I need GPU inference with autoscaling and no stranded capacity

For GPU inference with autoscaling and minimal/no stranded capacity, the main challenge is that GPUs are expensive, slow to scale, and usually allocated in fixed chunks. The best solution depends on whether you need real-time latency, batch throughput, or burst handling.

Best practical pattern

1) Use request-based autoscaling for inference pods

Scale on:

• concurrent requests
• queue depth
• GPU utilization
• latency / p95
• in-flight tokens for LLMs

This works better than CPU-based scaling for GPU workloads.

2) Put a queue in front

Instead of keeping lots of idle GPUs, route traffic through:

• Kafka / SQS / RabbitMQ / Redis queue
• a lightweight API layer
• workers that scale from zero or near-zero

This lets you absorb bursts without overprovisioning.

3) Use bin packing on shared GPU nodes

To reduce stranded capacity:

• run multiple inference replicas per GPU node when possible
• use smaller GPU slices if your framework supports it
• pack models with similar memory profiles together
• avoid dedicating whole nodes unless the model requires it

4) Use node autoscaling with GPU-aware scheduling

Have the cluster scale GPU nodes only when pods are pending.
Typical setup:

• Kubernetes + Cluster Autoscaler or Karpenter
• GPU node pools with tight instance types
• pod requests/limits set correctly
• topology-aware scheduling if needed

5) For LLMs, use an inference server that supports continuous batching

Examples:

• vLLM
• TGI
• TensorRT-LLM
• Triton

This improves GPU utilization and reduces the number of idle replicas required.

---

How to minimize stranded capacity

Good options

• Scale-to-zero for non-latency-sensitive workloads
• Multiple models per GPU only if memory allows
• MIG on NVIDIA GPUs when your workload can be partitioned
• Spot instances for overflow or non-critical traffic
• Right-size GPU instance types to match model memory needs

Things that cause stranded capacity

• one pod per GPU with low request volume
• over-requesting GPU memory or compute
• fixed minimum replica counts that are too high
• mixed model sizes on the same node
• long model warm-up times forcing large buffer capacity

---

Recommended architecture

If you want the simplest solid setup:

• Kubernetes
• GPU node autoscaling via Karpenter or Cluster Autoscaler
• vLLM/TGI/Triton for inference
• HPA/KEDA scaling on queue depth or request rate
• Redis/SQS/Kafka as a buffer
• scale-to-zero for non-urgent traffic
• spot/on-demand mix for cost control

---

Important caveat

If you need strict sub-second latency at all times, you cannot truly have zero stranded capacity, because you must keep some warm capacity ready.
In that case, the goal is:

• minimum warm baseline
• fast scale-out
• queueing for bursts
• high utilization on live GPUs

---

If you want, I can help with one of these next:

1. a reference architecture
2. a Kubernetes/Karpenter setup
3. a cost-optimized design for LLM inference
4. a decision matrix for managed services vs self-hosted

If you share your model type, traffic pattern, and cloud provider, I can recommend a concrete setup.