AI Chips & Cloud AI

Custom silicon powering the AI revolution, from wafer-scale to cloud scale

Explore Chips Pricing Calculator

6.0+

Custom Chips

4,614+

TFLOPS (Peak)

4.0T

Transistors (Max)

3.0+

Cloud Providers

CHIP LANDSCAPE

AI Chip Taxonomy

The AI silicon ecosystem spans custom ASICs, cloud TPUs, training accelerators, and neuromorphic chips, each optimized for different workloads.

Custom ASICs

Purpose-built silicon optimized for specific AI workloads, maximum efficiency, minimal flexibility.

Cerebras WSE-3Groq LPUSambaNova SN40L

Cloud TPUs

Google's tensor processing units, designed for JAX/TensorFlow training and inference at scale.

Google TPU v4TPU v5pTPU v7 (Ironwood)

Cloud Training

Cloud-native chips purpose-built for model training, cost-optimized for large-scale workloads.

AWS Trainium2Google TPU

Cloud Inference

Optimized for low-latency, high-throughput inference, delivering models to users efficiently.

AWS Inferentia2Google TPU

Neuromorphic

Brain-inspired computing architectures, spiking neural networks for ultra-efficient edge AI.

Intel Loihi 2

PRODUCT DEEP-DIVE

AI ASIC Product Lineup

Detailed specifications and positioning for the leading custom AI silicon on the market today.

Custom ASIC

Cerebras WSE-3

Wafer-Scale Engine

The world's largest chip, a full wafer of compute with no memory bottleneck. Dominates sparse models and massive parameter workloads.

Transistors

4 Trillion

SRAM

900 MB

Peak Perf.

125 PFLOPS

Cores

900,000

Custom ASIC

Groq LPU

Language Processing Unit

Purpose-built for LLM inference with deterministic ultra-low latency. Delivers 800+ tokens/sec on Llama 3, no batching required.

Throughput

800 tok/s

Latency

<10ms

Model

Llama 3

SRAM

230 MB

Cloud TPU

Google TPU v7 (Ironwood)

Next-Gen Tensor Unit

Google's most powerful TPU yet. Designed for massive-scale training and inference on Google Cloud with unprecedented efficiency.

Perf/Chip

4,614 TFLOPS

HBM

192 GB

Bandwidth

7.4 TB/s

Process

3nm

Cloud Training

AWS Trainium2

Cloud Training Optimized

Amazon's training-optimized chip with NeuronLink interconnect for massive scale. EC2 Trn2 instances deliver H100-class training at half the cost.

Perf.

1.8 TFLOPS

Interconnect

NeuronLink

Instance

EC2 Trn2

Scale

30K+ chips

Cloud Inference

AWS Inferentia2

High-Perf Inference

Purpose-built for high-performance inference. Excels at diffusion models and transformer inference with industry-best price/performance.

Perf.

4.5 TFLOPS

Memory BW

9.6 TB/s

Optimal for

Diffusion

vCPUs

Custom ASIC

SambaNova SN40L

RDU Architecture

Reconfigurable Dataflow Unit (RDU) with up to 1TB of external memory. Eliminates data movement bottlenecks for massive model training.

Ext. Memory

1 TB

Architecture

RDU

Process

7nm

Optimal for

DataScale

DEPLOYMENT COMPARISON

Data Center vs. Edge

Choosing between cloud-scale and edge deployment depends on your latency, power, and privacy requirements.

Feature

Data Center

Edge

Power

300W – 23kW per chip

5W – 75W

Memory

Up to 1TB HBM/SRAM

Up to 64GB LPDDR

Latency

Cloud network dependent

Sub-millisecond

Scale

10K+ chip clusters

Single device

Cost

$0.75 – $12.50 / hr

$50 – $500 device

Best For

Training, large inference

Real-time, on-device AI

CLOUD COST CALCULATOR

Cloud Pricing Calculator

Estimate your monthly cloud AI training and inference costs across providers.

Configuration

Select Chip

Chip Quantity: 8

1256

Hours/Month: 160 hrs(~100% utilization)

1 hr730 hrs (full month)

Estimated Monthly Cost

Google Cloud

$16,000

$12.5/hr × 8 chips × 160 hrs

Total Monthly$16,000

Based on 160 hours/month across 8 chips

SPEC COMPARISON

Performance Comparison

Side-by-side specification table of all leading AI ASICs and custom chips.

Chip	Architecture	Node	Memory	Peak Perf.	TDP	Price	Best Use Case
Cerebras WSE-3	Wafer-Scale (900K cores)	5nm	900MB SRAM	125 PFLOPS	23kW	Contact	Sparse models, massive params
Groq LPU	Tensor Streaming	14nm	230MB SRAM	188 TFLOPS	300W	$2.10/hr	LLM inference, low latency
TPU v7 Ironwood	TPU (MXU array)	3nm	192GB HBM	4,614 TFLOPS	~800W	Cloud	Training + inference scale
AWS Trainium2	NeuronCore v2	5nm	HBM shared	1.3 PFLOPS	300W	$1.89/hr	Cloud training at scale
AWS Inferentia2	NeuronCore v2	5nm	HBM shared	4.5 TFLOPS	200W	$0.75/hr	Diffusion, transformer inf.
SambaNova SN40L	RDU	7nm	1TB external	638 TFLOPS	400W	Contact	Enterprise AI, dataflow

FRAMEWORK SUPPORT

Software Ecosystem

Framework and toolchain support across the major AI accelerators.

Framework	Cerebras	Groq	Google TPU	Trainium	Inferentia	SambaNova
PyTorch	Native	Compile	XLA backend	Neuron SDK	Neuron SDK	SambaFlow
JAX	Limited	N/A	Native (best)	Plugin	Plugin	Limited
TensorFlow	Limited	N/A	Native	Neuron SDK	Neuron SDK	SambaFlow
ONNX	N/A	Native	Convert	Export	Export	Convert

Native support

SDK/Plugin

Conversion/Export

Not available

INDUSTRY TRENDS

How Custom Chips Are Challenging NVIDIA

The AI silicon landscape is shifting. Custom ASICs are gaining ground with better price/performance for specific workloads.

Cost Efficiency

AWS Trainium2 delivers H100-class training performance at ~50% the cost per training hour. Google TPU pods offer unmatched scaling economics.

Specialized Architectures

Unlike general-purpose GPUs, ASICs like Groq LPU and Cerebras WSE-3 are designed for specific AI workloads, eliminating overhead.

Cloud Integration

TPUs, Trainium, and Inferentia are deeply integrated into their respective cloud ecosystems, reducing deployment friction.

Energy Efficiency

Custom silicon achieves better performance-per-watt by eliminating general-purpose GPU circuitry not needed for AI workloads.

ASIC Release Timeline (2020–2025)

2020

Google TPU v4 launched, first massive pod-scale training

2021

Cerebras WSE-2: 2.6T transistors, record-breaking scale

2022

AWS Inferentia2 & Trainium enter general availability

2023

Groq LPU hits market, 500+ tok/s inference breakthrough

2024

Cerebras WSE-3: 4T transistors, 125 PFLOPS; TPU v5p (Trillium)

2025

TPU v7 Ironwood, AWS Trainium2 at scale, ASIC era begins

News & Updates

The latest developments in AI ASICs, custom silicon, and cloud AI infrastructure.

Cloud AI2 days ago

Google Unveils TPU v7 'Ironwood': 4,614 TFLOPS Per Chip

Google's latest Ironwood TPU delivers 4x the performance of v5p with 192GB HBM, positioning it as the most powerful AI accelerator in the cloud.

Custom ASIC4 days ago

Cerebras WSE-3 Benchmarked: 125 PFLOPS in Real-World Training

We put the 4-trillion transistor wafer-scale chip through comprehensive LLM training benchmarks. Results challenge GPU clusters on efficiency.

Inference1 week ago

Groq LPU Hits 800 Tokens/Sec on Llama 3 70B, New Record

Groq's Language Processing Unit sets a new inference speed record, delivering sub-10ms latency on the largest Llama 3 model without batching.

Cloud Training1 week ago

AWS Trainium2 Pods Now Available: 30K+ Chip Clusters

Amazon launches massive Trainium2 clusters via EC2, promising 50% cost reduction vs. H100 for large-scale training workloads.