Deploying Custom Fine-Tuned Models on NexusAI Edge

Introduction

Deploying fine-tuned language models to the edge reduces latency, improves data privacy, and scales inference costs predictably. This guide walks you through packaging, optimizing, and deploying your custom model to the NexusAI Edge infrastructure using the official CLI and manifest system.

Prerequisites

• NexusAI CLI v3.2.0+ installed globally
• A fine-tuned model in .safetensors or .gguf format
• Docker Desktop or equivalent runtime (v24.0+)
• Valid NexusAI workspace token with edge.deploy permissions

Step 1: Quantize & Optimize the Model

Edge devices have constrained VRAM. We recommend 4-bit or 8-bit quantization before deployment. Use the built-in optimization pipeline:

nexus optimize \
  --input ./models/finetuned-lora-v2 \
  --format gguf \
  --quantization Q4_K_M \
  --output ./optimized-edge-model \
  --parallel 4

This produces a production-ready model directory containing model.gguf, tokenizer files, and a config.json metadata file.

Step 2: Containerize with Edge Runtime

NexusAI Edge expects standardized OCI-compliant images. Use the provided base image to ensure compatibility with our inference scheduler.

FROM nexusai/edge-runtime:3.2-cuda12

# Set inference workspace
ENV NEXUS_WORKSPACE=/app/models
WORKDIR /app

# Copy quantized model
COPY ./optimized-edge-model /app/models/

# Expose gRPC & HTTP endpoints
EXPOSE 8080 50051

# Run optimized inference server
CMD ["nexus-serve", "--port", "8080", "--grpc-port", "50051"]

Build and tag the image:

docker build -t my-finetuned-edge:v1 .

Step 3: Configure the Deployment Manifest

Create a nexus.yaml manifest to define scaling policies, hardware requirements, and routing rules:

# nexus.yaml
apiVersion: nexusai/v1
kind: EdgeDeployment
metadata:
  name: finetuned-prod-v1
  region: us-east-1
spec:
  image: my-finetuned-edge:v1
  hardware:
    gpu: A10G
    memory: 16Gi
  scaling:
    minReplicas: 1
    maxReplicas: 8
    metric: concurrent_requests
    threshold: 45
  routing:
    latencyBudget: 120ms
    failover: nexus-fallback-lm:default

Step 4: Deploy & Monitor

Push the container and apply the manifest in one command:

nexus deploy \
  --manifest nexus.yaml \
  --push true \
  --env production

Once deployed, verify the endpoint:

curl https://edge.nexusai.run/finetuned-prod-v1/v1/chat/completions \
  -H "Authorization: Bearer $NEXUS_API_KEY" \
  -d '{
    "model": "finetuned-prod-v1",
    "messages": [{"role": "user", "content": "Summarize this log entry."}],
    "max_tokens": 256
  }'

Monitor latency, GPU utilization, and cache hit rates in the NexusAI Dashboard under Edge > Deployments.

Best Practices & Troubleshooting

Optimizing Cold Starts

Edge instances spin down after inactivity. Keep a minimum replica count of 1 or enable Always-On in the manifest to eliminate cold-start latency for latency-sensitive applications.

Memory & OOM Errors

If you encounter CUDA_OUT_OF_MEMORY, reduce the max_batch_size in your config.json or switch to Q4_K_S quantization. Monitor VRAM usage via:

nexus edge metrics --deployment finetuned-prod-v1 --metric vram_utilization

Tokenization Mismatches

Always deploy the exact tokenizer used during fine-tuning. NexusAI Edge validates tokenizer vocabulary on startup. Mismatches will fail deployment and log a TOKENIZER_VOCAB_MISMATCH error.

Conclusion

You now have a production-ready, fine-tuned model running on NexusAI Edge with auto-scaling, failover routing, and real-time observability. For advanced features like multi-region edge replication, model A/B testing, or custom CUDA kernels, explore the Advanced Edge API.