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A16 vs L40S

Explore a head to head comparison of specifications, performance, and pricing.

A16

The NVIDIA A16 delivers high-performance computing capabilities for AI, machine learning, and data science applications.

ManufacturerNVIDIA
GPU Architecture
Average Price$3.37/hr
GPU VRAM64 GB
Cloud Availability1 clouds
System Memory960 GB
CPU Cores96
Storage1.7 TB

L40S

The NVIDIA L40S delivers high-performance computing capabilities for AI, machine learning, and data science applications.

ManufacturerNVIDIA
GPU Architecture
Average Price$4.94/hr
GPU VRAM48 GB
Cloud Availability7 clouds
System Memory1536 GB
CPU Cores142
Storage30.7 TB

A16 vs L40S: Which Should You Choose?

The A16 offers 64 GB of VRAM — 1.3× the 48 GB on the L40S — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the L40S delivers 91.61 TFLOPS versus 4.493 TFLOPS on the A16 — 20× faster for mixed-precision training and inference. Memory bandwidth favors the L40S at 0.86 TB/s compared to 0.00 TB/s on the A16, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the A16 is built on Ampere while the L40S uses Ada Lovelace, reflecting different generational capabilities and optimizations. On Shadeform, the A16 starts from $0.51/hr versus $0.74/hr for the L40S — 45% more expensive — reflecting the performance premium. The L40S is available across 7 cloud providers on Shadeform compared to 1 for the A16, giving more options for region and pricing flexibility.

A16 — Best Use Cases

  • General-purpose deep learning training
  • Fine-tuning models up to 13B parameters
  • AI inference at moderate throughput
  • Computer vision and NLP workloads

Choose A16 when:

  • You need 64 GB+ VRAM for large models or long context windows
  • Cost efficiency is your primary concern
  • Your workload does not require peak FP16 throughput
  • Your preferred provider already has availability

L40S — Best Use Cases

  • LLM inference and model serving
  • Image generation and diffusion models
  • Smaller fine-tuning runs
  • Cost-efficient GPU compute

Choose L40S when:

  • 48 GB VRAM is sufficient for your workload
  • Maximum performance justifies the higher cost
  • You are training large models or running high-throughput inference
  • You need flexibility across multiple cloud providers or regions

See how the A16 & L40S compare

Compare detailed hardware specifications and average pricing for the A16 and L40S.

Compare Hardware Specifications

A16L40S
GPU Type
A16
L40S
VRAM per GPU
64 GB
48 GB
Manufacturer
NVIDIA
NVIDIA
Architecture
Ampere
Ada Lovelace
Interconnect
PCIe Gen4
PCIe Gen4
Memory Bandwidth
4x 200 GB/s
864 GB/s
FP16 TFLOPS
4.493 TFLOPS (1:1)
91.61 TFLOPS (1:1)
CUDA Cores
4x 1,280
18176
Tensor Cores
4x 40 (3rd Gen)
568 (4th Gen)
RT Cores
4x 10 (2nd Gen)
142 (3rd Gen)
Base Clock
1312 MHz
1110 MHz
Boost Clock
1755 MHz
2520 MHz
TDP
250W
350W
Process Node
TSMC 8nm
TSMC 4N
Data Formats
INT8, BF16, FP16, TF32, FP32
INT4, INT8, FP8, BF16, FP16, TF32, FP32

Compare Average On-Demand Pricing

A16L40S
1 GPU
$0.51 /hr
$1.27 /hr
2 GPUs
$1.02 /hr
$2.60 /hr
4 GPUs
$2.05 /hr
$5.18 /hr
8 GPUs
$4.09 /hr
$8.70 /hr

Frequently Asked Questions: A16 vs L40S

The main differences are VRAM (64 GB vs 48 GB), FP16 throughput (4.493 vs 91.61 TFLOPS), architecture (Ampere vs Ada Lovelace). The A16 uses the Ampere architecture while the L40S is based on Ada Lovelace, giving each GPU different generational capabilities.

The L40S is generally better for large language model training due to its higher throughput and 48 GB of VRAM, which allows fitting larger models or larger batch sizes in a single pass. For smaller models or fine-tuning tasks where cost matters more, both GPUs can be effective.

On Shadeform, the A16 is available from $0.51/hr. The L40S starts from $0.74/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.

The A16 has more VRAM at 64 GB, compared to 48 GB on the L40S. Higher VRAM allows you to run larger models without quantization, use longer context windows, and process larger batch sizes — all of which improve throughput and reduce latency for memory-bound workloads.

Based on TFLOPS per dollar, the L40S offers better raw compute value at current Shadeform on-demand rates. However, the best choice depends on your specific workload — if you need the extra VRAM or throughput of the A16, paying the premium may be justified by faster job completion and lower total cost.

The L40S is currently available across 7 cloud providers on Shadeform's network, compared to 1 for the A16. Shadeform lets you deploy either GPU across all available providers from a single platform, so you can always find available capacity without manually checking each cloud.

Mixing different GPU types in a single training cluster is generally not recommended, as it creates performance bottlenecks where faster GPUs wait for slower ones. For best results, use a homogeneous cluster of either A16 or L40S. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.

Explore A16 & L40S Instances

Browse available instances with A16 and L40S GPUs. Filter by provider, availability, and more to find the perfect instance for your needs.

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