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L4 on Google Cloud: Pricing, Specs & How to Rent

Deploybase · April 4, 2025 · GPU Pricing

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L4 GPU on Google Cloud: Availability & Pricing

Google Cloud offers L4 GPUs through Compute Engine instances in multiple regions as of March 2026. The L4 represents the most cost-effective inference GPU on the platform, designed specifically for batch processing and model serving workloads.

L4 availability spans US, EU, and Asia-Pacific regions. Standard on-demand instances start at approximately $0.71 per hour for GPU allocation (including vCPU and memory), with additional charges for storage.

The NVIDIA L4 GPU delivers 24 GB of GDDR6 memory, making it suitable for serving production models up to 8-13 billion parameters efficiently.

L4 Technical Specifications

The L4 GPU contains 7,424 NVIDIA CUDA cores organized across 58 Streaming Multiprocessors. Peak theoretical performance reaches 120 TFLOPS in TF32 precision (with sparsity), 242 TFLOPS in FP16 Tensor Core (with sparsity), and 30.3 TFLOPS in FP32 standard floating-point operations.

Memory capacity stands at 24 GB of GDDR6 with 300 GB/sec bandwidth. While lower bandwidth than HBM-equipped GPUs, GDDR6 proves adequate for inference where models remain constant and only input tokens change.

The L4 features fourth-generation RT Cores specialized for ray-tracing workloads, though most ML inference doesn't utilize ray-tracing capabilities.

Tensor Cores provide matrix acceleration across multiple precision levels including int8 for quantized inference, FP16 for mixed precision, and TF32 for efficient training at lower precision.

Thermal design power reaches only 72W, one of the lowest among modern GPUs. This low power consumption enables dense deployment without requiring specialized cooling infrastructure.

The GPU supports PCIe Gen4 with 64 GB/sec bandwidth sufficient for inference throughput demands.

L4 on Google Cloud: Pricing Breakdown

Standard L4 compute instance pricing (on-demand, including GPU accelerator):

  • L4 GPU (1x 24GB): ~$0.71/hour
  • Additional charges for persistent disk storage apply separately
  • Total (with typical storage): $0.73/hour ($17.52/day, ~$526/month)

Spot instances reduce L4 pricing significantly. Standard on-demand pricing varies by region; the us-central1 region is typically among the lowest-cost options.

Commitment discounts apply automatically when selecting 1-year or 3-year terms:

  • 1-year commitment: 25% discount vs on-demand
  • 3-year commitment: ~52% discount vs on-demand

For inference workloads running continuously, annual commitment delivers substantial savings compared to on-demand pricing.

L4 vs. Larger GPUs: When to Choose L4

L4 excels at inference serving for models up to 13 billion parameters. Larger models like Llama 2 70B or Mistral 8x7B require multiple L4s or larger single-GPU solutions.

Cost-per-inference metric favors L4 for many workloads. GCP A100 (40GB) at ~$3.67/hour delivers higher throughput but at a much higher cost, making L4 better value for inference on smaller models.

Batch inference processing benefits from L4's low latency. Unlike training which maximizes batch size for throughput, inference serving optimizes for per-token latency where L4's capabilities exceed optimization needs.

Real-time serving applications demand response time under 100 milliseconds. L4 delivers sub-50ms latency for most open-source models, sufficient for production applications.

How to Rent L4 on Google Cloud

Creating a Google Cloud Project:

  • handle to Google Cloud Console
  • Create new project or use existing project
  • Enable Compute Engine API
  • Set up billing account

Launching L4 Instance:

  • Go to Compute Engine > Instances > Create Instance
  • Configure instance name and region
  • Select Machine type: Custom
  • Set vCPU (2-8 recommended for inference serving)
  • Set memory (8 GB minimum, 16 GB recommended)
  • Expand GPU section
  • Select "NVIDIA L4" from dropdown
  • Set GPU count (1-2 for most cases)
  • Select boot disk: Ubuntu 22.04 LTS
  • Set disk size (100 GB minimum)
  • Click Create

Configuring L4 Instance:

  • SSH into instance after provisioning
  • Run: sudo apt update && sudo apt upgrade
  • Install NVIDIA drivers: curl -fsSL https://developer.download.nvidia.com/ | sudo bash
  • Install CUDA toolkit: sudo apt install nvidia-cuda-toolkit
  • Verify installation: nvidia-smi

Running Inference:

  • Install vLLM: pip install vllm
  • Launch model server: python -m vllm.entrypoints.openai.api_server --model=meta-llama/Llama-2-7b-hf
  • Create load balancer pointing to instance
  • Scale instances based on traffic with instance groups

L4 Performance for Common Inference Workloads

Text generation using Llama 2 7B achieves 280 tokens/second throughput with batch size 16, enabling continuous inference for 10+ concurrent users per instance.

Embedding generation for semantic search completes at 15,000 vectors/second with 384-dimensional embeddings.

Image classification with ResNet152 achieves 450 images/second throughput, suitable for batch processing large image datasets.

Speech recognition with Whisper large model processes audio at 8x real-time speed, handling 8 minutes of audio per minute of processing.

Multi-model inference serving handles simultaneous workloads. Running Llama 7B alongside BLIP image-caption model consumes 18 GB memory total with minimal contention.

L4 Integration with Google Cloud Services

Google Cloud LLM API provides managed inference without instance management. For cost-sensitive production workloads, direct L4 deployment on Compute Engine delivers 80-90% lower costs than managed APIs.

Cloud Storage integration enables streaming training data directly to inference containers. Standard object storage API reduces latency compared to external data sources.

Cloud Load Balancing distributes traffic across multiple L4 instances automatically. Health checks ensure failed instances receive no traffic.

Cloud Monitoring captures GPU metrics: utilization, memory usage, temperature. Automated alerts trigger when metrics exceed thresholds.

Cloud SQL integration enables model input/output logging for audit trails. Direct database connections from inference instances support operational needs.

Scaling L4 Deployment for Production

Single L4 instances serve 20-50 concurrent users depending on model size and response latency targets.

Two L4 instances in separate zones provide high availability with automatic failover through Cloud Load Balancing.

Instance groups with autoscaling automatically add/remove L4 instances based on CPU utilization and custom metrics. Scaling policies can base decisions on request queue depth.

Regional deployments span multiple zones within a region. Global load balancing distributes traffic across regions for geographic redundancy.

Cache layers with Cloud Memorystore reduce inference load for repeated queries. Common prompts cache embedding results for near-instant retrieval.

When L4 Makes Economic Sense

Batch processing of datasets completes cost-effectively on L4. Processing 1 million inference requests costs approximately $2-3 when running continuously on single L4.

24/7 inference serving becomes economical on L4 compared to larger GPUs. Monthly cost around $526 (on-demand) handles moderate traffic patterns efficiently, with significant discounts available through committed use.

Model serving for multiple smaller models justifies L4 allocation. MPS (Multi-Process Service) enables running 3-4 distinct inference models simultaneously without performance degradation.

Development and testing environments benefit from L4's low cost. DevOps teams rapidly provision instances for experimentation without budget constraints.

FAQ

Q: Can L4 run large language models like Llama 2 70B?

Llama 2 70B requires 140 GB memory for full precision. L4's 24 GB cannot accommodate this. Using int4 or int8 quantization requires 35-70 GB memory, still exceeding single L4 capacity. Multiple L4s or larger GPUs are necessary.

Q: What's the throughput for Llama 2 13B on L4?

Llama 2 13B generates approximately 80 tokens/second on L4 with batch size 4, or 180 tokens/second with batch size 16.

Q: Does Google Cloud offer committed pricing for L4?

Yes, 1-year and 3-year commitments provide 25-52% discounts. Commitment periods match reservation duration.

Q: How many L4 GPUs can single instance use?

Google Cloud supports up to 2 L4 GPUs per standard instance, 4 for specially configured instances. MIG partitioning enables further subdivision.

Q: Is L4 suitable for video processing?

L4 handles video encoding and transcoding efficiently. Typical throughput reaches 150 frames/second for 1080p H.264 encoding, suitable for live streaming backends.

Q: Can L4 handle fine-tuning of smaller models?

Yes, L4 fine-tunes models up to 7-13 billion parameters. Larger models require A100 or H100 due to memory constraints.

L4 Specs Guide - Technical specifications reference

GPU Pricing Guide - Compare all providers

LLM API Pricing - Managed inference alternative

Inference Optimization - Deployment best practices

RunPod GPU Pricing - Alternative L4 provider

Sources

  • Google Cloud Compute Engine GPU Documentation
  • NVIDIA L4 GPU Technical Specifications
  • Google Cloud Pricing Calculator
  • vLLM Model Server Documentation
  • NVIDIA CUDA and NVML Documentation