Compute Comparison
NVIDIAAda Lovelace2023

L4

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Current median: $0.79/hr across 14 providers (observed), 0.00 (0.0%) vs yesterday. Typical range: $0.29–$0.95/hr (P10–P90). 30-day trend unlocks after 30 days of captures

TDP 72W · 60.6 TFLOPS · 24GB VRAM · 300 GB/s bandwidth

The L4 has a 72W TDP, 60.6 TFLOPS of FP16 compute, 24GB of GDDR6 VRAM, 300 GB/s memory bandwidth.

Extremely low TDP (72W). Ideal for high-density inference racks. Best performance-per-watt in its class.

VRAM
24GB
GDDR6
FP16
60.6
TFLOPS
Bandwidth
300.0
GB/s
TDP
72W
power
Best for:Efficient inferenceEdge deploymentsLow-power serving

L4 currently rents for a median of $0.79/hr across 14 providers (observed on-demand prices). L4 has 24GB 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.

L4 Overview

The NVIDIA L4 is an Ada Lovelace data-center GPU optimized for efficiency rather than maximum model size. It provides 24GB of GDDR6 and 60.6 TFLOPS of FP16/BF16 performance in an exceptionally low 72W envelope, which makes dense, power-conscious inference deployments practical.

The 300 GB/s memory bandwidth is modest, so autoregressive models that repeatedly stream large weight sets will become memory-bound sooner than on HBM GPUs. Its 24GB capacity is a useful fit for compact models and quantized serving, but it does not leave much headroom for large FP16 weights, long contexts, or oversized batches.

The L4 excels at efficient inference, edge-adjacent deployment, video and vision pipelines, and high-density serving of modest models. It is not designed for large-model training, and its lack of NVLink means each card remains an independent 24GB resource.

Memory

VRAM24 GB
Memory TypeGDDR6
Bandwidth300 GB/s

Compute Performance

FP3230.3 TFLOPS
FP1660.6 TFLOPS
BF1660.6 TFLOPS
INT8242 TOPS

Hardware Specifications

Chip

ArchitectureAD104
GenerationAda Lovelace
Process NodeTSMC 4N
Transistors35.8B
Die Size295 mm²
Release DateMarch 21, 2023

Processors

CUDA / Shader Cores7,680
Tensor Cores240
RT Cores60

Clocks

Base Clock795 MHz
Boost Clock2,040 MHz

Memory

VRAM24 GB
Memory TypeGDDR6
Memory Bus192-bit
Bandwidth300 GB/s
L2 Cache48 MB

Power

TDP72 W
InterconnectPCIe 4.0

Relative Performance

FP16 Compute1%
VRAM Capacity8%
Mem Bandwidth2%

Relative to highest-spec GPU in database

Limitations

Only 24GB VRAM — limits to ~13B models at FP16
Low memory bandwidth (300 GB/s) — bottleneck for large batches
No NVLink — single-card only

Live Cloud PricingOn-demand hourly rates

Live17 providers · 22% price spread
ProviderOn-DemandSpotRegionAvailability
$0.23/hr—USunknown
$0.55/hr—APAC/ME/LATAMunknown
$0.80/hr—US/EUunknown
$0.79/hr—USunknown
$0.80/hr—USunknown
$0.30/hr—us-central1unknown
$0.44/hr—USunknown
$1.24/hr—Paris, France, Warsaw, Polandunknown
$0.80/hr—us-eastunknown
$0.89/hr—US/EUunknown
$0.32/hr—EU-Westunknown
$0.95/hr—Variousunknown
$0.59/hr—USunknown
$0.95/hr—EU/USunknown
$0.51/hr—us-east-1unknown
$0.09/hr—Japan, JPunknown
$0.28/hr—EU-Westunknown
Cheapest / month
$168
$0.23/hr × 730h
Median / month
$584
at median price
Spot savings
N/A
vs on-demand (best)

Job Cost Estimator

At median $0.79/hr across 14 providers (observed on-demand). Spot pricing typically 40–70% lower.

7B LLM fine-tune
8× GPU, 24 h · Full fine-tune, single node
$152
spot ~$68
70B LLM fine-tune
64× GPU, 72 h · Multi-node, 3-day run
$3.6k
spot ~$1.6k
Inference server
1× GPU, 730 h · 1 month continuous
$577
spot ~$260
Image gen batch
4× GPU, 8 h · Batch generation job
$25
spot ~$11

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

Compare L4 vs…

Use Case Guidance

Efficient inference
Edge deployments
Low-power serving

LLM Model Size Guidance

Max model (FP16)~12Bparameters at FP16 precision
Max model (INT8)~24Bparameters at INT8 precision
Max model (INT4)~48Bparameters 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 Ada Lovelace-class hardware.

Browse all 42 LLM models
Best LLMs for this GPU
Self-hosting guide & model recommendations

L4 vs Alternatives — Spec Comparison

SpecL4 thisRTX 3080A16RTX 4070
VRAM24GB GDDR610GB GDDR6X64GB GDDR6▲12GB GDDR6X
Memory Bandwidth300 GB/s760 GB/s▲432 GB/s504 GB/s
FP16 TFLOPS60.659.662.4▲58.2
BF16 TFLOPS60.659.662.4▲58.2
FP8 TFLOPS————
INT8 TOPS242119250▲116
TDP72W320W▲250W200W
Process NodeTSMC 4NSamsung 8nm▲Samsung 8nm▲TSMC 4N
ArchitectureAD104▲GA102GA102AD104▲
Release Year2023▲202020212023▲
Max model (FP16)~12B params~5B params~32B params▲~6B params
Max model (INT4)~48B params~20B params~128B params▲~24B 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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L4 Frequently Asked Questions

What is the L4 TDP?

The L4 has a TDP (Thermal Design Power) of 72W.

How many TFLOPS does the L4 have?

The L4 delivers 60.6 TFLOPS of FP16 compute and 60.6 TFLOPS BF16.

How much VRAM does the L4 have?

The L4 has 24GB of GDDR6 VRAM.

What is the L4's memory bandwidth?

The L4 has a memory bandwidth of 300 GB/s.

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LLM Self-Hosting Guide

Best LLM models to run on the L4

Low-power GPU for efficient 7B model serving at scale