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L40S on Lambda: Pricing, Availability & Setup

Deploybase · April 16, 2025 · GPU Pricing

L40S on Lambda: Pricing, Availability & Setup

Lambda Labs does not currently list L40S GPUs in its public infrastructure offerings. As of March 2026, Lambda focuses on professional-grade hardware like A100 and A6000, skipping consumer-oriented professional cards like L40S. Teams seeking professional GPU alternatives should evaluate Lambda's A100 instances at approximately $1.48 per hour or explore alternative providers offering L40S infrastructure. This guide covers Lambda's professional GPU portfolio and helps teams select appropriate alternatives for large-scale inference and training workloads.

Lambda's Professional GPU Strategy

Lambda Labs maintains a curated GPU selection focused on professional-class infrastructure. Current offerings emphasize A100, A6000, and RTX 6000 units rather than newer data center-optimized options like L40S.

Lambda's GPU selection reflects focus on established professional hardware with proven deployment histories. Newer hardware like L40S remains unavailable as Lambda prioritizes stability and support maturity over latest availability.

Lambda's pricing philosophy emphasizes consistency and reliability over per-hour cost optimization. Teams requiring professional support and SLA commitments benefit from Lambda's managed approach despite higher costs.

The company's infrastructure roadmap continues evaluating new GPU options as manufacturing capacity permits. Monitoring Lambda's announcements reveals emerging GPU availability.

Lambda's A100 as L40S Alternative

Lambda's A100 GPUs provide professional-class infrastructure suitable for large model inference and training. A100 40GB configurations priced at approximately $1.48 per hour offer substantially more compute capability than L40S despite similar memory capacity.

A100 tensor performance reaches approximately 312 TFLOPS TF32, comparable to L40S's 362 TFLOPS TF32. Both GPUs provide similar throughput for most inference workloads despite architectural differences.

Memory capacity on Lambda's A100 matches L40S at approximately 40-80GB depending on configuration. A100's memory capacity supports models identical in size to L40S deployments.

A100's memory bandwidth reaches 2 TB/s compared to L40S's 864 GB/s. This bandwidth advantage benefits memory-intensive operations and large batch processing despite higher per-hour costs.

Lambda's A100 pricing at $1.48 per hour represents 87% premium over L40S on RunPod at $0.79/hr. This pricing premium reflects professional infrastructure, managed operations, and SLA commitments. The yearly cost difference becomes substantial: $650 extra per month per GPU means $7,800 annually.

For what do developers get that premium? Lambda guarantees 99.5% uptime through SLAs. Lambda provides direct technical support from their team rather than community forums. Lambda handles all infrastructure patching, kernel updates, and driver maintenance. These operational benefits matter more to some teams than others.

Lambda's Complete Professional GPU Portfolio

A6000 instances on Lambda provide professional graphics GPUs with 48GB memory. A6000 pricing remains lower than A100, typically around $1.10 per hour.

RTX 6000 instances support professional graphics workflows alongside deep learning applications. Dual-purpose workloads combining rendering and inference benefit from RTX 6000's feature set.

H100 instances provide latest training performance for teams requiring fastest possible model training. H100 availability on Lambda remains limited, with pricing significantly higher than A100.

Availability and Regional Distribution

Lambda maintains A100 instance availability across primary US regions including us-west and us-east zones. Availability varies by region and fluctuates based on customer demand and infrastructure capacity.

GPU availability on Lambda's public website reflects real-time inventory. Teams planning A100 deployments should verify current availability and account for potential waiting periods.

Lambda provides no automatic scaling or spot pricing mechanisms. Pricing remains fixed regardless of demand, offering cost predictability at the expense of dynamic pricing optimization.

Persistent compute sessions on Lambda maintain instance state across multiple connection sessions. Users can disconnect and reconnect without losing running processes or loaded models.

Performance Characteristics for A100

Inference throughput on Lambda A100 instances rivals or exceeds L40S performance for most model architectures. Token generation rates for quantized 70B parameter models reach 15-40 tokens per second.

Long-context document processing with extended sequence lengths performs efficiently on A100. Superior memory bandwidth compared to L40S benefits processing 4K-8K token contexts.

Batch inference on A100 accommodates 16-48 concurrent requests depending on model size. Enhanced memory capacity and bandwidth support larger batch sizes than L40S.

Training workloads on A100 perform significantly better than L40S due to architectural optimization differences. Teams prioritizing training performance should select A100 despite similar inference throughput.

Cost Analysis and Economic Justification

Monthly costs for sustained Lambda A100 usage run approximately $1,066 for 720 hours. Reserved instance pricing reduces costs somewhat, though no aggressive discount mechanisms exist on Lambda.

Lambda A100 pricing at $1.48 per hour costs 87% more than RunPod L40S at $0.79. This cost premium reflects professional support, SLA commitments, and managed infrastructure.

Cost justification for Lambda A100 requires specific operational benefits. Teams valuing support services and reliability guarantees benefit from premium pricing.

Cost-conscious applications without professional support requirements should evaluate RunPod L40S or Vast.AI alternatives.

Deployment on Lambda Infrastructure

Lambda Cloud provides SSH access to GPU instances through standard terminal connections or web-based interfaces. Containerization through Docker integrates with Lambda's infrastructure.

Pre-configured PyTorch and TensorFlow environments accelerate model loading without requiring dependency installation. Deep learning framework versions remain updated through Lambda's managed environment.

JupyterLab integration enables interactive model development directly on Lambda instances. This capability suits exploration workflows before production deployment.

Lambda's unified pricing includes bandwidth for data transfer to instances. Models and datasets transfer without metering, reducing operational costs versus providers charging per-gigabyte egress fees.

Integration with Lambda's Ecosystem

Lambda's cloud storage integration simplifies accessing models and datasets from instances. Built-in S3-compatible interfaces reduce network transfer latency for model loading.

API gateway services on Lambda enable deploying inference endpoints without external infrastructure. Managed load balancing distributes requests across multiple GPU instances automatically.

SSH key management and VPC networking provide security controls appropriate for professional deployments. Network isolation and encryption options exceed marketplace provider offerings.

Integration with development tools including Git, MLflow, and Weights & Biases enables professional workflow compatibility. Pre-configured integrations reduce setup time for complex ML operations.

When to Choose Lambda A100 vs Alternatives

Teams already deployed on Lambda should evaluate A100 for integration benefits. Avoiding provider switching reduces operational complexity despite higher per-hour costs. If developers're already managing infrastructure on Lambda, staying with Lambda for additional GPU capacity reduces context switching.

Production inference services with uptime requirements exceeding 99% benefit from Lambda's managed infrastructure and SLA commitments. Mission-critical deployments justify Lambda's premium pricing. If the customers expect 99.95% availability, developers can't bet on marketplace infrastructure.

Teams requiring HIPAA, SOC2, or similar compliance certifications find Lambda's compliance infrastructure valuable. Regulated workloads may require Lambda despite higher costs. Financial services, healthcare providers, and government contractors often need certified infrastructure.

Professional support becomes valuable for teams lacking internal GPU expertise. Cost premiums partially reflect support service value. If the team has no experience managing GPU infrastructure, paying for professional support reduces risk.

Teams planning long-term GPU infrastructure benefit from reserved capacity on Lambda. Committing to 12-month reservations yields meaningful discounts, reducing the cost gap versus market alternatives.

When Not to Choose Lambda

Cost-conscious teams without AWS integration requirements should deploy L40S on RunPod instead. Cost differential of $0.69 per hour becomes substantial for extended inference workloads. For a single-GPU deployment running 24/7 for a month, that's $500 extra.

Proof-of-concept and experimental deployments should prioritize lowest-cost options. Lambda's infrastructure overhead adds little value during validation phases. Save Lambda for post-validation production workloads.

Teams comfortable managing cloud infrastructure across multiple providers should evaluate all options. Multi-cloud cost optimization often outweighs single-cloud convenience. Smart teams compare options quarterly.

Teams with spare GPU capacity elsewhere should not duplicate infrastructure on Lambda. Internal GPU infrastructure deployment costs less than cloud GPU purchasing. If developers own H100s in the own data center, use them.

Research teams exploring models and architectures benefit more from RunPod's hardware diversity. Lambda's limited selection restricts experimentation options. Vast.ai's peer marketplace offers even more hardware variety at lower costs.

Comparing L40S Availability Across Providers

L40S on RunPod at $0.79 per hour offers consistent availability and aggressive pricing. RunPod's dedicated infrastructure ensures reliable L40S access.

L40S on Vast.ai through peer marketplace typically ranges $0.60-0.90 per hour. Marketplace pricing offers 20-35% savings versus RunPod despite availability variability.

CoreWeave professional infrastructure provides L40S alternatives with SLA commitments and professional support. production deployments requiring reliability guarantees benefit from CoreWeave.

Exploring Lambda's Training Capabilities

Lambda A100 instances optimize for training workloads despite similar inference throughput to L40S. Teams requiring both training and inference benefit from single-platform infrastructure.

Distributed training across multiple A100 instances uses fast inter-GPU communication. Training frameworks achieve near-linear scaling across Lambda's infrastructure.

Training cost optimization through spot instance discounts and reserved capacity planning reduces operational expenses for sustained training workloads.

Final Thoughts

Lambda Labs does not currently offer L40S GPUs, instead providing professional A100 alternatives at $1.48 per hour. As of March 2026, no indication exists that Lambda will add L40S to their portfolio. Teams seeking L40S capability should deploy on RunPod, Vast.AI, or similar providers offering L40S at lower costs.

Lambda's A100 at $1.48 per hour provides professional-class infrastructure with SLA commitments and managed support. Premium pricing reflects production reliability rather than raw performance advantages. For the right team and workload, that premium is well-spent money.

Cost-conscious teams should prioritize RunPod L40S at $0.79 per hour. Pricing differentials of 87%+ justify exploring alternative providers for inference-focused workloads. The break-even point between Lambda and RunPod occurs when developers need the professional support and SLAs.

Teams requiring production SLA commitments and professional support should view Lambda's premium pricing as justified operational expense. Development and cost-sensitive applications benefit from marketplace providers offering L40S at substantially lower hourly rates.

The Lambda vs RunPod decision ultimately depends on the organization's tolerance for managing infrastructure yourself. Lambda Labs bundles operational support into the pricing. RunPod pushes more operational responsibility to the team. Choose based on the team's expertise and available engineering resources.

Contents

FAQ

Q: Will Lambda Labs ever offer L40S GPUs? A: No information suggests Lambda will add consumer-professional GPUs like L40S. Lambda's strategy emphasizes proven professional hardware. L40S remains an unconfirmed addition to their portfolio.

Q: How does Lambda A100 compare in performance to L40S? A: A100 provides better memory bandwidth for training (2 TB/s vs L40S's 864 GB/s). TF32 tensor performance is similar (A100: 312 TFLOPS vs L40S: 362 TFLOPS). For inference, the difference is marginal. Memory bandwidth favors A100, benefiting batch inference.

Q: What's the real cost difference between Lambda and RunPod over a year? A: Using A100 24/7 on Lambda costs $12,960 annually. RunPod A100 costs approximately $9,360 annually. The difference is $3,600 per GPU per year. For a team using 5 GPUs, that's $18,000 annually.

Q: Does Lambda offer SLA guarantees? A: Yes. Lambda provides 99.5% uptime SLAs on A100 instances. RunPod does not publish uptime guarantees. This SLA difference justifies pricing premiums for mission-critical workloads.

Sources

  • Lambda Labs pricing and product documentation (March 2026)
  • NVIDIA L40S and A100 specifications
  • DeployBase GPU pricing tracking (March 2026)
  • GPU infrastructure provider comparison data