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L40S vs H100: Specs, Benchmarks & Cloud Pricing Compared

Deploybase · May 5, 2025 · GPU Comparison

Contents

L40S vs H100: Overview

L40s vs H100 is the focus of this guide. Different philosophies. H100: flagship training GPU. L40S: professional inference GPU.

Pricing gap: L40S $0.79/hr, H100 $1.99-2.69/hr. 2.5-3.4x cost. Worth it? Depends on workload.

This compares specs, benchmarks, pricing, decision frameworks. As of March 2026.

Hardware Specifications

NVIDIA L40S specifications:

GPU Memory: 48GB GDDR6 (memory bandwidth: 864 GB/s) Tensor Performance: 91.6 TFLOPS FP32, 366 TFLOPS TF32 (with sparsity: 733 TFLOPS), 733 TFLOPS FP16 (with sparsity: 1,466 TFLOPS FP8) NVIDIA Tensor Cores: 568 Architecture: Ada (NVIDIA's latest high-end architecture) Power consumption: 350W Memory Interface: 384-bit GDDR6 Cooling: Passive (requiring active case cooling)

NVIDIA H100 SXM specifications:

GPU Memory: 80GB HBM3 (memory bandwidth: 3,350 GB/s) Tensor Performance: 67 TFLOPS FP32, 3,958 TFLOPS FP8 (with sparsity), 1,979 TFLOPS BF16/FP16 (with sparsity) NVIDIA Tensor Cores: 528 Architecture: Hopper (NVIDIA's latest data center architecture) Power consumption: 700W (SXM variant) Memory Interface: 12-Hi HBM3 Cooling: Liquid-cooled (integrated)

Memory bandwidth represents the most significant architectural difference. H100's HBM3 memory provides 3.87x the memory bandwidth of L40S GDDR6. This difference affects performance on memory-bandwidth-intensive operations like attention mechanisms in large language models.

Tensor core density also differs significantly. H100 contains 5.9x more tensor cores optimized for matrix operations. This density benefits training workloads requiring massive parallel computation.

The L40S architecture emphasizes different priorities: media encoding/decoding capabilities, lower thermal requirements, and cost optimization for inference serving.

Training Performance Comparison

Training large language models stresses memory bandwidth and tensor throughput-precisely where H100 dominates.

Single-GPU training (7B parameter model fine-tuning):

H100 throughput: 350-400 tokens/second L40S throughput: 45-55 tokens/second H100 advantage: 7-8x faster training

The performance gap reflects both tensor core density (5.9x difference) and memory bandwidth advantages. L40S cannot sustain the data flow required for training models exceeding several billion parameters efficiently.

Multi-GPU distributed training (8x H100 vs 8x L40S):

H100 cluster: 2,400-2,800 tokens/second L40S cluster: 320-400 tokens/second H100 advantage: 6.5-7.5x faster

Distributed training amplifies the performance gap because H100's superior intra-GPU communication capabilities (through NVLink) compound when coordinating across multiple GPUs.

Training 13B parameter models:

H100: Full precision training achievable with reasonable memory constraints L40S: Requires quantization or reduced batch sizes; full training becomes impractical

For models approaching or exceeding H100's 80GB memory capacity, L40S's 48GB becomes a limiting factor. Mixed precision training (FP8) mitigates this constraint but introduces quality trade-offs.

Training 40B+ parameter models:

H100: Full training practical with pipeline parallelism L40S: Not practical; models require further quantization or data parallelism reducing efficiency

The specification gap makes L40S unsuitable for training models commonly deployed in 2026.

Inference Performance Comparison

Inference workloads (serving requests to end users or applications) show narrower performance gaps. Modern inference optimization techniques reduce reliance on the specifications most favoring H100.

Token generation performance (serving requests):

H100: 180-220 tokens/second per GPU L40S: 120-150 tokens/second per GPU H100 advantage: 1.3-1.5x faster

Inference performance gaps are significantly smaller than training gaps. This reflects quantization benefits (8-bit inference reduces memory bandwidth pressure) and inference-specific optimizations that don't rely on H100's tensor core density.

Model serving capacity (concurrent requests):

H100 with TensorRT-LLM optimization: 50-100 concurrent requests per GPU (varying by model size) L40S with TensorRT-LLM optimization: 35-60 concurrent requests per GPU

The difference is less dramatic than training performance gaps. Inference throughput per GPU is closer between architectures because batch inference patterns differ from training patterns.

Latency characteristics:

H100: 150-300ms end-to-end latency for first-token generation L40S: 180-350ms end-to-end latency

For real-time applications, H100 provides slightly better latency characteristics but L40S remains within acceptable bounds for most applications.

Multimodal inference (vision-language models):

H100: 8-12 images per second with 7B language model L40S: 5-7 images per second with 7B language model H100 advantage: 1.4-1.7x

Multimodal workloads combine vision processing with language generation, stressing both tensor throughput and memory bandwidth. The advantage narrows but remains measurable.

Cloud Pricing Analysis

Cloud provider pricing for L40S and H100 reflects raw hardware costs plus provider markup:

RunPod pricing:

  • L40S: $0.79/hour
  • H100 SXM: $2.69/hour
  • Cost multiplier: 3.4x

Lambda Labs pricing:

  • Lambda Labs does not currently list L40S in its catalog
  • H100 SXM: $3.78/hour

CoreWeave pricing:

  • L40S: $2.25/hour per GPU ($18/hr for 8x)
  • H100 SXM: $6.16/hour per GPU ($49.24/hr for 8x)
  • Cost multiplier: 2.7x

The pricing multiplier varies significantly by provider and GPU configuration.

Extended workload costs:

72-hour inference service with 10 concurrent requests:

L40S cluster (5x GPUs): $288 total cost H100 cluster (3x GPUs): $486 total cost Difference: H100 costs 69% more despite needing fewer GPUs

The economic calculation depends on whether serving L40S overhead (additional GPUs) is cheaper than serving H100 cost premium.

Training cost comparison:

Fine-tuning a 7B model (10 hours compute):

L40S with 8x GPUs: 10 hours * $0.79/hr * 8 = $63.20 H100 with 1x GPU: 1.2 hours * $2.69/hr * 1 = $3.23

H100 is 90% cheaper due to vastly superior training speed. L40S becomes uneconomical for training workloads.

For training workloads, H100's performance advantage completely dominates pricing considerations. The superior performance makes H100 cheaper despite higher hourly cost.

Inference Cost-Per-Token Analysis

Cost-per-token metrics reveal the actual expense of running inference at scale, accounting for both hardware cost and performance characteristics.

Token generation rates and costs:

L40S configuration:

  • Tokens per second: 120-150
  • Hourly cost: $0.79
  • Tokens per hour: 432,000 to 540,000
  • Cost per 1,000 tokens: $0.00146 to $0.00183
  • Cost per 1M tokens: $1.46 to $1.83

H100 configuration:

  • Tokens per second: 180-220
  • Hourly cost: $2.69 (RunPod pricing)
  • Tokens per hour: 648,000 to 792,000
  • Cost per 1,000 tokens: $0.00340 to $0.00415
  • Cost per 1M tokens: $3.40 to $4.15

The cost-per-token analysis reveals that L40S actually offers 1.85-2.85x better cost efficiency per token despite lower absolute throughput. This economic advantage is significant for high-volume inference deployments where token count dominates costs.

Cost analysis for billion-token monthly inference:

1 billion tokens per month:

  • L40S: Approximately $1,460 to $1,830 in GPU costs
  • H100: Approximately $3,400 to $4,150 in GPU costs
  • Monthly savings with L40S: $1,570 to $2,690

For teams processing hundreds of millions to billions of tokens monthly, token cost becomes the dominant economic factor. L40S's superior cost-per-token makes it the rational choice despite H100's higher throughput.

Cost-per-token at different request patterns:

Single-request serving (latency-critical):

  • H100 advantage narrows because individual request latency doesn't scale linearly with throughput
  • Both GPUs serve requests in 180-350ms range regardless of token count
  • L40S cost advantage remains significant: 2x cheaper per token

Batch inference serving (throughput-optimized):

  • H100's memory bandwidth shines with large batches
  • Batched inference reduces per-token H100 cost advantage
  • L40S maintains cost-per-token lead despite smaller batch sizes

Break-even scenarios:

If deploying for pure inference cost optimization, teams need L40S when:

  1. Monthly inference volume exceeds 500 million tokens
  2. Latency requirements permit 150-300ms response time
  3. Cost per token is a primary optimization metric

teams should deploy H100 instead when:

  1. Latency requirements demand sub-150ms response times
  2. Total inference volume is under 100 million tokens monthly
  3. Serving very large models (40B+ parameters) where L40S memory becomes limiting

Mixed Precision Performance Comparison

Modern inference techniques use reduced-precision arithmetic to improve performance without sacrificing quality significantly. Precision choices create distinct performance profiles between L40S and H100.

FP32 (full precision) performance:

L40S: 91.6 TFLOPS H100: 67 TFLOPS Performance ratio: 1.37x in favor of L40S

Note: FP32 performance is rarely the bottleneck in AI workloads. Tensor core precision (TF32, FP16, FP8) governs real-world throughput.

FP16 (half precision) performance:

L40S: 733 TFLOPS (with sparsity) H100: 1,979 TFLOPS (with sparsity) Performance ratio: 2.7x in favor of H100

Half precision is common for model weights and activations. H100's Hopper architecture includes specialized FP16 execution units providing substantial advantage. This precision level works well for inference of models fine-tuned for mixed precision.

INT8 (8-bit integer) performance:

L40S: Approximately 1,466 TFLOPS (FP8 via Ada tensor cores) H100: 3,958 TFLOPS (dedicated Hopper tensor cores, with sparsity) Performance ratio: ~2.7x in favor of H100

8-bit quantization is standard for production inference. Most models deployed in 2026 use INT8 or FP8 weights to balance accuracy and performance. H100's Hopper tensor cores provide substantial advantage for quantized workloads.

FP8 (8-bit floating point) performance:

L40S: 1,466 TFLOPS (with sparsity, via Ada tensor cores) H100: 3,958 TFLOPS (dedicated FP8 tensor cores, with sparsity) Practical advantage: H100 ~2.7x better

FP8 represents the frontier of ultra-low-precision inference. H100's native FP8 support through Transformer Engine enables FP8 inference without requiring offline quantization. L40S can perform FP8 through emulation on standard cores, losing most performance advantage.

Effective real-world inference performance across precision levels:

INT8 inference on 7B model:

L40S: 220-280 tokens/second (using INT8-optimized libraries) H100: 320-400 tokens/second Performance ratio: 1.4-1.8x (narrower than raw specifications)

The practical advantage narrows because memory bandwidth and communication overhead limit execution. Even H100's superior arithmetic can't overcome memory subsystem constraints entirely.

INT8 inference on 13B model:

L40S: 110-140 tokens/second H100: 200-240 tokens/second Performance ratio: 1.6-2.0x

Larger models stress memory bandwidth more, allowing H100's 3.87x bandwidth advantage to show. Precision reduction helps both GPUs.

FP8 inference on 7B model (H100 advantage):

H100 with Transformer Engine: 380-450 tokens/second L40S with emulated FP8: 180-220 tokens/second Performance ratio: 2.0-2.2x

FP8 native support gives H100 significant advantage for advanced inference deployments. Teams deploying FP8 models benefit substantially from H100's architecture.

Precision selection guidance:

INT8 is recommended for most inference deployments in 2026. Both L40S and H100 support INT8 well, with H100 maintaining 1.5-2.0x advantage. Cost-per-token favors L40S despite this performance gap.

FP16 inference works well on both GPUs for models not quantized aggressively. This precision level remains relevant for specialized models or quality-sensitive applications.

FP8 inference represents the frontier. H100's native support makes FP8 practical for H100 deployments, while L40S lacks optimizations. Teams deploying FP8 models should strongly consider H100.

Multi-GPU Scaling Analysis

Inference deployments rarely use single GPUs at production scale. Understanding how multiple L40S GPUs compare to multiple H100s reveals economic trade-offs.

Single GPU baseline:

L40S single: 120-150 tokens/second at $0.79/hour H100 single: 180-220 tokens/second at $2.69/hour

Two-GPU deployment:

2x L40S: 240-300 tokens/second at $1.58/hour 1x H100: 180-220 tokens/second at $2.69/hour

At two GPUs, L40S cluster already exceeds single H100 throughput while costing 41% less. For throughput-focused applications, 2x L40S is cost-optimal.

Four-GPU deployment:

4x L40S: 480-600 tokens/second at $3.16/hour 2x H100: 360-440 tokens/second at $5.38/hour L40S advantage: 1.3x more throughput, 41% cheaper

Scaling to four L40S GPUs maintains cost advantage. Infrastructure complexity increases (more GPUs to manage) but cost efficiency improves substantially.

Eight-GPU deployment:

8x L40S: 960-1,200 tokens/second at $6.32/hour 4x H100: 720-880 tokens/second at $10.76/hour L40S advantage: 1.3x more throughput, 41% cheaper

Multi-GPU scaling patterns hold consistently. L40S clusters match or exceed H100 throughput at significantly lower cost when scaled appropriately.

Cost-per-request analysis at different scales:

Assuming $0.10 cost per generated response (including infrastructure):

5 concurrent users (15-20 requests/second):

  • L40S (1 GPU): $0.07-0.10 per request
  • H100 (1 GPU): $0.12-0.18 per request

L40S handles 5 concurrent users with single GPU efficiently. H100 provides margin but costs more per request.

50 concurrent users (150-200 requests/second):

  • L40S (2 GPUs): $0.05-0.07 per request
  • H100 (1 GPU): $0.12-0.18 per request

At 50 concurrent users, single H100 becomes oversubscribed. 2x L40S handles capacity at 50% H100's cost.

500 concurrent users (1,500-2,000 requests/second):

  • L40S (4-6 GPUs): $0.04-0.05 per request
  • H100 (3-4 GPUs): $0.08-0.12 per request

Large-scale deployments show L40S's economic advantage most clearly. Cost scales linearly, and 6 L40S GPUs remain cheaper than 4 H100s while providing better throughput.

When multi-GPU L40S matches or exceeds single H100:

  • 1.5x L40S > 1.0x H100 in throughput
  • 2x L40S = 2.5x cheaper than 2x H100
  • 3x L40S = 3.5x cheaper than 2x H100
  • 4x L40S = 2.4x cheaper than 3x H100

The scaling relationship means teams deploying at any meaningful production scale (10+ concurrent requests) achieve better economics with L40S multi-GPU deployment.

Practical deployment recommendations:

Single-GPU deployments: Choose based on latency requirements. H100 if sub-150ms latency required; L40S acceptable otherwise.

2-4 GPU deployments: L40S becomes economically superior. Scale L40S instead of deploying H100.

8+ GPU deployments: L40S cost advantage becomes overwhelming. Infrastructure complexity (managing more devices) is the primary trade-off.

Power Efficiency Comparison

Electricity costs represent a significant operating expense for GPU infrastructure at scale. Power consumption directly affects data center economics.

Power consumption specifications:

L40S power consumption: 350W (specified at 350W) H100 SXM power consumption: 700W (standard) H100 PCIe power consumption: 350W (alternative variant)

H100 standard deployment consumes 2.0-2.2x more power than L40S. Even H100 PCIe variant (roughly equivalent to L40S power) lacks the inference optimizations of H100 SXM.

Power cost calculation per month:

Assuming $0.12 per kWh electricity cost:

L40S (350W):

  • Daily consumption: 8.4 kWh
  • Monthly consumption: 252 kWh
  • Monthly electricity cost: $30.24

H100 SXM (700W):

  • Daily consumption: 16.8 kWh
  • Monthly consumption: 504 kWh
  • Monthly electricity cost: $60.48

Single-GPU electricity cost difference: $30.24 per month

At scale, power differences compound:

Data center with 100 L40S GPUs:

  • Total power: 35 kW
  • Monthly electricity cost: $3,024
  • Annual electricity cost: $36,288

Equivalent throughput with H100 (60 GPUs approximately):

  • Total power: 42 kW
  • Monthly electricity cost: $4,032
  • Annual electricity cost: $48,384

Data center annual electricity savings with L40S: $12,096

Power consumption directly reduces L40S's cost advantage beyond hardware rental costs. When accounting for electricity in self-managed infrastructure, L40S advantage grows further.

Cooling cost implications:

L40S passive cooling requires active case cooling (ambient ventilation, typical in standard data center racks).

H100 SXM requires liquid cooling systems-specialized infrastructure increasing capital investment and operational complexity.

Liquid cooling systems add 5-15% to data center operational costs for GPU infrastructure. Teams operating H100 clusters incur specialized cooling infrastructure costs absent in L40S deployments.

Performance per watt analysis:

L40S:

  • 120-150 tokens/second per GPU
  • 350W power consumption
  • 0.34-0.43 tokens per watt-second
  • Annual energy cost per token: approximately $0.0000024 per token

H100 SXM:

  • 180-220 tokens/second per GPU
  • 700W power consumption
  • 0.26-0.31 tokens per watt-second
  • Annual energy cost per token: approximately $0.0000048 per token

L40S achieves 1.3-1.4x better performance per watt. The inference architecture provides efficiency gains despite lower absolute throughput.

Long-term cost analysis including power:

Six-month deployment (1,000 concurrent users estimated):

L40S cluster (6 GPUs):

  • Hardware rental: $2,268 (6 GPUs * $0.79/hr * 730 hours)
  • Electricity: $181.44 (if self-managed; $0 if cloud-hosted)
  • Total: $2,449.44

H100 cluster (3 GPUs):

  • Hardware rental: $5,899 (3 GPUs * $2.69/hr * 730 hours)
  • Electricity: $362.88 (if self-managed; $0 if cloud-hosted)
  • Total: $6,261.88

L40S cost advantage over six months: $3,812.44 (61% cheaper)

Even including electricity costs for self-managed infrastructure, L40S maintains dramatic cost advantage.

Cost-Effectiveness Framework

Selecting between L40S and H100 depends on workload characteristics and scale:

Training workloads: H100 is overwhelmingly cost-effective. The 7-8x training speed advantage makes H100 cheaper despite 6-8x higher hourly cost. This relationship holds for any model larger than 3-4 billion parameters.

Inference workloads with consistent demand: L40S becomes cost-effective at reasonable scale. If serving 100+ concurrent requests, the L40S's lower per-hour cost with more GPUs can be cheaper than fewer H100s. This calculation is sensitivity to demand patterns and SLA requirements.

Inference workloads with variable demand: L40S's lower hourly cost enables cost-effective autoscaling. Provisioning additional L40S GPUs for traffic spikes costs less than provisioning H100s.

Multimodal workloads: H100 provides better cost-per-inference due to superior performance. The 1.4-1.7x inference advantage narrows the effective cost difference.

Mixed training/inference workloads: Organizational strategy matters. Dedicated training on H100 with inference on L40S provides cost optimization compared to single-GPU-type strategies. This requires infrastructure complexity.

Use Case Optimization

Training scenarios favor H100 decisively:

Model training (any size): H100 is the correct choice. L40S training is inefficient even for small models due to memory constraints and performance limitations.

Fine-tuning operations: H100 remains preferable due to training performance. For very small models (under 3B parameters), L40S might be viable but still not cost-optimal.

Inference-only workloads benefit from careful analysis:

Cost-optimized inference at massive scale: L40S can be more economical. Serving billions of inference requests monthly, the difference in GPU hours compounds substantially. A 10% L40S advantage at large scale represents millions in cost savings.

Inference with latency-critical requirements: H100 is safer. For sub-200ms latency requirements, H100 provides margin. L40S might require load shedding or timeout risks.

Development and testing: L40S accelerates iteration for cost-conscious teams. Development environments don't require H100 performance, making L40S suitable for prototyping and experimentation.

Specialized inference tasks: H100 benefits specialized tasks leveraging its capabilities:

  • Dynamic shape inference (variable-size requests): H100 scheduler complexity handles variable workloads better
  • Complex models exceeding 40B parameters: H100 fits larger models in memory
  • Spiky traffic patterns: H100's per-request efficiency minimizes over-provisioning

L40S benefits inference scenarios:

  • Static inference workloads with known patterns: Optimization to L40S becomes straightforward
  • Cost-sensitive applications: High volume, lower margin businesses optimize toward L40S
  • Inference serving: Professional media applications benefit from L40S media encoding features
  • Development and testing: Experimentation is cheaper on L40S

Architecture-Specific Considerations

Memory architecture differences affect more than just bandwidth:

HBM3 memory (H100) enables efficient large batch inference. Multiple requests batched together benefit from HBM3's high bandwidth.

GDDR6 memory (L40S) optimizes for single-request latency. Individual inference requests experience minimal memory latency degradation.

For single-request latency-critical applications, L40S can match H100 performance per-request despite lower absolute throughput.

Tensor core specialization:

H100 tensor cores optimize for FP8 and TF32 operations (training focused).

L40S tensor cores are broader-the Ada architecture supports diverse compute patterns suitable for inference preprocessing, post-processing, and varied model types.

Media encoding/decoding hardware:

L40S includes dedicated media processing engines (NVENC/NVDEC). Applications combining video encoding with inference benefit from L40S's integrated capabilities.

H100 lacks specialized media hardware; video processing requires GPU shader cores, consuming compute capacity needed for inference.

FAQ

Should I use L40S or H100 for training?

Use H100. The 7-8x training speed advantage makes H100 cheaper per-token despite 6-8x higher hourly cost. L40S is not cost-effective for training workloads.

What's the break-even point for inference deployment?

For pure inference, L40S becomes cost-effective at approximately 5-10 concurrent requests. Below that, a single H100 might be cheaper. Above that, L40S scaling provides cost advantages.

Can I train on L40S?

Technically yes, for small models. Practically, no-training 7B models on L40S requires 12+ hours versus 1.5 hours on H100. The time cost (waiting for training) often exceeds the hardware cost savings.

What if I need both training and inference?

Dedicated infrastructure is optimal: H100 for training, L40S for inference. This requires operational complexity but provides cost optimization. Alternatively, use H100 for both and absorb higher inference costs.

Does quantization change the comparison?

Yes. Quantized inference (8-bit weights) narrows performance gaps between L40S and H100. However, training quantized models still requires H100.

Is H100 necessary for inference?

Not always. L40S handles inference adequately for most applications. H100 is necessary when latency is critical, model size is very large (40B+), or multimodal performance matters.

Which GPU should I use for my first deployment?

For training: H100 is required For inference: Start with L40S unless latency requirements specifically demand H100 For development: L40S for cost optimization

How does power consumption affect my actual costs?

Power consumption directly impacts self-managed infrastructure costs but not cloud-hosted deployments. L40S consumes 350W versus H100's 700W-a 50% difference. At $0.12 per kWh, running L40S for a month costs $30.24 in electricity versus H100's $60.48. In cloud environments where power is included in hourly rates, this advantage is already reflected in the pricing. H100 SXM also requires liquid cooling infrastructure, increasing operational costs by 5-15% for self-managed deployments.

What's the break-even point for switching from L40S to H100?

Consider H100 when latency requirements drop below 150ms, model size exceeds 40B parameters, or you need FP8 inference support. For inference at massive scale (billions of tokens monthly), L40S maintains cost advantage despite H100's throughput edge. Most cost-conscious deployments only switch to H100 when L40S becomes technically insufficient, not when cost optimization alone would justify the switch.

Can I use H100 PCIe instead of H100 SXM for cost savings?

H100 PCIe consumes 350W (matching L40S power) and costs roughly $1.49-1.99 per hour in cloud environments. However, H100 PCIe performance falls between L40S and H100 SXM while costing 1.9-2.5x more than L40S. This variant offers no economic advantage over either alternative and is rarely optimal unless requiring specific H100 architecture features.

For comprehensive GPU comparison and pricing:

Sources

NVIDIA specifications: Official NVIDIA data sheets for H100 SXM and L40S GPUs.

Performance benchmarks: MLPerf inference benchmarks, LLM inference benchmarks, custom measurements from cloud providers.

Pricing data: RunPod, Lambda Labs, CoreWeave pricing pages, March 2026.

Inference optimization: NVIDIA TensorRT documentation and optimization guides.