AI accelerators beyond GPUs: the alternative silicon landscape

Updated December 11, 2025

December 2025 Update: AWS Trainium3 shipping with 2.52 PFLOPS FP8 per chip and 144GB HBM3e. Google TPU v7 Ironwood delivers 4,614 TFLOPS per chip—analysts calling it "on par with Blackwell." Intel confirms Gaudi discontinuation when next-gen GPUs launch 2026-2027. Groq LPU achieving 750 tokens/sec on smaller models while Cerebras WSE-3 hits 125 PFLOPS peak. Alternative silicon gaining traction for specific workloads despite NVIDIA's 80% market dominance.

NVIDIA holds approximately 80% of the AI accelerator market, but growing demand for cost-efficient and vertically integrated infrastructure is slowly increasing adoption of alternative silicon.¹ Google released its seventh-generation TPU Ironwood in November 2025, which analysts describe as "arguably on par with NVIDIA Blackwell."² AWS deployed over 500,000 Trainium2 chips for Anthropic's model training—the largest non-NVIDIA AI cluster in production.³ Cerebras launched the WSE-3 with 4 trillion transistors and 125 petaflops of peak performance.⁴ The AI accelerator landscape extends far beyond GPUs, offering architectures optimized for specific workloads that enterprises increasingly evaluate.

The GPU remains the default choice for flexibility and ecosystem maturity. CUDA's dominance and NVIDIA's sustained innovation make switching costs substantial. Yet hyperscalers designing their own silicon, startups challenging assumptions about chip architecture, and Intel's aggressive pricing all create options that did not exist five years ago. Organizations running AI at scale now evaluate accelerator choices as strategic infrastructure decisions rather than commodity procurement.

Google TPU: the hyperscaler benchmark

Google announced Trillium (TPU v6) in May 2024 and made it generally available in 2025.⁵ The sixth-generation TPU achieves 4.7 times the peak compute performance per chip compared to TPU v5e.⁶ Google expanded matrix multiply unit sizes and increased clock speeds to reach approximately 926 teraflops of BF16 performance.⁷

Memory capacity and bandwidth doubled over the previous generation.⁸ Trillium provides 32 gigabytes of HBM capacity per chip with proportionally increased bandwidth.⁹ The interchip interconnect bandwidth also doubled, improving multi-chip scaling efficiency.¹⁰

Energy efficiency improved by over 67% compared to TPU v5e.¹¹ Industry analysts estimate TPU v6 operates 60-65% more efficiently than GPUs, compared to 40-45% efficiency advantages in prior generations.¹² The efficiency gains compound at data center scale where power constraints limit deployment density.

Trillium scales to 256 TPUs in a single high-bandwidth, low-latency pod.¹³ Beyond pod-level scalability, multislice technology and Titanium Intelligence Processing Units enable scaling to hundreds of pods, connecting tens of thousands of chips in building-scale supercomputers.¹⁴ The largest Trillium cluster delivers 91 exaflops—four times more than the largest TPU v5p cluster.¹⁵

Training benchmarks demonstrate the performance improvements. Trillium delivered over four times the training performance increase for Gemma 2-27B, MaxText Default-32B, and Llama2-70B compared to TPU v5e.¹⁶ Inference throughput improved three times for Stable Diffusion XL.¹⁷ Google used Trillium to train Gemini 2.0.¹⁸

Google unveiled TPU v7 (Ironwood) at Cloud Next in April 2025.¹⁹ Ironwood delivers 4,614 teraflops per chip and will ship in configurations of 256 chips and 9,216 chips.²⁰ The SemiAnalysis team praised the silicon, stating Google's supremacy among hyperscalers is unmatched.²¹

TPU access requires Google Cloud. Organizations committed to multi-cloud or on-premises deployment cannot directly use TPU infrastructure. The cloud-only model limits adoption for organizations with data residency or sovereignty requirements that Google Cloud regions do not satisfy.

AWS Trainium: the Anthropic partnership

AWS launched Trainium3 in December 2025—the company's first 3nm AI chip.²² Each Trainium3 chip provides 2.52 petaflops of FP8 compute with 144 gigabytes of HBM3e memory and 4.9 terabytes per second of memory bandwidth.²³ The specifications represent 1.5 times more memory capacity and 1.7 times more bandwidth than Trainium2.²⁴

Trn3 UltraServers scale to 144 Trainium3 chips delivering 362 petaflops total FP8 performance.²⁵ A fully configured UltraServer provides 20.7 terabytes of HBM3e and 706 terabytes per second of aggregate memory bandwidth.²⁶ AWS claims 4.4 times more compute performance, 4 times greater energy efficiency, and nearly 4 times more memory bandwidth than Trainium2-based systems.²⁷

The NeuronSwitch-v1 fabric doubles interchip interconnect bandwidth over Trn2 UltraServer.²⁸ The all-to-all fabric architecture enables efficient distributed training across the full chip complement.

Project Rainier represents AWS's largest AI infrastructure deployment. AWS collaborated with Anthropic to connect more than 500,000 Trainium2 chips into the world's largest AI compute cluster—five times larger than the infrastructure used to train Anthropic's previous generation of models.²⁹ The partnership demonstrates Trainium viability for frontier model training.

Trainium2-based EC2 Trn2 instances offer 30-40% better price performance than GPU-based EC2 P5e and P5en instances according to AWS.³⁰ The cost advantage matters for sustained training workloads where compute costs dominate budgets.

AWS discontinued the Inferentia line because inference workloads increasingly resemble training in their computational requirements.³¹ The Trainium architecture now handles both training and inference, simplifying the chip portfolio.

Trainium4 is in development with expected delivery in late 2026 or early 2027.³² AWS announced at least 6 times FP4 throughput, 3 times FP8 performance, and 4 times more memory bandwidth compared to Trainium3.³³ Trainium4 will support NVIDIA NVLink Fusion interconnect technology, enabling integration with NVIDIA GPUs in common rack configurations.³⁴

Intel Gaudi: the price competitor

Intel launched Gaudi 3 in 2024, positioning it as a cost-effective alternative to NVIDIA H100.³⁵ Gaudi 3 uses two chiplets with 64 tensor processor cores, eight matrix multiplication engines, and 96 megabytes of on-die SRAM cache with 19.2 terabytes per second bandwidth.³⁶ The chip integrates 128 gigabytes of HBM2e memory with 3.67 terabytes per second bandwidth.³⁷

Gaudi 3 delivers 1,835 BF16/FP8 matrix teraflops at approximately 600 watts TDP.³⁸ Compared to NVIDIA H100, Gaudi 3 offers higher BF16 matrix performance (1,835 versus 1,979 teraflops without sparsity) and more HBM capacity (128 versus 80 gigabytes).³⁹ Memory bandwidth also exceeds H100.⁴⁰

Intel claims Gaudi 3 is typically 40% faster than NVIDIA H100 and could surpass H100 by up to 1.7 times training Llama2-13B at FP8 precision.⁴¹ Power efficiency claims are more dramatic—up to 220% of H100's value on Llama benchmarks and 230% on Falcon.⁴²

The pricing advantage is substantial. An eight-accelerator Gaudi 3 system costs $157,613 compared to $300,107 for an equivalent H100 system.⁴³ Per-chip pricing runs approximately $15,625 for Gaudi 3 versus $30,678 for H100.⁴⁴ The cost differential enables organizations to deploy roughly twice the compute capacity for equivalent budget.

Gaudi 3 uses HBM2e rather than HBM3 or HBM3e, contributing to the lower cost but limiting memory bandwidth compared to current-generation alternatives.⁴⁵ Organizations running memory-bandwidth-bound workloads should evaluate this tradeoff carefully.

The ecosystem challenge limits Gaudi adoption. NVIDIA's CUDA dominates AI development, and transitioning to Intel's tools requires engineering investment.⁴⁶ Intel's market share in AI accelerators remains negligible despite the competitive hardware.⁴⁷

Intel announced Gaudi will be discontinued when its next-generation AI GPUs launch in 2026-2027.⁴⁸ The discontinuation announcement creates adoption risk for organizations considering multi-year Gaudi deployments. Partners may hesitate to invest in a product line with announced end-of-life.

Groq LPU: inference speed leadership

Groq's Language Processing Unit (LPU) takes a fundamentally different architectural approach, optimizing specifically for inference rather than training.⁴⁹ The Tensor Streaming Processor architecture achieves 750 TOPS at INT8 and 188 teraflops at FP16 with massive on-chip SRAM bandwidth of 80 terabytes per second.⁵⁰

The first-generation LPU delivers over 1 teraop per second per square millimeter on a 14nm chip operating at 900 MHz.⁵¹ The second-generation LPU will use Samsung's 4nm process.⁵²

Inference speed defines Groq's value proposition. The LPU serves Mixtral 8x7B at 480 tokens per second and Llama 2 70B at 300 tokens per second.⁵³ Smaller models like Llama 2 7B achieve 750 tokens per second.⁵⁴ Groq was the first API provider to break 100 tokens per second on Llama2-70B.⁵⁵

The LPU delivers up to 18 times faster inference than traditional GPUs for language models with deterministic sub-millisecond latency.⁵⁶ Energy efficiency reaches 1-3 joules per token.⁵⁷

LPU cards cost approximately $20,000—comparable to high-end NVIDIA GPUs—but excel specifically in inference speed and efficiency.⁵⁸ The tradeoff is clear: LPUs handle inference only, not training.⁵⁹

Groq's deployment footprint expanded significantly in 2025. The company operates a dozen data centers across the US, Canada, the Middle East, and Europe.⁶⁰ In September 2025, Groq raised $750 million at a $6.9 billion valuation.⁶¹

The Saudi Arabia partnership announced in February 2025 commits $1.5 billion to build what Groq describes as the world's largest AI inferencing data center in Dammam.⁶² Initial deployments feature 19,000 LPUs with capacity expansions planned to exceed 100,000 LPUs by 2027.⁶³

Cerebras WSE-3: wafer-scale integration

Cerebras takes the most radical architectural approach, building chips at wafer scale rather than dicing wafers into individual processors.⁶⁴ The WSE-3 contains 4 trillion transistors across the entire wafer—46,225 square millimeters of silicon.⁶⁵

The WSE-3 packs 900,000 AI-optimized compute cores delivering 125 petaflops of peak AI performance.⁶⁶ On-chip SRAM reaches 44 gigabytes with 21 petabytes per second memory bandwidth.⁶⁷ Fabric bandwidth hits 214 petabits per second.⁶⁸ The chip is fabricated on TSMC's 5nm process.⁶⁹

The CS-3 system doubles the performance of CS-2 in the same 15-kilowatt power envelope.⁷⁰ A single CS-3 fits within 15U of rack space.⁷¹ External memory options extend capacity to 1.5 terabytes, 12 terabytes, or 1.2 petabytes depending on configuration.⁷²

Model capacity scales dramatically. The CS-3 can train neural network models up to 24 trillion parameters.⁷³ Clusters scale to 2,048 CS-3 systems delivering up to 256 exaflops of FP16 compute.⁷⁴

Cerebras claims significant ease-of-use advantages. The platform requires 97% less code than GPUs for LLMs and trains models from 1 billion to 24 trillion parameters in purely data parallel mode.⁷⁵ Compact four-system configurations can fine-tune 70B models in a day.⁷⁶ At full 2,048-system scale, Llama 70B trains from scratch in a single day.⁷⁷

The Condor Galaxy 3 supercomputer in Dallas will deploy 64 CS-3 systems for 8 exaflops of FP16 compute.⁷⁸ TIME Magazine recognized the WSE-3 as a Best Invention of 2024.⁷⁹

SambaNova SN40L: reconfigurable dataflow

SambaNova's Reconfigurable Dataflow Unit (RDU) architecture differs from both GPUs and custom ASICs.⁸⁰ The SN40L combines on-chip dataflow flexibility with a three-tier memory system: on-chip SRAM, on-package HBM, and off-package DRAM.⁸¹

The SN40L uses TSMC's 5nm process in a dual-die CoWoS package.⁸² Each socket contains 102 billion transistors delivering 640 BF16 teraflops and 520 megabytes of on-chip SRAM.⁸³ The DDR tier supports up to 1.5 terabytes of memory capacity at over 200 gigabytes per second bandwidth.⁸⁴

The three-tier memory architecture enables SambaNova to serve models up to 5 trillion parameters with 256,000+ sequence length on a single system node.⁸⁵

The reconfigurable dataflow architecture uses Pattern Compute Units, Pattern Memory Units, and Address Generation Units connected via a two-dimensional mesh interconnect.⁸⁶ The streaming dataflow allows fusing hundreds of operations into a single kernel call without manual kernel development.⁸⁷

Performance demonstrations show speedups from 2 times to 13 times on various benchmarks compared to unfused baselines on eight RDU sockets.⁸⁸ For Composition of Experts inference, the 8-socket RDU Node reduces machine footprint by up to 19 times, speeds up model switching by 15-31 times, and achieves 3.7 times overall speedup over DGX H100.⁸⁹

Sixteen SN40L RDUs create a single rack capable of running DeepSeek R1 671B and Llama 4 Maverick with fast inference.⁹⁰

Strategic considerations

Custom ASICs are growing faster than the GPU market according to industry analysts.⁹¹ Hyperscalers developing internal silicon—Google TPU, AWS Trainium, Microsoft Maia, Meta Artemis—optimize performance while reducing reliance on external vendors.⁹²

The ecosystem tradeoff remains fundamental. NVIDIA GPUs offer flexibility for adoption across AI workloads, but cost up to $40,000 and face supply constraints.⁹³ Designing custom ASICs requires tens of millions of dollars in upfront investment, limiting the approach to organizations with sufficient scale.⁹⁴

Organizations evaluating alternatives should consider workload specificity. Inference-heavy deployments may benefit from Groq's speed advantages. Training-dominated workloads with Google Cloud compatibility should evaluate TPU pricing. Cost-sensitive deployments might justify Gaudi's lower price point despite ecosystem limitations.

Lock-in implications vary by platform. Google and AWS alternatives require their respective clouds. Intel Gaudi runs on standard servers but faces discontinuation. Cerebras and SambaNova offer on-premises deployment but with specialized infrastructure requirements.

The 80% market share NVIDIA maintains reflects genuine advantages in ecosystem maturity, flexibility, and sustained innovation. Alternative accelerators succeed by excelling in specific dimensions—cost, inference speed, power efficiency, or vertical integration—rather than matching NVIDIA across all criteria. Organizations with workloads that align with these specializations find value in alternatives. Those requiring general-purpose flexibility continue choosing GPUs.

Quick decision framework

Accelerator Selection Guide:

Specification Comparison:

Key takeaways

For infrastructure architects: - Google TPU requires Google Cloud—evaluate only if GCP fits your multi-cloud strategy - AWS Trainium3 offers 30-40% better price-performance than GPU instances on AWS - Intel Gaudi 3 costs ~$15,625/chip vs ~$30,678 for H100—but faces discontinuation in 2026-2027 - Groq LPU delivers 18x faster inference than GPUs—inference-only, no training capability - Cerebras WSE-3 trains 24T parameter models in purely data parallel mode—simplified distributed training

For procurement teams: - NVIDIA maintains 80% market share—alternatives excel in specific niches, not general replacement - Intel Gaudi discontinuation creates adoption risk for multi-year deployments - AWS Trainium available only on AWS; TPU only on Google Cloud—lock-in implications differ - Groq raised $750M at $6.9B valuation; Saudi partnership commits $1.5B—inference market validation - Cerebras and SambaNova offer on-premises deployment with specialized infrastructure requirements

For strategic planning: - Custom ASICs grow faster than GPU market—hyperscalers reducing NVIDIA dependency - Inference workloads offer most opportunity for alternative adoption—CUDA moat narrower - Organizations need sufficient scale to justify alternative evaluation overhead - Hybrid strategies possible—train on NVIDIA, infer on alternatives where cost-effective - Alternative accelerator roadmaps matter—Trainium4 (2026-27), Gaudi discontinued, TPU v7 shipping

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Squarespace Excerpt (159 characters): Google TPU v7 rivals Blackwell. AWS Trainium3 hits 2.52 PFLOPS. Groq LPU delivers 750 tokens/sec. The AI accelerator landscape beyond NVIDIA's 80% market share.

SEO Title (55 characters): AI Accelerators Beyond GPUs: TPU, Trainium, Gaudi, Groq

SEO Description (155 characters): Google TPU Trillium, AWS Trainium3, Intel Gaudi 3, Groq LPU, Cerebras WSE-3, SambaNova SN40L. Analysis of AI accelerators challenging NVIDIA's GPU dominance.

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