Hardware & Capacity

Choosing the right hardware impacts cost, latency, and throughput. This guide covers GPU selection, memory planning, and capacity estimation.

GPU Selection Guide

SIE supports NVIDIA GPUs via CUDA. Choose based on your model size and throughput requirements.

Recommended GPUs

GPU	VRAM	Best For	GCP Machine Type
NVIDIA L4	24 GB	Most embedding models, cost-effective inference	g2-standard-8 (1x), g2-standard-24 (2x)
NVIDIA A100 40GB	40 GB	Large models, high throughput	a2-highgpu-1g
NVIDIA A100 80GB	80 GB	Very large models (7B+), multi-model serving	a2-ultragpu-1g
NVIDIA H100	80 GB	Highest throughput, latest generation	a3-highgpu-1g

Budget Options

GPU	VRAM	Best For	GCP Machine Type
NVIDIA T4	16 GB	Small models, development, testing	n1-standard-8 + T4

L4 is recommended for most production workloads. It offers the best price-performance ratio for embedding models under 4B parameters.

Memory Planning

GPU memory usage depends on model size, batch size, and sequence length.

Model Size Categories

Category	Parameters	Approximate VRAM	Example Models
Small	< 100M	0.5-1 GB	all-MiniLM-L6-v2
Medium	100M-500M	1-3 GB	bge-m3, e5-large-v2, multilingual-e5-large
Large	500M-2B	3-8 GB	gte-Qwen2-1.5B-instruct, stella_en_1.5B_v5
XLarge	2B-8B	8-20 GB	Qwen3-Embedding-4B, e5-mistral-7b-instruct, NV-Embed-v2

Batch Memory Overhead

Beyond model weights, inference requires memory for:

• Activations: Proportional to batch size and sequence length
• KV cache: For transformer attention (significant for long sequences)
• CUDA context: ~500MB-1GB fixed overhead per GPU

Rule of thumb: Reserve 2-3x the model weight size for safe operation with batching.

Multi-Model Serving

SIE loads models on-demand and uses LRU eviction when memory pressure exceeds 95%:

# From memory.py - default eviction threshold
pressure_threshold: float = 0.95  # Evict LRU model above 95%

For multi-model deployments, provision VRAM for:

• Your largest model (always loaded)
• 1-2 additional frequently-used models
• Headroom for batch processing

Example: Serving bge-m3 (~2GB) and e5-mistral-7b (~15GB) together requires at least 24GB VRAM.

Capacity Planning

Throughput varies by model architecture, sequence length, and hardware. Use these estimates as starting points.

Throughput by Model Type (L4 GPU)

The numbers below are rough planning estimates, not measured guarantees. Validate with your own workload before capacity planning:

Model Type	Example	Corpus Throughput	Query Throughput
Small encoder	all-MiniLM-L6-v2	~50,000 tokens/sec	~5,000 tokens/sec
Medium encoder	bge-m3	~30,000 tokens/sec	~3,000 tokens/sec
Large LLM-based	Qwen3-Embedding-4B	~5,000 tokens/sec	~700 tokens/sec
XLarge LLM-based	e5-mistral-7b	~3,000 tokens/sec	~400 tokens/sec

Corpus vs Query: Corpus encoding uses longer sequences (documents). Query encoding uses shorter sequences (search queries).

Scaling Estimates

For horizontal scaling, estimate required replicas:

replicas = (target_throughput / single_gpu_throughput) * safety_factor

Use a safety factor of 1.3-1.5 to account for traffic spikes and variance.

Example: To achieve 100,000 tokens/sec with bge-m3:

• Single L4 throughput: ~30,000 tokens/sec
• Replicas needed: (100,000 / 30,000) * 1.4 = 4-5 replicas

Cost Optimization

Spot/Preemptible Instances

The Helm chart’s machine profiles support spot instances for GPU worker pools:

# From deploy/helm/sie-cluster/values.yaml - machineProfiles
l4-spot:
  gpuType: nvidia-l4
  machineType: g2-standard-8
  spot: true

Recommended for:

• Batch processing workloads
• Non-latency-critical embedding jobs
• Development and testing

Not recommended for:

• Low-latency serving with strict SLAs
• Single-replica deployments

Scale-to-Zero

For variable traffic, enable KEDA autoscaling with autoscaling.enabled=true; the chart creates a KEDA ScaledObject per worker lane with minReplicas: 0. Stock Kubernetes HPA cannot scale to zero. Combine with:

• GKE Autopilot for automatic node provisioning
• Preemptible node pools for cost savings during scale-up

Cold start latency: Model loading adds 10-120 seconds depending on model size, and up to several minutes for large models. Consider keeping at least one warm replica for latency-sensitive workloads.

Right-Sizing Checklist

1. Start with L4 - Upgrade to A100 only if models exceed 24GB VRAM
2. Use spot instances - Enable for batch workloads and non-critical paths
3. Measure actual throughput - Run performance evals before capacity planning
4. Monitor memory pressure - High eviction rates indicate undersized VRAM

GCP GPU Quotas

Before deploying, request sufficient GPU quota in your target region.

Checking Quotas

# List GPU quotas in a region
gcloud compute regions describe us-central1 \
  --format="table(quotas.filter(metric ~ GPU))"

Common Quota Types

Quota Name	GPU Type	Notes
NVIDIA_L4_GPUS	L4	Most available, recommended
NVIDIA_A100_GPUS	A100 40GB	Limited availability
NVIDIA_A100_80GB_GPUS	A100 80GB	Very limited
NVIDIA_H100_GPUS	H100	Newest, limited availability
NVIDIA_T4_GPUS	T4	Widely available

Requesting Quota Increases

1. Go to IAM & Admin > Quotas
2. Filter by service “Compute Engine API” and metric containing “GPU”
3. Select the quota and click “Edit Quotas”
4. Provide justification and submit

Tip: Request quota in multiple regions. GPU availability varies significantly by zone.

Zone Availability

GPU availability varies by zone. Check before provisioning:

# List zones with L4 GPUs
gcloud compute accelerator-types list --filter="name=nvidia-l4"

What’s Next

• Request Lifecycle - How SIE processes requests through batching and inference
• CLI Reference - Server configuration options including device selection