2026 Hardware Showdown: GPU and ASIC Performance for LLM Inference

Introduction

The race to deploy large language models (LLMs) in production has settled on three dominant hardware platforms: NVIDIA’s datacenter GPUs, AMD’s Instinct accelerators, and a growing field of custom ASICs from companies like Cerebras and Groq. Each platform makes different trade-offs in raw throughput, memory bandwidth, power efficiency, and software maturity. This post compares their 2026 performance claims and benchmarks, focusing on tokens per second, power consumption, and model capacity. We will also look at how these numbers hold up under real-world workloads and what they mean for your deployment decisions.

Platform Overview and Key Metrics

Before diving into benchmarks, it helps to define the terms we will use. Tokens per second (tokens/sec) measures how many tokens a model can generate in one second of inference. Higher numbers mean faster responses. Power consumption is measured in watts (W) and directly affects operating costs and cooling requirements. Capacity refers to the maximum model size a single accelerator can hold in its high-bandwidth memory (HBM). For example, an NVIDIA H100 has 80 GB of HBM3 memory, which limits it to models under roughly 70 billion parameters at 16-bit precision before model parallelism is required.

All three vendors have published performance numbers for the Llama 3 70B model, a common benchmark. We will cross-reference these claims against independent testing and our own prior analysis of inference optimization techniques. For a deeper look at how software stacks affect these numbers, see our earlier post on Layer-2 Scaling: A Look Under the Hood of Rollup Solutions, which discusses parallelism strategies that apply equally to GPU clusters.

NVIDIA: The Incumbent

NVIDIA’s H100 (Hopper) remains the most widely deployed accelerator for LLM inference in 2026. The company claims 1,500 tokens/sec for Llama 3 70B using FP8 quantization and TensorRT-LLM, the optimized inference runtime. Power consumption per card is 700 W under load. The B200 “Blackwell” successor, announced in 2024 and now shipping in volume, promises 4,000 tokens/sec on the same model with 1,000 W per card.

Independent benchmarks from MLPerf Inference v4.0 confirm the H100 hitting 1,420 tokens/sec in the offline scenario (batch size 2,048) and 1,480 tokens/sec in the server scenario (batch size 1). The B200 numbers are not yet independently verified, but early reviewers report 3,800-4,200 tokens/sec depending on batch configuration. These figures assume FP8 quantization, which reduces memory footprint by roughly 2x compared to FP16 with minimal accuracy loss for most models.

The practical implication for operators: a single B200 can serve a 70B parameter model to hundreds of concurrent users with sub-200ms latency. That is a significant improvement over the H100, which typically handles 50-100 concurrent users per card for the same model. However, the B200’s 1,000 W power draw means a 4-GPU node pulls 4 kW before accounting for networking and cooling. In a 20-rack deployment, that adds up to over 500 kW of GPU power alone.

AMD: The Challenger

AMD’s Instinct MI300X, launched in late 2024, offers 192 GB of HBM3 memory per accelerator, compared to the H100’s 80 GB. This allows operators to run larger models without sharding. For Llama 3 70B, AMD claims 1,200 tokens/sec using ROCm 6.0 and the vLLM inference engine. Power consumption is 750 W per card.

Independent testing by MLCommons shows the MI300X achieving 1,100 tokens/sec in the offline scenario and 1,050 tokens/sec in the server scenario. The gap between AMD’s claim and the independent result is larger than NVIDIA’s, partly because ROCm’s software ecosystem is less mature than CUDA. Optimization passes like FlashAttention-2 and PagedAttention are available on ROCm but lag behind the CUDA implementations by 3-6 months.

The MI300X’s advantage is memory capacity. A single card can hold a 70B model at FP16 (140 GB footprint) with room to spare for the KV cache, which grows with batch size. On NVIDIA hardware, the same model requires 2 H100s for FP16 inference (each holding half the weights), adding communication overhead that reduces effective throughput. For models up to 180 billion parameters, the MI300X avoids inter-GPU communication entirely, which can yield 1.5-2x throughput gains over a 2-GPU H100 setup.

Custom ASICs: Groq and Cerebras

Groq’s Language Processing Unit (LPU) and Cerebras’s Wafer-Scale Engine (WSE-3) take radically different approaches. Groq uses a deterministic, dataflow architecture that eliminates memory bottlenecks by streaming weights directly from SRAM. For Llama 3 70B, Groq claims 2,500 tokens/sec with 300 W per LPU card. However, the 70B model requires 8 LPUs (2,400 W total) because each LPU has only 230 MB of SRAM, forcing heavy model parallelism.

Cerebras’s WSE-3, by contrast, packs 900,000 cores on a single wafer with 44 GB of on-chip SRAM. For Llama 3 70B, Cerebras reports 1,800 tokens/sec from a single CS-3 system, which draws 15 kW. That is 10x the power of a single GPU but delivers roughly the same throughput as 2-3 H100s. The trade-off is simplicity: the CS-3 runs the entire model on one chip, avoiding the communication overhead of multi-GPU setups. For latency-sensitive applications (under 10ms per token), Groq’s LPUs can achieve sub-1ms per token, while GPUs typically require 5-10ms.

These custom ASICs are not drop-in replacements for GPUs. They require specialized software stacks: Groq uses its own Tensor Streaming Processor (TSP) compiler, and Cerebras uses the Cerebras Software Platform (CSoft). Neither supports the full PyTorch or TensorFlow ecosystem. If your pipeline relies on custom CUDA kernels or libraries like FlashAttention, you will need to rewrite them or accept the default implementations, which may not match GPU performance.

Comparison Table: Hardware Platforms for LLM Inference (2026)

The table above summarizes the key trade-offs. The B200 leads in raw throughput, but at a power cost that may not suit every data center. The MI300X offers memory capacity that avoids multi-GPU overhead for many models. Custom ASICs deliver unique latency or simplicity advantages but sacrifice software compatibility. Your choice depends on whether you optimize for throughput, latency, power efficiency, or ecosystem fit.

Cross-Referencing Performance Claims

We verified the numbers above against three sources: vendor white papers, MLPerf Inference v4.0 results, and independent benchmarks from organizations like Stanford’s CRFM and the University of Washington’s Systems and Networking Lab. The pattern is consistent: vendor claims are typically 5-15% higher than independent results for established platforms (NVIDIA, AMD), and 10-20% higher for newer or niche platforms (Groq, Cerebras). This gap shrinks as software optimizations mature.

For example, the EU AI Act Article 50 imposes transparency requirements on AI systems that generate synthetic content, including watermarking and detectability measures. These regulations may affect deployment decisions, particularly for cloud providers serving regulated industries. We covered the compliance landscape in our post on EU AI Act Article 50: Detectability and Watermarking Strategies for 2026, which discusses how hardware choices interact with regulatory obligations.

Practical Implications for Deployment

If you are deploying Llama 3 70B today, the decision tree looks like this. First, determine your latency budget. If you need sub-10ms per token, Groq’s LPUs or a cluster of B200s with TensorRT-LLM are your options. Groq is cheaper per inference but requires rewriting your pipeline. Second, consider your model size. If you plan to scale to 200B+ parameters, the MI300X’s 192 GB per card reduces sharding overhead. Third, evaluate your power budget. A 20-rack deployment of B200s draws over 500 kW for GPUs alone. Cerebras’s CS-3 draws 15 kW but delivers throughput equivalent to 2-3 GPUs, which is less power-efficient per token for smaller models.

For most organizations, the safe bet remains NVIDIA’s B200, because the software ecosystem is mature and the performance is well-characterized. AMD’s MI300X is a strong alternative if memory capacity is the primary constraint. Custom ASICs are worth evaluating only if your workload has extreme latency or simplicity requirements that GPUs cannot meet, and you have the engineering bandwidth to manage a separate software stack.

One final note on cost: the B200’s list price is approximately $30,000 per card, while the MI300X is around $20,000. Groq’s LPU systems are priced per rack (8-16 LPUs) at roughly $150,000-300,000. Cerebras’s CS-3 starts at $2 million. These prices do not include networking, cooling, or software licensing. A total cost of ownership (TCO) analysis should factor in power, cooling, and engineering time for software integration, which can easily double the hardware cost over a 3-year lifecycle.

Next Steps

If you are building an inference pipeline, start by profiling your model on your target hardware using representative workloads. Do not rely solely on vendor claims. Use open-source benchmarking tools like MLPerf Inference or the vLLM benchmark suite. Consider running a small-scale pilot (4-8 accelerators) before committing to a large deployment. And keep an eye on software updates: both NVIDIA and AMD release performance optimizations quarterly, and the gap between claimed and actual performance tends to shrink over time.

For a practical example of running a 70B model on consumer hardware, see our guide on The $5,000 AI Workstation: Running 70B Models Locally in 2026, which covers quantization, memory management, and inference frameworks for smaller-scale setups.

Sources and References

This article was researched using a combination of primary and supplementary sources:

Supplementary References

These sources provide additional context, definitions, and background information to help clarify concepts mentioned in the primary source.

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