NVIDIA

A30

A30 — illustration of the card's form factor
VRAM
24GB
FP32 TFLOPS
10.3 teraFLOPS TFLOPS
TDP
165 W
Memory
HBM2

Provider Marketplace

Cheapest
$0.35/hour
Starting from
Best Value
$0.41/hour
Starting from
Enterprise Choice
$1.35/hour
Starting from

All Cloud Providers

5 Options available
Massed Compute logo
On-Demand
$0.35/ hour
Estimated Cost
Provision
Runcrate logo
On-Demand
$0.39/ hour
Estimated Cost
View Provider
Jarvis Labs logo
On-Demand
$0.41/ hour
Estimated Cost
Provision
RedSwitches logo
On-DemandAmsterdamNetherlands
$0.68/ hour
Estimated Cost
Provision
E2E Networks logo
On-Demand
$1.35/ hour
Estimated Cost
Provision

Estimates onlyrates are collected automatically from public provider pages and may be out of date. Prices vary by region, commitment term, and availability, and typically exclude storage, egress, and tax. Confirm current pricing with the provider before purchasing. Last collected .

Compute Performance

FP645.2 teraFLOPS TFLOPS
FP3210.3 teraFLOPS TFLOPS
TF3282 teraFLOPS | 165 teraFLOPS* TFLOPS
FP16165 teraFLOPS | 330 teraFLOPS* TFLOPS
BF16165 teraFLOPS | 330 teraFLOPS* TFLOPS
INT8330 TOPS | 661 TOPS*
INT4661 TOPS | 1321 TOPS*

Architecture

MicroarchitectureAmpere
Transistors54B
Tensor Cores3rd Gen
Matrix EngineTensor Float (TF32)
Sparse AccelerationSupported (structural sparsity, up to 2X)
Dynamic PrecisionSupported (FP64 to TF32 to INT4)

Memory & VRAM

Memory TypeHBM2
Total Capacity24GB
Bandwidth933GB/s

Connectivity & Scaling

InterconnectPCIe Gen4; Third-gen NVLINK
GenerationPCIe Gen4; Third-gen NVLINK
IB BandwidthPCIe Gen4: 64GB/s; Third-gen NVLINK: 200GB/s
PCIe InterfacePCIe Gen4
TopologyNVLink Bridge for up to two GPUs
Scale-OutNVIDIA InfiniBand

Virtualization

MIG SupportSupported
MIG Partitions4 MIGs @ 6GB each; 2 MIGs @ 12GB each; 1 MIGs @ 24GB
vGPU ReadinessSupported (NVIDIA AI Enterprise; NVIDIA Virtual Compute Server)
K8s ReadinessSupported (MIG works with Kubernetes)
GPU SharingMIG (Multi-Instance GPU)

Power & Efficiency

TDP165 W

Physical Design

Form FactorPCIe
FHFLYes
Slot WidthDual-slot

Software Ecosystem

Triton Serversupported
DockerNGC containers / containers and Kubernetes supported

Server & Deployment

OEM AvailabilityAvailable from OEM partners as NVIDIA-Certified Systems
PreconfiguredNVIDIA-Certified Systems (preconfigured systems program)
Edge DeploySupport for edge via NVIDIA EGX platform and stated availability to edge

System Compatibility

Required PCIePCIe Gen4
MotherboardDual-slot, full-height, full-length (FHFL); PCIe form factor (PCIe card)
Rack PowerMax thermal design power (TDP) 165W
OS CompatCertified to run on VMware vSphere in hypervisor-based virtual infrastructure; vGPU software support: NVIDIA AI Enterprise, NVIDIA Virtual Compute Server

Benchmarks & Throughput

Structured Sparsity

Supported (up to 2X more performance)

Training Benchmarks

AI Training—Up to 3X higher throughput than v100 and 6X higher than T4

Inference Benchmarks

AI Inference—Up To 3X higher throughput than V100 at real-time conversational AI

Scaling Efficiency

Can scale to thousands of GPUs when combined with NVLink, PCIe Gen4, networking and Magnum IO

Multi-GPU Scalability

Scaling Characteristics

ParallelismMulti-Instance GPU (MIG): can partition an A30 GPU into as many as four independent instances (options: 4×6GB, 2×12GB, or 1×24GB); MIG works with Kubernetes, containers, and hypervisor-based virtualization; provides secure hardware partitions with guaranteed QoS.

Workload Readiness

LLM Training

AI Training—Up to 3X higher throughput than v100 and 6X higher than T4

LLM Inference

AI Inference—Up To 3X higher throughput than V100 at real-time conversational AI

HPC / Simulation

HPC—Up to 1.1X higher throughput than V100 and 8X higher than T4

Scientific Computing

FP64 5.2 teraFLOPS

Real-Time Serving

BERT Large Inference (Normalized) Throughput for <10ms Latency

Market Authority

MLPerf Ranking

NVIDIA set multiple performance records in MLPerf

Community Benchmarks

MLPerf benchmark data referenced

Key Strengths

Limitations

Expert Insight

The A30 represents a strategic leap in AI compute. When comparing cloud providers, consider not just the hourly rate, but also the interconnect bandwidth (InfiniBand/NVLink) and regional availability which can significantly impact total cost of ownership for large-scale training.

Glossary Terms

FP32 TFLOPS
VRAM
TDP
Cores
Information updated daily. Cloud pricing subject to vendor availability.