NIM API endpoints B) Deploy NIM containers on local Kubernetes cluster C) Use cloud-based NIM with VPN tunnel D) Deploy NIM on edge devices

Correct Answer: B Explanation: On-premises requirement mandates local deployment. Kubernetes cluster (B) provides production-grade orchestration while keeping data in-network. Hosted API (A) sends data to NVIDIA cloud. VPN tunnel (C) still routes through cloud. Edge devices (D) lack the GPU resources for production LLMs.

Hands-On NIM Practice

Week-by-Week Learning Plan

Week 1: Basic NIM Deployment

• Deploy LLM NIM locally with Docker
• Test API with curl and Python client
• Monitor metrics endpoint
• Goal: Familiarity with NIM basics

Week 2: RAG Pipeline with Multiple NIMs

• Deploy embedding + reranker + LLM NIMs
• Build simple RAG agent
• Measure latency at each stage
• Goal: Multi-NIM orchestration

Week 3: Optimization and Scaling

• Experiment with quantization (FP8, INT8)
• Configure batching and KV cache
• Deploy on Kubernetes with auto-scaling
• Goal: Production optimization skills

Week 4: Monitoring and Troubleshooting

• Set up Prometheus + Grafana
• Simulate high traffic and debug bottlenecks
• Practice GPU utilization optimization
• Goal: Operational readiness

Recommended Resources

Official NVIDIA Resources:

• NVIDIA NIM Documentation (developer.nvidia.com)
• NVIDIA Deep Learning Institute: "Deploying AI with NIM" course
• NVIDIA Technical Blog: NIM performance optimization articles

Hands-On Labs:

• NVIDIA LaunchPad: Free NIM sandbox environments
• Google Colab: Deploy NIM with T4 GPU (free tier)
• AWS/Azure/GCP: Deploy production NIMs (paid)

Preporato's NCP-AAI Practice Tests: NIM Coverage

NIM-Specific Question Distribution

Domain 3: NVIDIA Platform Implementation

• 20+ questions on NIM deployment and configuration
• Optimization scenario questions (quantization, batching, multi-GPU)
• Troubleshooting and debugging scenarios

Domain 4: Deployment and Scaling

• 15+ questions on production deployment patterns
• Kubernetes and Docker best practices
• Auto-scaling and load balancing

Domain 5: Run, Monitor, and Maintain

• 10+ questions on NIM monitoring and observability
• Performance metrics and SLAs
• Incident response and debugging

What's Included

• 7 full-length practice exams with detailed NIM scenarios
• Architecture diagrams for complex multi-NIM deployments
• Performance calculations (batch size, KV cache sizing, GPU selection)
• Troubleshooting guides for common NIM issues
• Up-to-date content reflecting latest NIM features (Dec 2025)

Why Preporato for NIM Prep?

1. Hands-On Scenarios: Real-world deployment challenges, not just theory
2. Performance Math: Practice calculating optimal configurations
3. Architecture Decisions: Choose between deployment patterns with trade-off analysis
4. Debugging Practice: Identify and resolve performance bottlenecks
5. Affordable: $49 for complete NIM exam preparation

Master NVIDIA NIM for NCP-AAI: Start practicing with Preporato at Preporato.com

Key Takeaways

1. NIM is 15-20% of exam - critical for passing, especially Domain 3
2. Know deployment patterns: Single-agent, RAG pipeline, multi-agent swarm
3. Optimization hierarchy: Quantization → Batching → KV Cache → Multi-GPU
4. Metrics mastery: TTFT, ITL, throughput, GPU utilization
5. Platform integration: NIM + NeMo + TensorRT-LLM + Triton
6. Hands-on practice: Deploy at least 3 different NIM configurations
7. Cost optimization: GPU selection, auto-scaling, caching, multi-tenancy
8. Security: On-prem deployment, API auth, TLS encryption

Ready to master NVIDIA NIM for your NCP-AAI certification? Combine hands-on practice with Preporato's expert-crafted exam scenarios!