GPU Selection for LLM Inference: A100 vs H100 vs CPU Offloading

Choosing the right hardware for large language model (LLM) inference is no longer just a technical detail; it’s a financial and operational decision that defines your product's responsiveness. You have three main paths: sticking with the reliable NVIDIA A100 is a high-performance GPU based on Ampere architecture launched in 2020, upgrading to the cutting-edge NVIDIA H100 is the latest Hopper-architecture GPU designed specifically for transformer workloads, or saving money by using CPU Offloading is a technique that moves model weights between CPU RAM and GPU memory to handle large models on limited hardware. Each option comes with distinct trade-offs in speed, cost, and complexity.

The Core Problem: Memory Bandwidth Bottlenecks

Before comparing specific chips, you need to understand why LLM inference is so demanding. Unlike training, which is compute-bound, inference is often memory-bandwidth bound. The GPU has to read massive amounts of model weights from its high-speed memory (HBM) for every single token generated. If the memory bandwidth is slow, the powerful cores sit idle waiting for data. This is why the choice between A100 and H100 isn't just about raw processing power-it's about how fast data can move.

Why the H100 Dominates Modern Inference

The H100 wasn't just an incremental upgrade; it was engineered specifically for the transformer architecture that powers modern LLMs. Its biggest advantage lies in the Transformer Engine is a dedicated hardware feature in H100 that dynamically switches precision formats during computation. This engine allows the GPU to use FP8 precision is an 8-bit floating-point format that reduces memory usage and increases throughput without significant accuracy loss for intermediate calculations while maintaining higher precision for critical steps. This dynamic adjustment can deliver up to 6x faster performance for GPT-style transformers compared to older architectures.

In real-world benchmarks, this translates to massive throughput gains. When running the Llama 3.1 70B model using the vLLM inference engine, the H100 SXM5 generates approximately 3,311 tokens per second. In contrast, the A100 manages only 1,148 tokens per second under similar conditions. That’s a 2.8x improvement in raw output speed. For smaller models in the 13B-70B range, the H100 consistently delivers 1.5 to 2x better inference speeds than the A100.

But speed isn't everything-cost matters. While H100 instances historically commanded premium prices, cloud providers dropped rates significantly in early 2025. An AWS user reported paying $1.20/hour for an H100 versus $0.75/hour for an A100. Despite the higher hourly rate, the H100 produced 247 tokens/sec compared to the A100's 112 tokens/sec for Mistral 7B. This makes the H100 roughly 18% cheaper per token generated, flipping the traditional cost-efficiency narrative.

When the A100 Still Makes Sense

Don't dismiss the A100 too quickly. It remains a robust workhorse, especially for organizations with mixed workloads or budget constraints. The A100 benefits from mature tooling; 85% of popular inference frameworks like TensorRT-LLM and DeepSpeed offer out-of-the-box support requiring minimal optimization. Setting up an efficient A100 deployment typically takes 1-3 days, whereas squeezing maximum performance from an H100 can require 2-4 weeks of engineering effort to fully leverage FP8 and the Transformer Engine.

If you are deploying models under 13 billion parameters with low concurrency requirements, the A100 often provides better price/performance. MIT's AI Systems Lab noted in April 2025 that for these lighter workloads, the lower hourly rates and wider availability of A100 instances make them more economical. Additionally, if your infrastructure already relies on A100 clusters, migrating entirely to H100 might not be justified unless you are hitting severe latency bottlenecks.

The Reality of CPU Offloading

CPU Offloading sounds appealing because it lets you run massive 70B+ parameter models on consumer-grade hardware with 64GB of RAM. Techniques like vLLM's PagedAttention or Hugging Face's accelerate library manage swapping model weights between the slower CPU RAM and the faster GPU VRAM. However, this convenience comes at a steep performance cost.

Latency is the killer here. MLPerf benchmarks from late 2024 show that while full GPU inference on an H100 takes 200-500ms per token, CPU offloading jumps to 2-5 seconds per token. For interactive applications like chatbots where users expect sub-second responses, this delay is unacceptable. Stanford University's Efficient LLM Deployment study concluded that CPU offloading introduces "unacceptable latency for production applications requiring sub-second response times." Throughput drops to a mere 1-5 tokens per second even on high-end server CPUs like the AMD EPYC 9654.

Furthermore, implementation is tricky. Developers on GitHub's llama.cpp repository reported spending 5-7 days just to achieve stable performance with 70B models due to complex memory management issues. Documentation quality for CPU offloading tools averages a poor 2.8/5 in clarity surveys, making debugging a nightmare. Reserve CPU offloading strictly for development environments, proof-of-concept demos, or extremely low-volume batch jobs where latency doesn't matter.

Decision Framework: Which Path Should You Take?

To help you choose, consider these specific scenarios:

• High-Concurrency Production (>100 concurrent users): Choose H100. Its superior NVLink interconnect (900 GB/s vs 600 GB/s) and memory bandwidth allow it to handle heavy loads without degrading latency. One financial services engineer reported handling 37 concurrent users on H100 before latency exceeded 2 seconds, compared to only 22 on A100.
• Mixed Workloads & Budget Constraints: Stick with A100. If you are also doing some training or running smaller models, the A100's mature ecosystem and lower entry cost provide better overall value. It maintains strength in environments where workload diversity outweighs pure inference speed.
• Development & Prototyping: Use CPU Offloading. If you are experimenting with new model architectures or don't need real-time responses, offloading allows rapid iteration without expensive cloud bills. Just don't plan to launch this to customers.
• Massive Models (>80GB): Look at H100 NVL. This dual-GPU configuration offers 188GB of combined HBM3 memory, specifically targeting models that exceed the standard 80GB limit of single GPUs. Both standard H100 and A100 struggle here without complex multi-node setups.

Future-Proofing Your Infrastructure

Looking ahead, the gap between H100 and A100 will only widen. McKinsey's AI Infrastructure Report predicts that organizations standardizing on H100 will achieve 3-5 years of relevance, while A100 deployments face obsolescence within 2-3 years as models grow toward 1 trillion parameters. The H100's memory bandwidth is simply necessary for the next generation of larger, more capable models.

Meanwhile, alternatives like AMD's MI300X or Google's TPU v5p are emerging, but they haven't closed the efficiency gap for general-purpose transformer workloads yet. As of mid-2025, H100 dominates 62% of new enterprise LLM inference deployments. Unless you have a very specific reason to avoid NVIDIA's ecosystem, the H100 is currently the safest bet for long-term viability.