Compute Comparison
NVIDIAAmpere2020

A40

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Current median: $0.85/hr across 13 providers (observed), 0.00 (0.0%) vs yesterday. Typical range: $0.56–$3.04/hr (P10–P90). 30-day trend unlocks after 30 days of captures

TDP 300W · 74.8 TFLOPS · 48GB VRAM · 696 GB/s bandwidth

The A40 has a 300W TDP, 74.8 TFLOPS of FP16 compute, 48GB of GDDR6 VRAM, 696 GB/s memory bandwidth.

48GB GDDR6 at a lower price than A100. Good for workloads needing large VRAM without HBM cost.

VRAM
48GB
GDDR6
FP16
74.8
TFLOPS
Bandwidth
696.0
GB/s
TDP
300W
power
Best for:Visualization + computeMid-size inferenceVirtual workstations

A40 currently rents for a median of $0.85/hr across 13 providers (observed on-demand prices). A40 has 48GB of memory. Compare its specs and listed cloud rental prices below. The workload must fit in VRAM with room for runtime memory; a listed price does not confirm stock.

A40 Overview

The NVIDIA A40 is an Ampere data-center GPU that combines 48GB of GDDR6 with a 300W thermal envelope and broad professional graphics support. Its 74.8 TFLOPS of FP16/BF16 compute makes it useful for mixed visualization and AI work, while its 48GB capacity is the feature that separates it from many lower-cost inference cards.

Its 696 GB/s GDDR6 bandwidth is far below A100-class HBM throughput, so it is better at workloads that need capacity or graphics features than at bandwidth-intensive autoregressive generation. The 48GB allocation gives useful room for medium-sized models, quantized larger models, and rendering assets, but there is no NVLink to combine memory across cards.

The A40 fits virtual workstations, rendering plus inference, and mid-size model serving where 48GB is more important than peak tokens per second. It is an older Ampere option with no FP8 support and is not a direct substitute for an HBM accelerator in large-model training.

Memory

VRAM48 GB
Memory TypeGDDR6
Bandwidth696 GB/s

Compute Performance

FP3237.4 TFLOPS
FP1674.8 TFLOPS
BF1674.8 TFLOPS
INT8149.7 TOPS

Hardware Specifications

Chip

ArchitectureGA102
GenerationAmpere
Process NodeSamsung 8nm
Transistors28.3B
Die Size628 mm²
Release DateOctober 5, 2020

Processors

CUDA / Shader Cores10,752
Tensor Cores336
RT Cores84

Clocks

Base Clock1,305 MHz
Boost Clock1,740 MHz

Memory

VRAM48 GB
Memory TypeGDDR6
Memory Bus384-bit
Bandwidth696 GB/s
L2 Cache6 MB

Power

TDP300 W
InterconnectPCIe 4.0

Relative Performance

FP16 Compute1%
VRAM Capacity17%
Mem Bandwidth4%

Relative to highest-spec GPU in database

Limitations

GDDR6 memory bandwidth (696 GB/s) far below HBM alternatives
No NVLink — cannot pool VRAM across cards
Older Ampere architecture — no FP8 support

Live Cloud PricingOn-demand hourly rates

Live20 providers · -27% price spread
ProviderOn-DemandSpotRegionAvailability
$0.89/hr—US/EU/Asiaunknown
$0.37/hr—USunknown
$0.55/hr—US/EUunknown
$0.52/hr—Asia-Pacificunknown
$0.79/hr—USunknown
$0.59/hr—EUunknown
$3.20/hr—EU/USunknown
$0.95/hr—EU-Westunknown
$0.49/hr—Globalunknown
$3.40/hr—US/EU/BRunknown
$2.42/hr—Los Angeles, CA, United Statesunknown
$0.65/hr—us-east-1unknown
$0.60/hr—us-west-1unknown
$1.86/hr—Variousunknown
$0.65/hr—Variousunknown
$0.55/hr—US/EUunknown
$0.59/hr—USunknown
$0.99/hr—EU/US/Asiaunknown
$0.85/hr—APACunknown
$0.65/hr—INunknown

Salad: Lowest priority tier — preemptible

Cheapest / month
$650
$0.89/hr × 730h
Median / month
$1767
at median price
Spot savings
N/A
vs on-demand (best)

Job Cost Estimator

At median $0.85/hr across 13 providers (observed on-demand). Spot pricing typically 40–70% lower.

7B LLM fine-tune
8× GPU, 24 h · Full fine-tune, single node
$163
spot ~$73
70B LLM fine-tune
64× GPU, 72 h · Multi-node, 3-day run
$3.9k
spot ~$1.8k
Inference server
1× GPU, 730 h · 1 month continuous
$621
spot ~$279
Image gen batch
4× GPU, 8 h · Batch generation job
$27
spot ~$12

Estimates only. Actual cost depends on provider, region, availability, and utilization. Use the GPU cost calculator for custom inputs.

Compare A40 vs…

Use Case Guidance

Visualization + compute
Mid-size inference
Virtual workstations

LLM Model Size Guidance

Max model (FP16)~24Bparameters at FP16 precision
Max model (INT8)~48Bparameters at INT8 precision
Max model (INT4)~96Bparameters at INT4/GGUF

Estimates only. Actual capacity depends on context length, KV cache, and framework overhead.

Related Guides

LLM APIs Running on This GPU Class

Providers that serve frontier LLM inference on Ampere-class hardware.

Browse all 42 LLM models

A40 vs Alternatives — Spec Comparison

SpecA40 thisRTX A6000RTX 3090RTX 5060 Ti
VRAM48GB GDDR6▲48GB GDDR6▲24GB GDDR6X16GB GDDR7
Memory Bandwidth696 GB/s768 GB/s936 GB/s▲672 GB/s
FP16 TFLOPS74.877.4▲7170.4
BF16 TFLOPS74.877.4▲7170.4
FP8 TFLOPS————
INT8 TOPS149.7309▲142140.8
TDP300W300W350W▲180W
Process NodeSamsung 8nm▲Samsung 8nm▲Samsung 8nm▲TSMC 4NP
ArchitectureGA102GA102GA102GB206▲
Release Year2020202020202025▲
Max model (FP16)~24B params▲~24B params▲~12B params~8B params
Max model (INT4)~96B params▲~96B params▲~48B params~32B params
▲ indicates best value in row · FP16/BF16 TFLOPS at full precision · Max model estimates at 2 bytes/param (FP16) and 0.5 bytes/param (INT4)Full side-by-side comparison

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A40 Frequently Asked Questions

What is the A40 TDP?

The A40 has a TDP (Thermal Design Power) of 300W.

How many TFLOPS does the A40 have?

The A40 delivers 74.8 TFLOPS of FP16 compute and 74.8 TFLOPS BF16.

How much VRAM does the A40 have?

The A40 has 48GB of GDDR6 VRAM.

What is the A40's memory bandwidth?

The A40 has a memory bandwidth of 696 GB/s.

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