The LLM Inference Wars: A Strategic Analysis of CPU, GPU, and Custom Silicon

Executive Summary: The Great Unbundling of AI Inference

The monolithic, GPU-dominated era of artificial intelligence is fracturing. The “LLM Inference Wars” are not a single battle but a multi-front conflict, signaling a fundamental “unbundling” of the AI stack. The general-purpose, flexible infrastructure that defined the training era, dominated by NVIDIA GPUs, is proving to be architecturally and economically suboptimal for the specialized, high-volume, and latency-sensitive work of inference.

Real-time serving—the instantaneous response a user expects from a chatbot or AI agent—has become the new battleground. The fight is being won and lost based on critical user-experience metrics: Time-to-First-Token (TTFT), which measures perceived responsiveness, and Time-per-Output-Token (TPOT), which defines the speed of generation.

This report analyzes the competing hardware philosophies through the lens of these real-time serving metrics. Three distinct factions have emerged, each with a different strategy to win the market:

The Incumbent and Direct Challengers: NVIDIA, the reigning power, leverages its mature CUDA and TensorRT software stack to create a balanced, high-performance ecosystem. Direct challengers like AMD and Intel are attacking this dominance with “more-for-less” strategies, competing on memory capacity and price-performance.
The Hyperscalers: Google (TPU), Amazon (Inferentia), and Microsoft (Maia) are leveraging their massive scale to pursue vertical integration. They are designing custom silicon, co-designed with their own software, to slash Total Cost of Ownership (TCO) and reduce their strategic dependency on a single vendor for their own large-scale inference workloads.
The Architectural Disruptors: A new class of startups, including Groq (LPU), Cerebras (WSE), and SambaNova (RDU), has abandoned the GPU’s design principles. They offer radical new architectures, each purpose-built to solve a specific, critical inference bottleneck—such as memory latency, model size, or multi-model “agentic” workflows—at a level the incumbent’s general-purpose hardware cannot match.

The analysis concludes that there will be no single “winner.” The future of inference infrastructure is a fragmented, specialized, and “workload-optimized” landscape. This report provides a strategic framework for navigating this new, complex, and rapidly evolving market.

I. The New Physics of AI: Deconstructing Real-Time Serving Performance

The Two-Phase Problem: Why Inference is Not “Training-Lite”

Understanding the hardware war requires deconstructing the two-phase nature of LLM inference. This two-part process is the central reason the market is fracturing, as a chip designed for one phase is often inefficient at the other.

Phase 1: Prefill (Prompt Processing). When a user submits a prompt, the system processes all those tokens in parallel. This phase is computationally intensive, resembling a “training” workload. It is the primary driver of Time-to-First-Token (TTFT). A long, complex prompt, such as one used in a Retrieval-Augmented Generation (RAG) system, can create a substantial compute load, leading to a noticeable delay before the model “starts” responding.1
Phase 2: Decode (Token Generation). After the prompt is processed, the model generates the response one token at a time, auto-regressively. This phase is not compute-bound; it is memory-bandwidth-bound.2 For every single token generated, the system must fetch the entire model’s parameters and the “Key-Value” (KV) cache, which stores the context of the conversation. This phase determines the Time-per-Output-Token (TPOT), or the perceived “speed” of the model’s typing.1

This architectural schism creates the market opportunity. General-purpose GPUs, with their thousands of compute cores, are highly efficient at the parallel prefill phase. However, during the serial decode phase, the vast majority of those cores sit idle, waiting for data to be fetched from off-chip memory (HBM). This profound inefficiency—using a supercomputer for a memory-bound task—is the central weakness that specialized accelerators are built to exploit.

A Lexicon for the Latency Wars

To evaluate these competing architectures, a precise vocabulary of metrics is essential. These metrics define both the user experience and the system’s economic efficiency.4

Time-to-First-Token (TTFT): The time from when a request is sent to when the first output token is received.3 This is the most critical metric for interactive applications like chatbots, as it dictates the user’s perception of “responsiveness”.6
Time-per-Output-Token (TPOT): The average time taken to generate each subsequent token after the first.1 This defines the “speed” and “smoothness” of the streaming response. A low TPOT (e.g., 10 tokens/sec, or 100ms/tok) is crucial to keep up with human reading speed.1
Inter-Token-Latency (ITL): The specific pause between consecutive tokens. The mean of all ITLs for a request is equal to the TPOT, and the terms are often used interchangeably.3
End-to-End Latency (E2EL): The total time from request submission to the reception of the final token.3 This can be calculated as: E2EL = TTFT + (TPOT $\times$ (Total Output Tokens – 1)).1
Throughput (Tokens-per-Second / Requests-per-Second): This measures the aggregate capacity of the server, not the per-user experience.8 Tokens-per-Second (TPS) is the total number of output tokens generated by the server, across all concurrent users, in one second.3

The Fundamental Conflict: The Latency vs. Throughput Trilemma

The most significant challenge in real-time serving is the trade-off between latency (the user experience) and throughput (the system cost).9

Throughput (TPS) is typically increased by batching—processing multiple user requests simultaneously to maximize hardware utilization.8 However, batching is toxic to latency. A larger batch size means individual requests must wait longer to be processed, which directly increases their TTFT and E2EL.9

Real-time applications demand the opposite: low-latency at low batch sizes (often a batch size of 1). This is the most economically inefficient way to run a GPU, as its massive compute resources are left severely underutilized. This conflict creates the TCO (Total Cost of Ownership) crisis for inference: providers must over-provision massive, expensive GPU clusters, running them inefficiently, just to maintain a responsive user experience at peak concurrency.

Table 1: Key Real-Time Inference Metrics Defined

Metric	Acronym	Definition	What It Measures	Key Hardware/Software Factors
Time-to-First-Token	TTFT	Time from request sent to first token received.3	Perceived responsiveness; “How fast did it start?” [5, 6]	Prefill compute, network latency, prompt length, scheduling.
Time-per-Output-Token	TPOT	Average time to generate each token after the first.1	Perceived generation speed; “How fast does it type?” 1	Memory bandwidth (KV cache), batch size, generation length.
Inter-Token-Latency	ITL	The exact pause between two consecutive tokens. Avg(ITL) = TPOT.3	Smoothness of streaming output.	Memory bandwidth, decode compute.
End-to-End Latency	E2EL	Total time from request sent to final token received.3	Total time for a complete, non-streamed answer.	TTFT + (TPOT $\times$ (Num_Tokens – 1)).1
Tokens-per-Second	TPS	Total output tokens generated by the server per second, across all users.3	Aggregate system throughput and cost-efficiency.	Batch size, KV cache efficiency, GPU memory bandwidth.3
Requests-per-Second	RPS	Total requests completed by the server per second.3	General sense of load handling. Varies wildly with prompt/generation length.3	Prompt complexity, optimizations, latency per request.3

The Software Mediator: Why the Inference Engine Matters

Hardware potential is only unlocked by the software stack.9 An optimized inference engine can dramatically improve performance and TCO.

Quantization: Reducing the precision of model weights (e.g., from 16-bit to 8-bit or 4-bit precision) drastically cuts the memory footprint and increases computational speed, often with minimal impact on accuracy.9
KV Caching: A critical optimization that stores the attention keys and values from the prompt, so they are not recomputed for every new token. Efficiently managing this cache is a primary performance bottleneck.9
PagedAttention (vLLM): A breakthrough innovation that manages the KV Cache in non-contiguous memory blocks, similar to virtual memory in an operating system. This dramatically improves memory efficiency, reduces waste, and allows for much higher concurrency.12
Continuous Batching (In-Flight Batching): A dynamic scheduling technique. Instead of waiting for a “static” batch of requests to fill, it continuously adds new requests to the batch as they arrive. This is a core feature of systems like NVIDIA’s Triton Server and Google’s JetStream, vastly improving throughput without the severe latency penalties of static batching.13

A mature software stack (like NVIDIA’s) running on older hardware can often outperform brand-new hardware with a naive or immature software stack. NVIDIA’s dominance is as much about its CUDA and TensorRT ecosystem as its silicon, a “moat” that all competitors must now build.

II. The Reigning Power: NVIDIA’s GPU-CUDA-TensorRT Fortress

Architectural Trajectory: The “More Memory” March

NVIDIA’s hardware evolution demonstrates a clear pivot from a pure-compute focus to addressing the memory-bandwidth bottleneck of inference.

Hopper H100: The 80 GB HBM3 memory and 3.35 TB/s bandwidth set the modern baseline. Its 8-bit floating point (FP8) Tensor Cores are crucial for accelerating transformer operations, which are the building blocks of LLMs.15
Hopper H200: This upgrade is a direct response to the inference bottleneck. By increasing memory to 141 GB of HBM3e (a 1.7x increase) and bandwidth to 4.8 TB/s (a 1.4x increase), the H200 can hold much larger models and longer contexts in memory.15 This directly slashes response times for long-context RAG applications, which are memory-bound.16
Blackwell B200 / GB200: This architecture is NVIDIA’s “all-in” solution. It continues the memory-first trend, expanding to 192 GB of HBM3e and 8 TB/s of bandwidth, while also introducing new, lower-precision data formats (FP4 and MXFP4) to further accelerate compute in the prefill phase.18

The evolution from H100 to H200 reveals NVIDIA’s acknowledgment that inference is a memory-bound problem. Blackwell continues this strategy while also re-accelerating compute, creating a platform designed to offer the best-balanced performance across both the prefill and decode phases.

The Software Moat: TensorRT-LLM and Triton Inference Server

NVIDIA’s true dominance is not just hardware; it is the full-stack, co-designed platform that makes its GPUs manageable at scale.10

NVIDIA TensorRT-LLM: This is an open-source library that acts as an LLM-specific compiler.20 It takes a standard model and rebuilds it into a highly optimized runtime engine, automatically applying kernel fusions, FlashAttention, quantization, and paged KV caching. It is designed to extract the maximum possible performance from the underlying Hopper or Blackwell hardware.13
NVIDIA Triton Inference Server: This is the production-grade serving software that manages the runtime, scheduling, and request queuing.22 Its most critical feature for real-time serving is in-flight batching.13 This technique, also known as continuous batching, dynamically groups incoming requests to maximize GPU utilization (throughput) while minimizing the “wait time” (latency) for any single request.24

This software stack is an incredibly complex layer of schedulers and memory managers designed to tame the non-deterministic, general-purpose nature of a GPU and make it behave like a predictable, efficient inference chip. This complexity is NVIDIA’s moat, but it also creates an opening for competitors whose architectures are inherently simpler and more deterministic.

Cluster-Level Reality: The Hidden Costs of Scaling

In production, LLM serving is a cluster-level problem. Serving a model like Llama 3 70B requires multiple H100 GPUs (e.g., eight H100s in a DGX server) using tensor parallelism to split the model.27 This introduces new bottlenecks:

Multi-Tenancy Interference: To be cost-effective, a cluster must host multiple models on the same hardware (multi-tenancy). This creates a “noisy neighbor” problem, where models compete for shared resources like GPU memory, Streaming Multiprocessors (SMs), and the PCIe bus, leading to unpredictable performance spikes and high tail latency.28
The PCIe Bottleneck: The PCIe bus connecting the host CPU’s main memory (DRAM) to the GPU’s VRAM is a major bottleneck. Serving engines must constantly page inference context (like the KV cache) back and forth, and this transfer is limited by PCIe bandwidth. This is so severe that advanced systems are being developed to bypass the host entirely and page context directly between GPUs over high-speed NVLink.31

This reality is forcing NVIDIA to shift its marketing message. Recognizing it can be beaten on niche benchmarks, its strategy is to sell the TCO-optimized platform. Recent analyses focus on performance-per-watt, cost-per-million-tokens, and “AI factory economics”.18 One such claim is that a $5 million investment in a GB200 system can generate $75 million in token revenue, a 15x return on investment.33 This is a strategic pivot to selling the best-balanced Pareto curve—the optimal trade-off between cost, efficiency, and responsiveness—rather than just the single-fastest chip.18

III. The GPU Counter-Offensive: AMD and Intel’s Assault on the Baseline

NVIDIA’s dominance has invited a powerful counter-offensive from its traditional rivals, who are attacking its baseline GPU business on two fronts: memory and price.

AMD’s Memory Gambit: The MI300X

AMD’s MI300X accelerator attacks NVIDIA’s H100 on its most vulnerable point: memory capacity.

The Core Advantage: The MI300X’s primary weapon is its 192 GB of HBM3 memory and 5.3 TB/s of bandwidth.34
The “Simplicity” Play: This massive, unified memory pool allows a large model like Llama 3 70B (which requires ~141 GB in 16-bit precision) to fit on a single MI300X.35 To run the same model on 80 GB H100s, an operator must use two chips and implement complex, operationally-intensive tensor parallelism.35
TCO via Simplicity: The MI300X’s value proposition is not just performance, but a drastic reduction in operational complexity. A single-GPU, multi-user system is vastly simpler to deploy, manage, and scale than a multi-GPU, single-user system. This translates directly to a lower TCO.34 Independent benchmarks show the MI300X is highly competitive, often outperforming the H100 in both throughput and TTFT.35

The Software War: Can AMD’s ROCm Break the CUDA Moat?

AMD’s hardware is competitive, but the real war is software. NVIDIA’s counter-argument has been that its hardware is 2x faster when benchmarked properly with TensorRT-LLM, a flex of its software moat.27

AMD’s response is ROCm (Radeon Open Compute), its open-source “CUDA alternative.” Historically, ROCm’s immaturity has been its greatest weakness. However, AMD is now aggressively pushing a “developer-first” strategy with ROCm 7.0.37 This new push promises “Day-0” support for new models (like Llama 4 and Gemma 3), full integration of key open-source frameworks like vLLM, JAX, and Triton, and new native inference engines (“DeepEP”).38 AMD’s success in the inference market hinges almost entirely on its ability to make ROCm a “drop-in,” bug-free, and high-performance alternative to the CUDA ecosystem.

Intel’s Two-Pronged Attack: Gaudi 3 and the Re-emergence of the CPU

Intel is fighting a two-front war, attacking the high-end accelerator market with Gaudi 3 and the low-end/niche market with its Xeon CPUs.

The Gaudi 3 Accelerator: Gaudi 3 is not designed to be the fastest chip, but the best value. Its strategy is pure price-performance.

The “Price-Performance” Play: Analysis shows Gaudi 3 has comparable performance to the H100 (ranging from 15% lower to 30% higher depending on the task), but a significantly better price-performance ratio.41
Ideal Use Case: Gaudi 3’s architecture excels in workloads with small inputs and large outputs.41 This makes it a strong, cost-effective contender for summarization, translation, and long-form content generation.
Benchmarks: Intel’s data shows an 8-chip Gaudi 3 system delivering over 21,000 aggregate tokens-per-second on Llama 3.1 70B, and a single chip outperforming H100/H200 on Llama 8B in Queries-per-Second (QPS).42

The Viable CPU (Xeon vs. EPYC): For small models (e.g., Llama 3 8B) or non-latency-critical tasks, the CPU is a surprisingly viable and cost-effective solution.

The Hardware: The key is Intel’s Advanced Matrix Extensions (AMX), a built-in tensor accelerator in its Xeon processors.44
The Matchup: Intel claims its Xeon 6 CPUs with AMX deliver 1.4x higher performance than AMD’s EPYC 9755 for vLLM inference.45 AMD counters, claiming its EPYC 9654 system has a 1.27x better performance-per-dollar ratio on Llama2-7B.46
The “Right-Sizing” Option: The market often overlooks CPUs, but for multi-tenant hosts running many small, specialized models, a “right-sized” CPU solution can be the most TCO-efficient, avoiding the high cost and power draw of a dedicated accelerator. The choice often comes down to Intel’s AMX for native compute 44 versus AMD’s high PCIe lane counts (128 per socket) for multi-GPU setups.47

Table 2: The General-Purpose Accelerator Showdown

Accelerator	Memory Size	Memory Type	Memory Bandwidth	Key Architectural Features	Core Software Stack
NVIDIA B200	192 GB	HBM3e	8.0 TB/s	Blackwell Arch., FP4/MXFP4, 5th-gen NVLink 18	CUDA / TensorRT-LLM / Triton
NVIDIA H200	141 GB	HBM3e	4.8 TB/s	Hopper Arch., FP8, 4th-gen NVLink 15	CUDA / TensorRT-LLM / Triton
NVIDIA H100	80 GB	HBM3	3.35 TB/s	Hopper Arch., FP8, 4th-gen NVLink 15	CUDA / TensorRT-LLM / Triton
AMD MI300X	192 GB	HBM3	5.3 TB/s	CDNA 3 Arch., 192GB simplifies deployment 34	ROCm 7.0 / DeepEP
Intel Gaudi 3	128 GB	HBM2e	3.7 TB/s	64 Tensor Cores, 8 EUs, TPC-based arch. [48]	Intel Gaudi Software / Optimum-Habana

IV. The Hyperscaler ‘In-House’ Revolution: The Strategic Pivot to Custom Silicon

The Core Strategy: Vertical Integration and TCO

The most significant long-term threat to NVIDIA’s dominance comes from its largest customers: Google, Amazon, and Microsoft. These hyperscalers are aggressively pursuing vertical integration by building their own custom AI silicon.49

The strategy is twofold. First, it is a defensive move to reduce vendor dependency and control their massive TCO for inference, which is a high-volume, cost-sensitive, and architecturally stable workload.51 Second, it allows them to co-design hardware that is perfectly optimized for their own software stacks and flagship products (like Google Search, Alexa, or Microsoft Copilot).

This is leading to a clear split in spending. Hyperscalers are still buying NVIDIA’s best-in-class chips (like the B200) for the R&D-heavy, flexible workload of training. But for the predictable, at-scale workload of inference, they are increasingly shifting that spend to their own internal, custom-built, and TCO-optimized ASICs.52

Google’s System-Level Architecture: TPU Pods and JetStream

Google’s strategy is the most mature, viewing the pod as the product, not the chip.

TPU v5e vs. v5p: Google offers two tiers. The v5p is the top-tier performance chip.53 The v5e is the “cost-efficient” chip, offering “2.3X price-performance improvements” over the TPU v4 54 and delivering up to “3x more inferences per dollar”.14
System-Level Co-Design: Google’s true advantage is its Optical Circuit Switching (OCS), an ultra-high-bandwidth, reconfigurable interconnect.55 This allows Google to connect up to 8,960 TPU v5p chips into a single, massive supercomputer, co-designed to train and serve its largest models like Gemini.54
The Software Stack: The entire platform is vertically integrated. Models are built in JAX (a NumPy-like library) 57, compiled by OpenXLA (an open-source ML compiler) 58, and served by JetStream.14 JetStream is Google’s equivalent of Triton, a dedicated inference engine that applies optimizations like continuous batching, sliding window attention, and int8 quantization, all co-designed specifically for the TPU architecture.14

Amazon’s TCO King: AWS Inferentia

Amazon’s strategy is to provide the lowest-cost inference within its own cloud ecosystem, bifurcating its silicon into Trainium (for training) and Inferentia (for inference).60

Purpose-Built for Inference: The AWS Inferentia 2 (Inf2) accelerator is designed with one goal: “high performance with lowest cost for generative AI inference”.61
The Hardware: Inf2 instances feature dedicated NeuronCores-v2 and a high-speed NeuronLink interconnect. This allows a single Inf2 instance to serve models as large as 175 billion parameters, providing a low-latency, high-throughput, and low-cost alternative to GPU instances.61
The Software: The “cost of entry” is the AWS Neuron SDK.62 This compiler is the mandatory software layer that optimizes models from frameworks like PyTorch and TensorFlow to run efficiently on the proprietary Inferentia hardware.

Microsoft’s Co-Design: Azure Maia

Microsoft’s strategy is the clearest example of a company building an “appliance” for its own internal needs.

The “Copilot” Chip: The Azure Maia 100 accelerator was “informed by Microsoft’s experience in running…Microsoft Copilot”.63 It is a chip co-designed from the ground up to slash the (presumably astronomical) TCO of Microsoft’s flagship generative AI products.
System-Level Optimization: Microsoft’s approach is a holistic, end-to-end stack optimization. This includes the Maia 100 silicon, the custom server, a dedicated “sidekick” for rack-level liquid cooling, and a custom Ethernet-based networking protocol.63
The Internal Customer: Unlike Google and Amazon, Microsoft is not (yet) aggressively positioning Maia as a direct competitor for external customer workloads. Its primary goal is to secure its own supply chain, margins, and performance for its internal services.65

Table 3: Hyperscaler Custom Silicon Architecture Comparison

Platform	Accelerator(s)	Stated Goal / Strategy	Key Architectural Differentiator	Core Software Stack
Google Cloud	TPU v5p (Performance) 54, TPU v5e (Cost) 14	Massive-scale models (Gemini); Best price-performance (v5e)	Optical Circuit Switching (OCS) for 8,960-chip pods [55, 56]	JAX / PyTorch/XLA / JetStream 14
Amazon AWS	Inferentia 2 (Inference) [66], Trainium (Training)	Lowest cost-per-inference on AWS 61	NeuronLink interconnect for distributed inference 61	AWS Neuron SDK / SageMaker [61, 62]
Microsoft Azure	Maia 100 (Inference) 63, Cobalt (CPU)	Vertically integrated, co-designed for internal workloads (Copilot) [64]	Full-stack co-design: chip, server, networking, liquid cooling 63	(Internal) Azure AI Stack

V. The New Architects: Radical Solutions for a Post-GPU World

This final faction of competitors is not trying to build a “better GPU.” They are building fundamentally different architectures, each designed to solve one specific, critical bottleneck of the GPU’s design.

Groq’s LPU: The “Instant” Conversational AI

Groq’s LPU (Language Processing Unit) is an architecture built to solve one problem: the memory latency of the decode phase.

The Architecture: The LPU is a deterministic, compiler-driven, SRAM-based chip.67 It completely removes off-chip HBM. Instead, model weights are stored in hundreds of megabytes of on-chip SRAM.67
The Advantage: This on-chip SRAM provides memory bandwidth upwards of 80 TB/s, compared to the ~4.8 TB/s of an H200’s HBM.2 More importantly, the Groq compiler maps the entire LLM dataflow statically before runtime.2 This eliminates the need for a runtime scheduler and removes all resource contention. The result is a system with zero tail latency—it executes a task in the exact same amount of time, every time.68
The Performance: This architecture delivers unmatched TPOT, making it the ideal solution for real-time conversational agents.69 Benchmarks show it running Llama 3 70B at 284 tokens/sec and Llama 2 70B at 241 tokens/sec, performance so high it forced benchmark charts to be resized.70
The Trade-off: SRAM is expensive and space-intensive, limiting the size of the model that can fit on a single chip, though this is a challenge the 14nm-based architecture plans to address with newer process nodes.2

Cerebras’s CS-3: The Wafer-Scale “Monster Model” Server

Cerebras’s WSE-3 (Wafer-Scale Engine) is a single “chip” the size of a dinner plate, containing 4 trillion transistors. It is built to solve the inter-chip communication bottleneck.

The Architecture: A 400B+ parameter model (like Llama 3.1 405B) requires a cluster of over 100 GPUs, all communicating over a (relatively) slow network. This inter-GPU communication is the latency bottleneck.74
The Advantage: The Cerebras CS-3 can fit the entire 405B model on its single wafer, which provides 1.2 TB of on-wafer memory and 21 PB/s (petabytes per second) of memory bandwidth.75 All “communication” is on-silicon.
The Performance: This unique architecture allows it to serve the Llama 3.1 405B model at a staggering 969 tokens/sec with a 240ms TTFT.74 It makes serving massive, multi-trillion-parameter models 75 not just possible, but real-time.
The Trade-off: This is an exotic, “all-or-nothing” system with unique power, cooling, and cost challenges.76 It is inflexible by design.

SambaNova’s RDU: The “Agentic Workflow” Engine

SambaNova’s SN40L RDU (Reconfigurable Dataflow Unit) is a dataflow processor built to solve the multi-model switching bottleneck.

The Architecture: The RDU is designed for a future of “agentic workflows” 77 and “Composition of Experts” (CoE).78 This is where a single query is routed to multiple small, specialized expert models, rather than one large monolithic model.79
The Bottleneck: On a GPU, this CoE approach is a TCO nightmare. You must either keep all models in VRAM (prohibitively expensive) or constantly swap them from host memory (prohibitively slow).79
The Advantage: The SN40L features a unique three-tier memory system (SRAM, HBM, and DDR).81

DDR (large, slow): Holds hundreds of expert models.82
HBM (medium, fast): Caches the active models.
SRAM (small, instant): Used for the computation.

The Performance: This architecture allows the RDU to switch between models in microseconds 83, making it the ideal platform for CoE and agentic systems.84 It can serve a 671B model on a single rack 85 and outperforms GPU clusters by 3.7x to 10x+ on these specific multi-model workloads.78

Table 4: Disruptor Architecture Comparison

Platform	Core Architecture	Primary Bottleneck Solved	Key Specs	Ideal Use Case
Groq LPU	Deterministic, SRAM-based 67	Memory Latency (Decode)	~80 TB/s on-chip SRAM bandwidth 2	Ultra-low-latency conversational AI.[69]
Cerebras CS-3	Wafer-Scale Engine (WSE) 75	Inter-Chip Communication (Model Size)	4T transistors, 21 PB/s bandwidth, 1.2TB on-wafer memory 75	Real-time serving of massive 400B-24T+ parameter models.[69]
SambaNova RDU	Reconfigurable Dataflow Unit [81]	Multi-Model Switching / CoE	3-tier memory (SRAM/HBM/DDR) 82	Agentic workflows, Composition of Experts (CoE).[69, 85]

VI. Strategic Synthesis: Matching the Infrastructure to the Application

Comparative TCO: Beyond Benchmarks

The “best” hardware is simply the one with the lowest Total Cost of Ownership (TCO) for a specific workload. The ultimate business metric is cost-per-million-tokens. This cost is a function of hardware price, power (performance-per-watt) 18, and aggregate throughput.

This cost is in freefall. Google claims a price of $0.30 per 1M output tokens for its cost-efficient TPU v5e.87 NVIDIA, defending its new platform, claims its B200 software optimizations can achieve “two cents per million tokens”.33

This TCO must be calculated differently for each use case:

Real-Time Chatbot: TCO is driven by per-user latency at high concurrency.
RAG / Long-Context: TCO is driven by prefill (TTFT) performance.
Agentic AI: TCO is driven by model-switching speed.
Offline Batch: TCO is driven by pure throughput-per-dollar.

Table 5: Real-Time Inference Benchmark Synthesis

Disclaimer: The following benchmarks are compiled from diverse sources, each with its own methodology, model versions, and configurations. They are intended for directional comparison, not as a direct, apple-to-apples performance claim.

Platform	Model	Throughput (tokens/sec/user)	TTFT (ms)	Source / Context
Groq LPU	Llama 3 70B	~284 t/s	~300 ms	[70] (ArtificialAnalysis benchmark)
NVIDIA DGX H100	Llama 2 70B	~5.8 t/s (Batch 1)	~1700 ms (Batch 1)	27 (NVIDIA data, single-request latency)
NVIDIA DGX H100	Llama 2 70B	~32 t/s (at 1200ms E2E Latency)	(N/A)	27 (NVIDIA data, latency-constrained throughput)
Intel Gaudi 3 (8-chip)	Llama 3.1 70B	~2,658 t/s (Aggregate)	(N/A)	[42] (Total throughput, 128/2048 seq, BS=768)
SambaNova (16-chip)	DeepSeek 671B	~250 t/s	(N/A)	[86] (Per-user throughput on a much larger model)
Cerebras CS-3	Llama 3.1 405B	~969 t/s	~240 ms	74 (On a model 5.7x larger)

The Decision Framework: Choosing the Right Accelerator

The strategic decision is no longer just “which chip?” It is “which platform and what level of flexibility?” The market has fractured into three distinct business models:

The “DIY” Platform (NVIDIA, AMD): Provides best-in-class, general-purpose components (GPUs, networking) that enterprises must assemble.

Pros: Maximum flexibility, supports training and inference, best for R&D.
Cons: Highest operational complexity. Requires a world-class engineering team to manage the complex software stack (TensorRT, ROCm) and multi-tenancy challenges.10

The “Walled Garden” Platform (Google, AWS): Offers a vertically-integrated, co-designed, and cost-optimized stack (e.g., TPU + JetStream, Inferentia + Neuron).

Pros: Potentially the lowest TCO for “good enough” performance 14, with zero hardware management.
Cons: Total vendor lock-in. Models must be compiled via proprietary, platform-specific software (XLA, Neuron SDK).

The “Purpose-Built Appliance” (Groq, Cerebras, SambaNova): Sells a full-stack, non-flexible “appliance” that solves one problem 10-100x better than anyone else.

Pros: Unmatched performance on its specific niche (latency, model size, or agentic workflows).71
Cons: Zero flexibility. An operator cannot (or should not) train on a Groq LPU or run a simple chatbot on a SambaNova RDU.

Final Recommendations: The Future of Inference

The key takeaway is that the inference market is fragmenting for good reason. The architectural needs of a real-time chatbot, a massive reasoning model, a multi-model agent, and a general-purpose API are all fundamentally different.

The optimal strategy for a large enterprise will be a hybrid one, matching the hardware to the job.

NVIDIA (B200/H200) clusters will remain the “default” for flexible R&D, training, and general-purpose inference backends where model diversity and flexibility are key.
Hyperscaler Silicon (TPU v5e, Inferentia 2) will be the TCO-driven choice for high-volume, cost-sensitive, “solved” workloads.
Groq LPUs will be deployed for flagship, latency-critical conversational products where a premium user experience is the top priority.
SambaNova RDUs will be the platform for emerging agentic products that rely on Composition of Experts.
Cerebras will power cutting-edge, massive-model (400B+) applications that are impossible to serve in real-time on any other system.

The “LLM Inference Wars” will not have a single winner. The ultimate winner may not be a hardware company at all, but the software company that builds the “Triton-for-Heterogeneous-AI”—a universal abstraction layer that can intelligently route any inference request to the most TCO-efficient hardware (GPU, LPU, TPU) in the cluster in real-time.

Table 6: Strategic Decision Matrix (Workload vs. Optimal Hardware)

Workload	Primary Metric	Optimal Platform(s)	Justification
Real-Time Chatbot / Conversational AI	TPOT (Latency)	Groq LPU	Unmatched low-latency and deterministic TPOT via SRAM-based architecture.[2, 68] Solves the decode bottleneck.
Long-Context RAG (e.g., “Chat with PDF”)	TTFT (Latency)	NVIDIA H200/B200, AMD MI300X	High-memory and high-bandwidth HBM is ideal for the compute-heavy prefill on long contexts.[16, 35]
Serving Massive Models (400B+)	Model Size	Cerebras CS-3	Only platform that fits the entire model on-silicon, eliminating the inter-chip communication bottleneck.74
Multi-Model / Agentic Workflows	Switching Speed	SambaNova RDU	Purpose-built 3-tier memory (SRAM/HBM/DDR) allows microsecond model switching for Composition of Experts (CoE).[83, 84]
General-Purpose API (e.g., OpenAI API)	Balanced TCO	NVIDIA B200 Cluster	Best balance of performance, flexibility, and mature software (TensorRT-LLM) for a wide, unpredictable range of models.18
High-Volume, Low-Cost (Batch)	Cost/Token	Google TPU v5e, AWS Inferentia 2	Hyperscaler silicon is “purpose-built” and co-designed to deliver the lowest possible TCO for high-volume, “solved” workloads.[14, 61]
Small Model / Edge Inference	Cost/Core	Intel Xeon (AMX), AMD EPYC	For “right-sized” tasks, on-chip accelerators (AMX) or high core counts can be the most TCO-efficient solution.[44, 45]

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