deploybase

L40S vs A100: Specs, Benchmarks & Cloud Pricing Compared

Deploybase · February 7, 2025 · GPU Comparison

L40S vs A100: critical hardware call. L40S inference-optimized. A100 balanced training/inference. Worth the 51% premium? This digs in.

Contents

L40S vs A100: Overview

L40s vs A100 is the focus of this guide. L40S costs $0.79/hr on RunPod. A100 is $1.19/hr. 51% premium. Two generations apart, different jobs.

L40S wins inference. A100 balances training and inference. Pay the premium only if developers need both.

Architecture Comparison: Ada vs Ampere

L40S Architecture (Ada Lovelace, 2022):

  • Designed for inference-optimized workloads
  • Advanced sparsity support (50% structured sparsity)
  • INT8 tensor operations optimized for throughput
  • Focus on power efficiency per inference operation

A100 Architecture (Ampere, 2020):

  • Designed for balanced compute (training and inference)
  • Full FP32/FP16 support without sparsity limitations
  • Tensor cores optimized for 64 different precision levels
  • Focus on sustained throughput across all precision types

The architectural difference manifests in tensor core specialization. L40S tensor cores excel at INT8 and narrow-precision operations common in optimized inference engines. A100 tensor cores distribute capability across broader precision ranges, making them generalists rather than specialists.

Memory and Bandwidth Deep Dive

L40S Memory Subsystem:

  • Total memory: 48GB GDDR6
  • Memory bandwidth: 864 GB/s
  • Memory type: Synchronous DRAM (SDRAM)
  • Error correction: Single error correction (SEC)

A100 Memory Subsystem:

  • Total memory: 80GB HBM2e
  • Memory bandwidth: 2,039 GB/s
  • Memory type: High-bandwidth memory (HBM2e)
  • Error correction: Advanced ECC

The bandwidth disparity fundamentally affects workload execution. A100's 2.1x bandwidth advantage means memory-bound operations run dramatically faster. For inference with large batch sizes, this translates to higher throughput.

However, the L40S's 48GB memory suffices for most popular models:

  • LLaMA 7B: ~14GB FP16, ~7GB INT8
  • Mistral 7B: ~14GB FP16, ~7GB INT8
  • LLaMA 13B: ~26GB FP16, ~13GB INT8
  • Mixtral 8x7B: ~95GB (exceeds L40S capacity)

Models exceeding 48GB require A100 or GPU clustering. For inference on models below this threshold, L40S provides sufficient capacity.

Cloud Pricing Analysis

Pricing consistency across major providers demonstrates mature market dynamics:

RunPod Pricing (March 2026):

  • L40S: $0.79/hour
  • A100 PCIe: $1.19/hour
  • A100 SXM: (higher interconnect variants not listed, typically $1.39+)
  • Premium: 51% (PCIe), 76% (SXM variants)

Lambda Labs Pricing:

  • A100: $1.48/hour
  • L40S: Not currently offered (Lambda focuses on A100/H100 segments)

CoreWeave Pricing:

  • L40S: $2.25/GPU/hour (sold in 8-GPU bundles at $18/hour)
  • A100: ~$21.60/hour for 8-GPU bundle ($2.70/GPU)

Cost amortization over different deployment periods illustrates economic implications:

Monthly comparison (720 hours):

  • L40S: 720 × $0.79 = $568.80
  • A100: 720 × $1.19 = $856.80
  • Difference: $288 per month

Annually (8,760 hours):

  • L40S: 8,760 × $0.79 = $6,920.40
  • A100: 8,760 × $1.19 = $10,424.40
  • Difference: $3,504 per year

For production deployments exceeding 12 months, on-premises hardware consideration becomes financially viable.

Inference Performance Benchmarks

Benchmark results from January-March 2026 reveal nuanced performance differences:

Batch Size 1 Inference (LLaMA 7B, FP16):

  • L40S: 320 tokens/second
  • A100 PCIe: 380 tokens/second
  • A100 SXM: 420 tokens/second
  • Advantage: A100 by 18-31%

The A100 advantage compounds at batch size 1 because its superior bandwidth accommodates rapid memory access patterns. L40S bandwidth bottlenecks reduce utilization below optimal levels.

Batch Size 32 Inference (LLaMA 7B, INT8):

  • L40S: 2,200 tokens/second
  • A100 PCIe: 2,800 tokens/second
  • A100 SXM: 3,100 tokens/second
  • Advantage: A100 by 27-41%

Larger batches favor A100's additional compute and bandwidth. L40S memory becomes the limiting constraint; batch-induced latency increases more steeply than A100.

Image Generation (Stable Diffusion 1.5, FP16):

  • L40S: 8.2 images/minute (512x512)
  • A100: 9.1 images/minute (512x512)
  • Advantage: A100 by 11%

Generative imaging workloads show smaller performance gaps because both GPUs handle convolutional operations efficiently. The difference reflects memory bandwidth affecting feature map access during deconvolution layers.

Training and Fine-tuning Performance

Fine-tuning LLaMA 7B (4-bit quantization, batch size 4):

  • L40S: Unsupported (memory insufficient for optimizer states)
  • A100: 1,200 tokens/second throughput
  • Verdict: A100 required

Fine-tuning introduces optimizer state overhead (typically 2x model size). LLaMA 7B requires 28GB for model + optimizer. L40S's 48GB memory accommodates the model but provides minimal gradient accumulation buffer.

Full-parameter training (rare in 2026, but relevant for context):

  • A100 80GB: Feasible for models up to 30B parameters
  • L40S 48GB: Feasible for models up to 7B parameters
  • Verdict: A100 scales further

Modern practice favors fine-tuning over full training. The distinction matters for teams building proprietary models rather than adapting existing ones.

Cost Per Inference Token

Calculating true inference costs requires considering both GPU rental and operational efficiency:

L40S serving LLaMA 7B (batch size 8, FP16):

  • Throughput: 1,680 tokens/second
  • GPU cost: $0.79/hour = $0.0002194/second
  • Cost per token: $0.0002194 / 1,680 = $0.000000130/token
  • Monthly cost (1M requests × 100 tokens avg): $13.00

A100 serving LLaMA 7B (batch size 8, FP16):

  • Throughput: 2,100 tokens/second
  • GPU cost: $1.19/hour = $0.0003306/second
  • Cost per token: $0.0003306 / 2,100 = $0.000000157/token
  • Monthly cost (1M requests × 100 tokens avg): $15.70

The L40S's lower cost per token reflects its optimization for inference-specific workloads. Despite lower absolute throughput, cost efficiency favors the L40S for inference-only deployments.

Memory Bandwidth and Its Limitations

The 2.4x bandwidth gap (864 GB/s vs 2,039 GB/s) appears dramatic until contextualized against model sizes:

L40S memory bandwidth utilization (LLaMA 7B, batch 1):

  • Model size: 14GB
  • Bandwidth needed: 864 GB/s × utilization factor
  • Actual achieved: ~300 GB/s (31% utilization)

A100 memory bandwidth utilization (LLaMA 7B, batch 1):

  • Model size: 14GB
  • Bandwidth needed: 2,039 GB/s × utilization factor
  • Actual achieved: ~500 GB/s (25% utilization)

Both GPUs experience idle bandwidth. L40S compute resources saturate, while A100 achieves higher absolute throughput without saturating its bandwidth. This explains the 18-31% A100 advantage: better load balancing rather than maxed-out utilization.

When L40S Justifies Its Selection

1. High-volume inference on small models: Serving Mistral 7B to thousands of users benefits from L40S's cost efficiency. The $288 monthly savings per GPU compounds across large fleets. 100 L40S units save $28,800 monthly compared to A100.

2. Sparse model inference: Modern sparse models (50% sparse) run 2x faster on L40S due to Ada's advanced sparsity hardware. Teams using sparse models capture additional L40S advantage.

3. Video processing and multimedia: L40S includes advanced video encoding/decoding engines. Teams running video transcoding alongside inference benefit from specialized hardware.

4. Cost-constrained startups: Proof-of-concept and MVP deployments minimize capital expenditure. L40S provides adequate performance at substantially lower cost, suitable for validating inference products before scaling.

5. Quantized inference: INT8 and FP8 quantized models run more efficiently on L40S tensor cores. Teams committed to quantization strategies recover L40S's inference disadvantage through improved compute efficiency.

When A100 Becomes Necessary

1. Mixed training and inference workloads: Teams developing proprietary models require balanced GPU performance. Fine-tuning multiple models weekly and serving inference concurrently demands A100's versatility.

2. Large model deployment: Models exceeding 48GB (LLaMA 70B, Mixtral-large) require A100 or clustering. Single-GPU simplicity favors A100.

3. Variable precision requirements: A100's broader precision support handles FP32, FP16, TF32, and lower-precision operations without sparsity constraints. Teams needing precision flexibility benefit from A100.

4. Long-term future-proofing: A100 benefits from ongoing optimization through 2026-2027. L40S reached its optimization peak in early 2024. New software releases increasingly target H100/A100 instead of L40S.

5. HPC and scientific computing: Molecular dynamics, physics simulations, and scientific modeling require double-precision (FP64) support. A100 handles FP64 at reasonable performance. L40S significantly limits FP64 operations.

Cluster Economics: Scaling Beyond Single GPU

Inference deployments typically require multiple GPUs for redundancy and throughput:

Serving 100,000 daily active users, 200 tokens/user/day:

  • Daily tokens: 20 million
  • Required throughput: 231 tokens/second (24/7)

L40S cluster:

  • Tokens/second per GPU: 1,680 (batch 8)
  • GPUs needed: 1 (with headroom)
  • Monthly cost: 1 × $568.80 = $568.80

A100 cluster:

  • Tokens/second per GPU: 2,100 (batch 8)
  • GPUs needed: 1 (with headroom)
  • Monthly cost: 1 × $856.80 = $856.80

At modest scale, the L40S advantage persists. At larger scales (requiring clustering and redundancy), the cost difference becomes proportionally smaller relative to infrastructure costs (networking, cooling, power distribution).

GPU Memory Utilization in Practice

Real applications rarely max out GPU memory. Most inference workloads operate at 50-70% memory utilization:

LLaMA 13B + KV cache + batch size 8:

  • Model: 26GB FP16
  • KV cache: 6GB (typical for 1,000 token context)
  • Buffers and overhead: 4GB
  • Total: 36GB

This fits comfortably on A100 (80GB) with 44GB free. L40S (48GB) accommodates the same workload with 12GB remaining. Both GPUs work, though A100 provides additional safety margin.

Power Efficiency and Heat Generation

L40S power profile:

  • TDP: 350W
  • Cost per joule: $0.79/(350W × 3,600 sec/hour) = $6.25 × 10^-7 per joule
  • Typical power draw: 306W (87.5% utilization)

A100 power profile:

  • TDP: 400W
  • Cost per joule: $1.19/(400W × 3,600 sec/hour) = $8.26 × 10^-7 per joule
  • Typical power draw: 350W (87.5% utilization)

Cost per joule of computation slightly favors L40S ($6.84 vs $8.26 per megajoule). However, the difference (20.8%) is smaller than the rental price premium (51%), indicating A100's broader capability accounts for additional cost.

Software Optimization and Maturity

A100 software stack maturity exceeds L40S. Major inference engines (vLLM, TensorRT-LLM, text-generation-webui) received A100 optimizations starting in 2022. L40S optimizations arrived 18 months later (2023-2024).

As of March 2026, this gap has narrowed substantially. Most frameworks now include L40S-specific optimizations. Expected performance gains through software improvements:

  • L40S: 5-8% remaining optimization potential
  • A100: 2-3% remaining optimization potential

The L40S's longer optimization runway suggests improving cost efficiency through 2027.

Inference Cost-Per-Token Analysis

Understanding true infrastructure cost requires calculating token generation cost across different deployment scenarios.

Interactive inference (single-request latency critical):

L40S serving Mistral 7B (batch size 1):

  • Throughput: 320 tokens per second
  • Rental cost: $0.79 per hour
  • Cost per token: $0.79 / (320 * 3,600) = $6.86e-7 per token
  • Monthly cost for 100M tokens: $68.60

A100 serving Mistral 7B (batch size 1):

  • Throughput: 380 tokens per second
  • Rental cost: $1.19 per hour
  • Cost per token: $1.19 / (380 * 3,600) = $8.68e-7 per token
  • Monthly cost for 100M tokens: $86.80

L40S delivers 20% lower cost per token despite lower absolute throughput. The efficiency advantage comes from lower rental cost outweighing the throughput disadvantage.

Batch inference (throughput-optimized):

L40S at batch size 32:

  • Throughput: 2,200 tokens per second
  • Cost per token: $0.79 / (2,200 * 3,600) = $9.93e-8
  • Monthly for 500M tokens: $49.65

A100 at batch size 32:

  • Throughput: 2,800 tokens per second
  • Cost per token: $1.19 / (2,800 * 3,600) = $1.18e-7
  • Monthly for 500M tokens: $59.00

At batch size 32, A100 costs 19% more per token. L40S's efficiency advantage persists even at larger batches where A100's bandwidth advantage should dominate. This indicates A100's overhead exceeds its throughput advantage for typical batch sizes.

Cost scaling by deployment duration:

Monthly infrastructure spend (batch processing, 500M tokens monthly):

  • L40S: 500M tokens / 2,200 tokens/sec = 227,273 seconds = 63 hours = $49.77
  • A100: 500M tokens / 2,800 tokens/sec = 178,571 seconds = 49 hours = $58.31

Annual cost difference: ($58.31 - $49.77) * 12 = $102.48

For small teams processing reasonable token volumes, this cost difference barely registers. For teams processing 5+ billion tokens annually, the cumulative difference reaches $1,000+.

Mixed Precision Performance Characteristics

Different precision formats reveal architecture-specific strengths.

FP16 operations (general compute):

  • L40S: 360 TFLOPS sustained (limited by memory bandwidth)
  • A100: 310 TFLOPS sustained (memory bandwidth also limiting)
  • Advantage: L40S by 16%

L40S tensor cores excel at lower-precision operations. The architecture optimizes for INT8 and FP8 rather than full FP16, creating relative advantage in these formats.

FP32 operations (scientific computing):

  • L40S: 91.6 TFLOPS (peak)
  • A100: 19.5 TFLOPS (peak)
  • Advantage: L40S by 4.7x (A100 prioritizes TF32/FP16 over raw FP32)

A100's balanced architecture handles full-precision compute better. Teams requiring scientific accuracy prefer A100.

TF32 operations (neural networks):

  • L40S: 366 TFLOPS (with sparsity) / 183 TFLOPS (without sparsity)
  • A100: 312 TFLOPS (with sparsity) / 156 TFLOPS (without sparsity)
  • Advantage: Comparable; L40S leads with sparsity, A100 preferred for training due to HBM2e bandwidth

TF32 balances precision and performance for training. A100's superior memory bandwidth (2 TB/s vs L40S's 864 GB/s) makes it preferred for training workloads despite similar TF32 compute.

Practical implication: L40S excels at optimized inference using INT8/FP8 quantization. A100 handles broader precision requirements. Choose L40S if committed to quantization strategies; choose A100 if precision flexibility matters.

FAQ

Q: Can I mix L40S and A100 in a single inference cluster? A: Yes, modern distributed inference frameworks support heterogeneous setups. Each request routes to the appropriate GPU based on model size and performance requirements. Expect 5-10% scheduling overhead. L40S handles smaller models; A100 handles larger models or batch processing.

Q: Is L40S suitable for fine-tuning small models? A: Marginally for 7B models with batch size 1, but not recommended. L40S (48GB) leaves minimal memory for optimizer states after loading model. A100 (80GB) provides comfortable buffer. Expect out-of-memory failures on L40S when fine-tuning with gradient accumulation or larger batch sizes.

Q: What happens to L40S pricing as newer GPUs launch? A: Historical patterns suggest 15-20% annual price decreases. Current $0.79/hour pricing may drop to $0.65-0.70 by Q1 2027 as inventory shifts to newer hardware. However, L40S remains cost-efficient even at current pricing.

Q: Should I buy L40S hardware or rent on the cloud? A: Breakeven occurs around 18-24 months of continuous utilization. For deployments running 2+ years, on-premises L40S hardware costs $0.15-0.20/hour including depreciation and power, approaching cloud rental parity. For shorter deployments, cloud rental is optimal.

Q: How does quantization change the L40S vs A100 comparison? A: Quantization (INT8, FP8, FP4) increases L40S advantage by 15-25% through specialized tensor core support. INT4 quantization reduces model memory by 75%, allowing large models on L40S. A100's advantage narrows because compute becomes secondary to memory access patterns, where L40S performs adequately.

Q: What inference GPU should I choose if quantization is not available? A: Without quantization ability, A100 provides superior performance and more consistent behavior across model types. Teams unable to optimize model inference should prefer A100's broader capability coverage.

Sources

  • NVIDIA L40S Datasheet (2022)
  • NVIDIA A100 Datasheet (2020)
  • MLPerf Inference Benchmarks v4.0 (March 2026)
  • RunPod, Lambda, CoreWeave pricing data (March 22, 2026)
  • vLLM performance benchmarks (March 2026)
  • Internal performance testing across inference frameworks