bundle-combo—sap-bpc-classic-and-embedded By Uplatz<\/a><\/h3>\nAWS has pursued a pragmatic, bifurcated strategy, developing two distinct Application-Specific Integrated Circuits (ASICs): Trainium for cost-effective training and Inferentia for high-performance, low-cost inference. This specialization allows AWS to offer highly optimized price-performance for the two most common AI workloads, appealing to a broad range of cloud-native customers for whom total cost of ownership is a primary driver. The AWS Neuron SDK provides a unified software layer, simplifying development across this dual-chip architecture.<\/span><\/p>\nGroq, founded by a key architect of Google’s original TPU, introduces a disruptive paradigm focused exclusively on ultra-low-latency inference. Its Language Processing Unit (LPU) employs a radical, deterministic, compiler-driven architecture that eliminates the sources of unpredictability inherent in other systems. By using on-chip SRAM as primary memory and pre-scheduling every operation, Groq achieves unparalleled speed and consistency in token generation, positioning itself as the premier solution for the emerging wave of real-time, conversational, and agentic AI applications where user-perceived latency is the most critical performance metric.<\/span><\/p>\nPerformance benchmarks confirm this strategic differentiation. Google’s TPUs and AWS’s Trainium demonstrate significant cost-to-train advantages over GPUs for large models. For inference, AWS’s Inferentia offers superior throughput-per-dollar for batch-oriented tasks, while Groq’s LPU establishes a new standard for tokens-per-second and time-to-first-token in real-time scenarios, outperforming all competitors by a significant margin.<\/span><\/p>\nEconomically, the choice of accelerator represents a strategic commitment. Google and AWS leverage their custom silicon to create deeply integrated, but ultimately proprietary, cloud ecosystems, presenting a classic trade-off between streamlined MLOps and vendor lock-in. Groq, in contrast, offers an easily accessible, pay-per-token API and an on-premise option, promoting application-level portability at the cost of hardware-level control and model flexibility.<\/span><\/p>\nThis report concludes with a decision framework for technology leaders. The selection of an AI accelerator is no longer a simple choice of the fastest chip, but a strategic decision that must align with an organization’s primary workloads, economic model, and long-term platform strategy. For massive-scale training, Google’s TPUs remain a leading choice. For cost-optimized, high-throughput cloud deployment, AWS’s specialized chips present a compelling case. For applications where real-time responsiveness is a defining competitive advantage, Groq’s LPUs offer a capability that is currently unmatched in the market. The future of AI infrastructure will be heterogeneous, and understanding the distinct strengths of this new silicon triad is essential for navigating it successfully.<\/span><\/p>\n <\/p>\n
Section 1: The Custom Silicon Revolution in AI<\/b><\/h2>\n
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The landscape of artificial intelligence is being reshaped by a fundamental shift in its underlying hardware foundation. For years, the industry relied on the computational power of general-purpose processors\u2014first Central Processing Units (CPUs) and then, more decisively, Graphics Processing Units (GPUs). However, the explosive growth in the scale and complexity of AI models, particularly Large Language Models (LLMs), has exposed the inherent limitations of these architectures, catalyzing a move toward custom-designed, specialized silicon. This section explores the technical and strategic drivers behind this revolution, setting the stage for the emergence of a new class of AI accelerators.<\/span><\/p>\n <\/p>\n
1.1 The End of General-Purpose Dominance<\/b><\/h3>\n
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The computational paradigm of modern AI is dominated by a small set of mathematical operations, primarily large-scale matrix multiplications. While CPUs, with their design centered on sequential task execution, are ill-suited for the massive parallelism required, GPUs proved to be an effective, if accidental, solution. The architecture of a GPU, containing thousands of small Arithmetic Logic Units (ALUs) designed for parallel graphics rendering, was well-adapted to the matrix and vector operations at the heart of neural networks.<\/span>1<\/span> This adaptability made GPUs the workhorse of the AI revolution for over a decade.<\/span><\/p>\nHowever, this success came with inherent inefficiencies. GPUs are still general-purpose processors that must support a wide range of applications, carrying architectural overhead from their graphics legacy, such as hardware for rasterization and texture mapping, which is entirely superfluous for AI workloads.<\/span>2<\/span> For every calculation, a GPU must access registers or shared memory, a process that, while highly parallelized, still creates bottlenecks and consumes significant power.<\/span>1<\/span> As AI models grew from millions to billions and now trillions of parameters, the cost, power consumption, and physical footprint of training and serving these models on GPU-based infrastructure became a primary business constraint, pushing the limits of economic and environmental sustainability.<\/span>5<\/span> The industry reached a point where simply adding more general-purpose processors was no longer a viable long-term strategy, necessitating a new approach: hardware designed from the ground up for the specific demands of AI.<\/span><\/p>\n <\/p>\n
1.2 The Strategic Imperative for Hyperscalers<\/b><\/h3>\n
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The first to experience these scaling pains most acutely were the hyperscale cloud providers: Google, Amazon Web Services (AWS), and Microsoft. Operating at a global scale, they faced the dual challenge of powering their own massive AI-driven services (such as search, e-commerce recommendations, and social media feeds) and providing the computational infrastructure for the entire AI industry. This unique position created a powerful strategic imperative to invest billions of dollars in developing custom silicon, a move driven by several key factors:<\/span><\/p>\n\n- Performance and Efficiency Optimization:<\/b> Custom ASICs can be meticulously tailored to a company’s specific software stack and dominant workloads. By stripping away unnecessary components and optimizing data paths for AI-specific operations like tensor calculations, these chips can achieve significant gains in performance and power efficiency (performance-per-watt) compared to their general-purpose counterparts.<\/span>6<\/span><\/li>\n
- Cost Control and Supply Chain Security:<\/b> Designing chips in-house allows companies to reduce their dependency on a small number of third-party suppliers, most notably NVIDIA. This vertical integration provides greater control over the supply chain, mitigates the risk of shortages, and allows hyperscalers to capture the hardware profit margin themselves, ultimately lowering the total cost of ownership (TCO) for their massive data center fleets.<\/span>8<\/span><\/li>\n
- Competitive Differentiation:<\/b> In the highly competitive cloud market, custom silicon creates a powerful “moat.” By offering infrastructure that is demonstrably faster, cheaper, or more efficient for AI workloads, cloud providers can make their platforms more attractive and “stickier” for the most valuable customers in the AI space. The hardware becomes a key differentiator for the entire cloud ecosystem.<\/span>10<\/span><\/li>\n
- Meeting Unprecedented Scale:<\/b> The computational demand of training and deploying state-of-the-art generative AI models is staggering. Foundational models require computations measured in exaflops. Custom silicon is not merely an optimization but a necessity to meet this demand in a physically and economically feasible manner.<\/span>5<\/span><\/li>\n<\/ul>\n
This trend of vertical integration is a direct consequence of AI evolving from a niche research field into a core, industrial-scale business function. The initial experimental phase, where the flexibility of expensive GPUs was paramount, has given way to a production phase where industrial-grade efficiency is the primary concern. This mirrors historical shifts in computing, such as the development of specialized network interface cards (NICs) or video encoding hardware, where functions once handled by general-purpose CPUs were offloaded to dedicated ASICs for superior performance and efficiency.<\/span><\/p>\n <\/p>\n
1.3 Introducing the Triad<\/b><\/h3>\n
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This strategic imperative has given rise to a new set of powerful, specialized AI accelerators. This report focuses on three of the most significant and distinct approaches, which together form a new triad of custom silicon, each representing a different philosophy and strategic bet on the future of AI.<\/span><\/p>\n\n- Google’s Tensor Processing Unit (TPU):<\/b> The pioneering incumbent in this space. The TPU was born from Google’s internal need to handle the massive inference demands of its core products like Search and Photos.<\/span>3<\/span> It has since evolved into a mature, powerful architecture for both training and inference, deeply integrated into the Google Cloud Platform and representing a bet on massive scale and a unified, deeply optimized hardware-software stack.<\/span><\/li>\n
- AWS Trainium & Inferentia:<\/b> AWS’s pragmatic, market-driven response. Recognizing that training and inference have distinct technical requirements and economic profiles, AWS developed a bifurcated strategy with two separate chips: Trainium, optimized for cost-effective training, and Inferentia, optimized for high-throughput, low-cost inference.<\/span>12<\/span> This approach is a bet on market segmentation and providing cost-optimized solutions for the most common cloud workloads.<\/span><\/li>\n
- Groq’s Language Processing Unit (LPU):<\/b> The radical innovator, founded by Jonathan Ross, one of the original engineers behind Google’s TPU.<\/span>3<\/span> Groq’s LPU is built on the philosophy that for a critical and growing class of interactive AI applications, predictable, ultra-low latency is the single most important metric. The LPU’s unique deterministic architecture sacrifices training capabilities entirely to become the world’s fastest inference engine, representing a highly specialized bet on a future dominated by real-time, conversational AI.<\/span>14<\/span><\/li>\n<\/ul>\n
Together, these three platforms represent the leading edge of custom AI silicon and offer a clear alternative to the GPU monoculture. Understanding their distinct architectures, software ecosystems, performance characteristics, and economic models is now essential for any technology leader charting a course in the AI era.<\/span><\/p>\n <\/p>\n
Section 2: Architectural Deep Dive: The Engines of AI Acceleration<\/b><\/h2>\n
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The performance, efficiency, and scalability of any AI system are fundamentally determined by the architecture of its underlying silicon. While Google’s TPUs, AWS’s Trainium and Inferentia, and Groq’s LPUs are all classified as ASICs for AI, their core design philosophies and hardware components diverge significantly. These differences reflect distinct strategic bets on which aspects of AI computation are most critical to optimize. This section provides a granular, comparative analysis of each hardware platform, from its high-level design principles down to its specific compute, memory, and interconnect systems.<\/span><\/p>\n <\/p>\n
2.1 Google’s Tensor Processing Unit (TPU): A Legacy of Systolic Acceleration<\/b><\/h3>\n
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Google’s TPU architecture has evolved significantly since its inception, but its core design philosophy remains rooted in the concept of using large systolic arrays to deliver massive throughput for matrix multiplication, the foundational operation of deep learning.<\/span><\/p>\n <\/p>\n
Design Philosophy<\/b><\/h4>\n
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The TPU project began internally at Google in the early 2010s to address the growing computational bottleneck of running deep learning models for inference in its data centers.<\/span>5<\/span> The first generation, TPU v1, was a dedicated inference accelerator, focused on executing pre-trained models quickly and efficiently.<\/span>15<\/span> Recognizing that model training was an even greater challenge, Google expanded the architecture’s scope. Starting with TPU v2, the platform became a dual-purpose system capable of both high-performance training and inference, evolving into a full-fledged supercomputing architecture.<\/span>5<\/span> The central principle is to maximize computational density and data throughput by designing hardware specifically for the wave-like data flow of systolic computation.<\/span>3<\/span><\/p>\n <\/p>\n
Core Components<\/b><\/h4>\n
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The modern TPU chip is a complex system-on-a-chip (SoC) built around specialized processing units:<\/span><\/p>\n\n- TensorCores:<\/b> This is the fundamental compute engine within a TPU chip. Each chip in recent generations, such as the TPU v4, contains two TensorCores.<\/span>18<\/span> Each TensorCore is a self-contained processor with its own set of compute units and memory.<\/span><\/li>\n
- Matrix Multiply Units (MXUs):<\/b> The heart of the TensorCore is the MXU, a large two-dimensional systolic array of arithmetic logic units. For example, older TPUs used 128×128 arrays, while newer generations like Trillium feature larger 256×256 arrays.<\/span>20<\/span> These arrays are designed to perform thousands of multiply-accumulate operations in a single clock cycle, making them exceptionally efficient for the dense matrix multiplications found in neural networks.<\/span>17<\/span> They typically accept lower-precision inputs like bfloat16 for speed but perform accumulations in higher-precision FP32 to maintain numerical accuracy.<\/span>20<\/span><\/li>\n
- Vector and Scalar Units:<\/b> Complementing the MXU, each TensorCore also includes a Vector Processing Unit (VPU) for element-wise operations (like applying activation functions such as ReLU) and a Scalar Unit for control flow and calculating memory addresses.<\/span>18<\/span><\/li>\n
- Memory System:<\/b> TPUs rely on High-Bandwidth Memory (HBM), which is integrated onto the same package as the TPU chip. This provides a large pool of fast memory with very high bandwidth, crucial for feeding the hungry MXUs with data. For example, a TPU v4 chip has a unified 32 GiB HBM2 memory space with 1,200 GB\/s of bandwidth, shared across its two TensorCores.<\/span>3<\/span><\/li>\n<\/ul>\n
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Scalability and Interconnect<\/b><\/h4>\n
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A single TPU chip is powerful, but the architecture’s true strength lies in its ability to scale to massive supercomputer-scale systems known as “pods.”<\/span><\/p>\n\n- Inter-Chip Interconnect (ICI):<\/b> Google developed a custom, high-speed, low-latency optical interconnect fabric that links thousands of TPU chips together.<\/span>20<\/span> This allows a large cluster of TPUs to function as a single, cohesive machine, which is essential for distributed training of very large models.<\/span><\/li>\n
- 3D Torus Topology:<\/b> Starting with TPU v4, Google introduced a 3D mesh\/torus interconnect topology. Each TPU chip has direct network connections to its nearest neighbors in three dimensions.<\/span>18<\/span> This physical arrangement reduces the average distance data packets must travel between chips, improving communication efficiency, increasing the system’s bisection bandwidth, and enabling better load balancing for complex communication patterns like all-reduce operations.<\/span>18<\/span><\/li>\n
- Scaling Hierarchy (Pods, Slices, and Cubes):<\/b> The physical and logical scaling of TPUs follows a clear hierarchy. Individual chips are grouped into boards. Multiple boards form a “cube” or “rack,” a physical unit that in the v4 generation contains 64 chips in a 4x4x4 topology.<\/span>19<\/span> A full “TPU Pod” is the largest unit connected by the high-speed ICI network, comprising up to 4,096 chips in a v4 pod or 8,960 in a v5p pod.<\/span>18<\/span> A “slice” is a logical partition of a pod, representing the group of TPUs allocated to a user’s job.<\/span>20<\/span><\/li>\n<\/ul>\n
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2.2 AWS’s Bifurcated Strategy: Trainium for Training, Inferentia for Inference<\/b><\/h3>\n
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In contrast to Google’s unified architecture, AWS adopted a pragmatic, market-driven strategy that acknowledges the distinct technical and economic requirements of AI model training versus inference. This led to the development of two separate, purpose-built families of ASICs, managed under a common software umbrella.<\/span><\/p>\n <\/p>\n
Design Philosophy<\/b><\/h4>\n
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The core of AWS’s strategy is workload specialization.<\/span>13<\/span> Training large models is a throughput-bound task that requires immense computational power, massive memory bandwidth, and ultra-fast interconnects for distributed processing. It is a high-cost, but often infrequent, capital expenditure. Inference, on the other hand, is a latency-sensitive, high-volume operational task that runs continuously in production. It demands efficiency, low cost-per-inference, and responsiveness. By creating two different chips, AWS can optimize each one for its specific task without making the design compromises inherent in a one-size-fits-all approach.<\/span><\/p>\n <\/p>\n
AWS Trainium Architecture<\/b><\/h4>\n
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Trainium is AWS’s accelerator designed exclusively for high-performance deep learning training.<\/span>12<\/span><\/p>\n\n- Purpose-Built for Training:<\/b> The architecture is optimized to provide the best price-performance for training large models, particularly those with over 100 billion parameters, such as LLMs and diffusion models.<\/span>12<\/span><\/li>\n
- NeuronCore-v2:<\/b> At the heart of a Trainium chip are two second-generation NeuronCores.<\/span>28<\/span> These cores feature powerful systolic arrays for matrix math and support a wide range of numerical formats, including FP32, TF32, BF16, FP16, and the new configurable FP8 (cFP8). It also incorporates hardware-accelerated stochastic rounding, which can improve training performance and accuracy.<\/span>12<\/span><\/li>\n
- Memory and Interconnect:<\/b> Each Trainium chip is equipped with 32 GB of HBM, providing 820 GB\/sec of memory bandwidth.<\/span>28<\/span> For scaling out, chips are connected via NeuronLink-v2, a high-speed, proprietary chip-to-chip interconnect that enables efficient model and data parallelism and allows for memory pooling across devices within an instance.<\/span>12<\/span><\/li>\n
- UltraClusters and UltraServers:<\/b> For training at the largest scale, AWS connects multiple instances (each containing 16 Trainium chips) into “UltraServers” and “UltraClusters”.<\/span>12<\/span> An UltraServer connects 64 Trainium2 chips into a single node, and these can be scaled further into clusters of up to 40,000 chips connected via a non-blocking, petabit-scale Elastic Fabric Adapter (EFA) network.<\/span>26<\/span><\/li>\n<\/ul>\n
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AWS Inferentia Architecture<\/b><\/h4>\n
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Inferentia is AWS’s accelerator family optimized for high-throughput, low-latency, and cost-effective inference in production environments.<\/span>25<\/span><\/p>\n\n- Purpose-Built for Inference:<\/b> The architecture has evolved to meet the demands of increasingly complex models. The first generation, Inferentia1, powered Inf1 instances and was highly effective for models like BERT.<\/span>31<\/span> The second generation, Inferentia2, was designed from the ground up to handle large-scale generative AI models and was the first AWS inference chip to support scale-out distributed inference.<\/span>31<\/span><\/li>\n
- NeuronCore Generations:<\/b> The evolution from Inferentia1 to Inferentia2 brought significant architectural improvements. Inferentia1 featured four NeuronCore-v1s per chip and used 8 GB of slower DDR4 DRAM.<\/span>31<\/span> Inferentia2 features two more powerful NeuronCore-v2s per chip but upgrades the memory to 32 GB of HBM, increasing memory capacity by 4x and bandwidth by over 10x.<\/span>31<\/span> This is critical for serving large models.<\/span><\/li>\n
- Distributed Inference with NeuronLink:<\/b> A key feature of Inferentia2 is its use of NeuronLink, an ultra-high-speed interconnect between chips.<\/span>25<\/span> This allows very large models, whose parameters do not fit into the memory of a single chip, to be sharded across multiple Inferentia2 accelerators, enabling the efficient deployment of models with hundreds of billions of parameters.<\/span>25<\/span><\/li>\n<\/ul>\n
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2.3 Groq’s Language Processing Unit (LPU): A Paradigm Shift in Deterministic Computing<\/b><\/h3>\n
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Groq’s LPU represents the most radical departure from conventional accelerator design. Founded by a key member of Google’s original TPU team, Groq took a first-principles approach to solve a single, specific problem: eliminating the non-determinism that creates unpredictable latency in AI inference.<\/span>3<\/span> The entire architecture is built around predictability and compiler control.<\/span><\/p>\n <\/p>\n
Design Philosophy<\/b><\/h4>\n
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The core philosophy of the LPU is “software-first” and deterministic.<\/span>4<\/span> Traditional GPUs and TPUs rely on complex hardware mechanisms\u2014such as caches, schedulers, and arbiters\u2014to manage the execution of tasks at runtime. While this provides flexibility, it also introduces variability and unpredictability; the exact time an operation will take can vary depending on resource contention and cache hits\/misses.<\/span>4<\/span> Groq’s architecture eliminates these reactive hardware components entirely. Instead, all scheduling and data movement is pre-planned and orchestrated by a purpose-built compiler ahead of time. The hardware simply executes a pre-determined, static plan, ensuring that every operation takes a precisely known number of clock cycles, every time.<\/span>4<\/span> This makes the system’s performance completely predictable.<\/span><\/p>\n <\/p>\n
Architectural Breakdown<\/b><\/h4>\n
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This philosophy leads to a unique set of architectural choices that differentiate the LPU from all other accelerators:<\/span><\/p>\n\n- Single-Core, Compiler-Defined Architecture:<\/b> Rather than a multi-core design, the LPU functions as a single, massive, programmable assembly line.<\/span>34<\/span> The compiler defines every step of this assembly process, dictating exactly when and where data moves and is processed. This removes the software complexity and overhead associated with managing and synchronizing thousands of independent cores.<\/span>37<\/span><\/li>\n
- On-Chip SRAM as Primary Storage:<\/b> This is arguably the LPU’s most significant architectural innovation. Instead of relying on off-chip HBM, the LPU integrates hundreds of megabytes of high-speed SRAM directly onto the silicon die to be used as the primary storage for model weights.<\/span>35<\/span> While SRAM is more expensive and less dense than HBM, its proximity to the compute units provides vastly superior memory bandwidth (upwards of 80 TB\/s, an order of magnitude higher than HBM) and dramatically lower access latency.<\/span>34<\/span> This eliminates the primary bottleneck in many inference workloads: fetching model parameters from memory.<\/span><\/li>\n
- Direct Chip-to-Chip Connectivity:<\/b> To scale beyond a single chip, LPUs connect directly to each other using a plesiosynchronous protocol, bypassing traditional networking switches and routers.<\/span>37<\/span> The Groq compiler statically schedules not only the computations within each chip but also the data transfers between chips. It knows precisely when a data packet will arrive at a neighboring chip, allowing hundreds of LPUs to operate in perfect lockstep as if they were a single, monolithic core.<\/span>36<\/span><\/li>\n<\/ul>\n
The architectural bets made by each company reveal their core strategic priorities. Google’s unified architecture is a bet on the economies of scale that come from deep vertical integration and a design that can serve both massive training and inference workloads. AWS’s specialized, dual-chip strategy is a bet on market segmentation, offering customers cost-optimized solutions for the two most common and distinct AI tasks in the cloud. Groq’s highly specialized, deterministic architecture is a bet that as AI becomes more interactive and conversational, predictable low latency will become the most valuable performance characteristic, creating a new market for an inference-only accelerator that is unmatched in speed and responsiveness.<\/span><\/p>\nTable 1: Key Architectural Specifications Comparison (Latest Generations)<\/b><\/p>\n\n\n\n| Feature<\/b><\/td>\n | Google TPU v5p\/Trillium<\/b><\/td>\n | AWS Trainium2<\/b><\/td>\n | AWS Inferentia2<\/b><\/td>\n | Groq LPU<\/b><\/td>\n<\/tr>\n\n| Primary Use Case<\/b><\/td>\n | Training & Inference<\/span><\/td>\n | Training<\/span><\/td>\n | Inference<\/span><\/td>\n | Inference<\/span><\/td>\n<\/tr>\n\n| Core Compute Unit<\/b><\/td>\n | TensorCore<\/span><\/td>\n | NeuronCore-v2<\/span><\/td>\n | NeuronCore-v2<\/span><\/td>\n | LPU Core<\/span><\/td>\n<\/tr>\n\n| Core Principle<\/b><\/td>\n | Scalable Systolic Array<\/span><\/td>\n | Specialized Systolic Array<\/span><\/td>\n | Specialized Systolic Array<\/span><\/td>\n | Deterministic, Compiler-Scheduled<\/span><\/td>\n<\/tr>\n\n| On-Package Memory<\/b><\/td>\n | HBM3<\/span><\/td>\n | HBM3 (96GB per chip)<\/span><\/td>\n | HBM (32GB per chip)<\/span><\/td>\n | On-chip SRAM (Hundreds of MB)<\/span><\/td>\n<\/tr>\n\n| Memory Bandwidth<\/b><\/td>\n | >3 TB\/s (estimated)<\/span><\/td>\n | 46 TBps (per 16-chip instance)<\/span><\/td>\n | 10x over Inferentia1<\/span><\/td>\n | >80 TB\/s<\/span><\/td>\n<\/tr>\n\n| Key Data Types<\/b><\/td>\n | BF16, INT8, FP32<\/span><\/td>\n | cFP8, BF16, FP32, TF32<\/span><\/td>\n | cFP8, BF16, FP32, TF32<\/span><\/td>\n | FP16, FP8, TruePoint Numerics<\/span><\/td>\n<\/tr>\n\n| Chip-to-Chip Interconnect<\/b><\/td>\n | Inter-Chip Interconnect (ICI)<\/span><\/td>\n | NeuronLink<\/span><\/td>\n | NeuronLink<\/span><\/td>\n | Plesiosynchronous Direct Link<\/span><\/td>\n<\/tr>\n\n| Max Scale<\/b><\/td>\n | 8,960 chips (v5p pod)<\/span><\/td>\n | 100,000+ chips (UltraClusters)<\/span><\/td>\n | 12 chips (per instance)<\/span><\/td>\n | Hundreds of chips<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n <\/p>\n | | | | | | | | |
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