GPU comparison

L4 vs MI300X

Specifications and current cloud pricing, side by side.

Ada LovelacevsCDNA 3Updated 34 days ago

The MI300X wins for the most common use case of LLM training because its 1307.4 TFLOPS dense FP16 rating and 192 GB memory exceed the L4 specifications by wide margins and enable larger batches without frequent offloading.

L4 from $0.59/GPU/hrMI300X from $2.39/GPU/hr

Right now, from live stock

  • Cheapest right now: L4 at $0.59/hr on RunPod

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  • Most providers in stock: L4 and MI300X (2 each)

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  • Best $ per TFLOPS FP16 dense right now: MI300X ($0.0018 per TFLOPS-hour at 1,307.4 TFLOPS)

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Specifications Compared

SpecL4MI300X
TDP72W750W
VRAM24 GB192 GB
CUDA Cores7,424Not published
FP8 (dense)242 TFLOPS2,614.9 TFLOPS
Memory TypeGDDR6HBM3
ArchitectureAda LovelaceCDNA 3
FP16 (dense)121 TFLOPS1,307.4 TFLOPS
Form FactorsPCIeOAM
INT8 (dense)242 TOPS2,614.9 TOPS
InterconnectPCIe 4.0Infinity Fabric, PCIe 5.0
Tensor Cores232Not published
FP32 Performance30.3 TFLOPS163.4 TFLOPS
FP64 Performance0.5 TFLOPS81.7 TFLOPS
Memory Bandwidth300 GB/s5,300 GB/s
FP8 (with sparsity)485 TFLOPS5,229.8 TFLOPS
FP16 (with sparsity)242 TFLOPS2,614.9 TFLOPS
INT8 (with sparsity)485 TOPS5,229.8 TOPS

Performance Analysis

Dense FP16 performance stands at 121 TFLOPS on the L4 versus 1307.4 TFLOPS on the MI300X so the MI300X completes matrix operations more than ten times faster under identical dense conditions. The same ratio appears in dense FP8 at 242 TFLOPS versus 2614.9 TFLOPS. Memory bandwidth of 5300 GB/s on the MI300X compared with 300 GB/s on the L4 permits substantially larger batch sizes during both training and inference passes. FP32 figures of 30.3 TFLOPS and 163.4 TFLOPS indicate the MI300X sustains higher precision workloads with correspondingly greater throughput when sparsity modes remain disabled.

Current On-Demand Offers

Cheapest secure on-demand offer per provider that is reported in stock right now, per GPU per hour; multi-GPU instances show the whole-instance price alongside. Deploy opens the DeployGPU console for providers on the platform, otherwise the provider's own site. Stock is rechecked every minute.

L4

ProviderRegionGPUsPer GPU / hrInstance / hrDeploy
RunPodglobal1$0.59—Deploy
Orilille-41$0.93—Deploy

2 providers in stock, 2 offers. All L4 offers, price history and alerts

MI300X

ProviderRegionGPUsPer GPU / hrInstance / hrDeploy
RunPodglobal1$2.39—Deploy
Hot AisleMichigan1$2.99—Deploy

2 providers in stock, 2 offers. All MI300X offers, price history and alerts

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When to Choose the L4

The L4 suits deployments that operate under strict power limits because its TDP measures 72W. PCIe form factor and 24 GB capacity align with inference pipelines that process moderate batch sizes at dense FP16 rates of 121 TFLOPS. Organizations that prioritize lower energy draw over peak throughput select the L4 for edge inference nodes.

When to Choose the MI300X

The MI300X addresses training jobs that require 192 GB memory capacity and 5300 GB/s bandwidth to accommodate large models. Its dense FP16 rating of 1307.4 TFLOPS supports accelerated iteration cycles in scientific computing and fine tuning tasks. Teams that need maximum memory and compute scale choose the MI300X despite its 750W TDP.

Use Cases

LLM Training
MI300X

The MI300X supplies 1307.4 TFLOPS dense FP16 and 192 GB memory that accommodate large model training runs.

LLM Inference
MI300X

Dense FP16 performance of 1307.4 TFLOPS and 5300 GB/s bandwidth allow higher throughput on the MI300X.

Fine-tuning
MI300X

192 GB HBM3 and 2614.9 TFLOPS dense FP8 on the MI300X support efficient parameter updates at scale.

Stable Diffusion
L4

The L4 at 72W TDP and 24 GB capacity meets typical image generation demands with lower power draw.

Scientific Computing
MI300X

FP32 performance of 163.4 TFLOPS on the MI300X accelerates simulation workloads that require high memory bandwidth.

Frequently Asked Questions

What are the dense FP16 ratings?▾

The L4 lists 121 TFLOPS dense FP16 and the MI300X lists 1307.4 TFLOPS dense FP16.

How do the TDP values compare?▾

The L4 TDP measures 72W and the MI300X TDP measures 750W.

Which GPU offers higher memory bandwidth?▾

The MI300X reaches 5300 GB/s while the L4 reaches 300 GB/s.

What interconnects do they use?▾

The L4 uses PCIe 4.0 and the MI300X uses Infinity Fabric together with PCIe 5.0.

Are FP8 figures listed as dense or with sparsity?▾

Both GPUs publish separate dense FP8 and sparsity FP8 numbers with the L4 at 242 TFLOPS dense and the MI300X at 2614.9 TFLOPS dense.

Which is cheaper to rent, the L4 or the MI300X?▾

Cloud rental prices for both the L4 and MI300X vary by provider, configuration, and availability. This page shows live pricing from 25+ providers updated every 60 seconds. Scroll to the Live Cloud Pricing section to compare current rates.

How much VRAM does the L4 have compared to the MI300X?▾

The L4 has 24 GB of GDDR6 memory. The MI300X has 192 GB of HBM3 memory.

Can I find L4 and MI300X GPUs available to rent right now?▾

Yes. This page shows real-time availability across 25+ cloud GPU providers. The Live Cloud Pricing section displays only in-stock offers with current pricing.

What is the main difference between the L4 and the MI300X?▾

The L4 uses the Ada Lovelace architecture (2023) while the MI300X uses CDNA 3 (2023). The MI300X delivers 10.8x the dense FP16 throughput (1,307.4 vs 121 TFLOPS, both without sparsity) and 17.7x the memory bandwidth of the L4.

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How this page is made

  • Specifications come from the NVIDIA, AMD and Intel datasheets for the L4 and the MI300X. Dense and sparse throughput are listed separately.
  • Prices and availability are live: every offer shown is an in-stock, on-demand listing from a provider we track, rechecked every minute.
  • The written comparison (overview, performance notes, when to choose each card, verdict, use cases and FAQ) was drafted with an AI model from the specification table above and passed an automated check that rejects any figure not in that table. It was last generated on . It contains no prices; those are always read live.
  • Read how we collect the data, or report an error on this page.

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