GPU Selection for LLM Inference: A100 vs H100 vs CPU Offloading
Choosing the right hardware for Large Language Model (LLM) inference is the process of running trained AI models to generate responses in real-time often feels like trying to pick a sports car without knowing if you’re driving on a highway or a dirt road. You have three main paths: the reliable workhorse, the new speed demon, and the budget-friendly detour. Specifically, you are likely weighing the NVIDIA A100 against the NVIDIA H100, with CPU offloading as your fallback option when budgets tighten.
The decision isn't just about raw power; it's about cost-per-token, latency requirements, and memory bandwidth bottlenecks. By mid-2026, the landscape has shifted dramatically. The H100 is no longer just for deep-pocketed research labs-it’s becoming the standard for production-grade inference due to significant cloud price drops. Meanwhile, the A100 remains a solid choice for smaller models, and CPU offloading is finally viable for low-stakes applications thanks to better software optimization. Let’s break down exactly which path fits your specific use case.
Why Memory Bandwidth Matters More Than Raw Compute
Before comparing specific cards, we need to address the elephant in the room: why do GPUs matter so much for LLMs? Unlike training, where compute power (FLOPS) is king, inference is heavily bottlenecked by memory bandwidth. Think of it this way: the model weights are massive files sitting in VRAM. Every time the model generates a token, it needs to read those weights. If the data can’t move from memory to the processor fast enough, the GPU sits idle, waiting for data.
This is why the architectural differences between the A100 and H100 are critical. The A100 Tensor Core GPU, launched in May 2020 based on Ampere architecture, features 80GB of HBM2e memory with a bandwidth of 2.0 TB/s. It’s a robust machine that served the industry well during the early boom of transformer models. However, the H100 Tensor Core GPU, released in November 2022 using the newer Hopper architecture, upgrades this to 80GB of HBM3 memory with a staggering 3.35 TB/s bandwidth. That is a 67.5% increase in data throughput.
For large models like Llama 3.1 70B, this bandwidth difference translates directly into tokens per second. Higher bandwidth means faster context loading and quicker generation speeds. If you are running models larger than 13 billion parameters, memory bandwidth is usually the single most important spec to look at, not the number of CUDA cores.
NVIDIA A100: The Reliable Workhorse
The A100 is still widely available and often cheaper per hour in cloud environments. For many teams, especially those deploying smaller models (under 13B parameters), the A100 offers excellent price-to-performance ratio. It features 6,912 CUDA cores and third-generation Tensor Cores delivering up to 312 TFLOPS of deep learning performance.
Where does the A100 shine? It excels in mixed-workload environments where you might be running both training jobs and inference requests. According to MIT’s AI Systems Lab analysis from April 2025, for models under 13B parameters with low concurrency requirements, the A100 often provides better overall value due to wider cloud availability and lower hourly rates. Additionally, the tooling ecosystem for A100 is mature. About 85% of popular inference frameworks like vLLM and TensorRT-LLM offer out-of-the-box support, requiring only 1-3 days of optimization time.
However, the A100 has limits. Its 2.0 TB/s bandwidth becomes a choke point when scaling to 70B+ parameter models or handling high-concurrency user loads. If your application requires sub-second response times for hundreds of concurrent users, the A100 will struggle to keep up without significant sharding across multiple nodes, which adds complexity and network overhead.
NVIDIA H100: The Inference Powerhouse
If your primary goal is maximizing throughput for large models, the H100 is currently the gold standard. With 14,592 CUDA cores and fourth-generation Tensor Cores, it delivers up to 4x the performance of the A100’s Tensor Cores. But the real game-changer is the Transformer Engine, which introduces FP8 precision support.
FP8 allows dynamic adjustment between FP8, FP16, and INT8 formats during inference. This optimizes memory usage significantly, allowing you to fit larger models into the same 80GB VRAM or run existing models faster with less memory pressure. Benchmarks from Hyperstack.cloud in April 2025 show the H100 SXM5 generating 3,311 tokens per second for Llama 3.1 70B, compared to the A100’s 1,148 tokens per second-a 2.8x throughput advantage.
Cost-wise, while H100 instances were prohibitively expensive in 2023, prices dropped by approximately 40% in cloud markets throughout 2025. As one developer noted on Reddit in May 2025, the H100 delivered 247 tokens/sec at $1.20/hour versus the A100’s 112 tokens/sec at $0.75/hour on AWS. Despite the higher hourly rate, the H100 was actually 18% cheaper per token generated. For high-volume production apps, this efficiency gain quickly offsets the initial infrastructure costs.
The H100 also features improved NVLink interconnect at 900 GB/s (versus A100’s 600 GB/s), which is crucial for multi-GPU setups. If you need to shard a massive model across several GPUs, the H100’s faster interconnect reduces communication latency between chips, keeping the pipeline full.
CPU Offloading: The Budget-Friendly Compromise
What if you don’t have access to enterprise GPUs? Or what if you are building a prototype and want to minimize costs? CPU offloading is a technique where model weights are stored in system RAM and loaded into GPU memory in chunks during inference allows you to run large models on consumer hardware or standard server CPUs. Tools like vLLM’s PagedAttention and Hugging Face’s accelerate library make this manageable.
However, come with significant trade-offs. Because system RAM (DDR4/DDR5) is vastly slower than GPU VRAM (HBM2e/HBM3), moving data back and forth creates massive latency penalties. MLPerf benchmarks from late 2024 showed inference latency increasing from 200-500ms on an H100 to 2-5 seconds per token with CPU offloading. Throughput drops to just 1-5 tokens per second even on high-end server CPUs like the AMD EPYC 9654.
Is it usable? Yes, but only for specific scenarios. Stanford University’s Efficient LLM Deployment study concluded that CPU offloading remains a necessary compromise for resource-constrained environments but introduces unacceptable latency for production applications requiring sub-second response times. It is ideal for batch processing, offline analytics, or internal tools where users can wait a few seconds for a response. It is terrible for real-time chatbots or customer-facing APIs.
Comparison Table: A100 vs H100 vs CPU Offloading
| Feature | NVIDIA A100 (80GB) | NVIDIA H100 (80GB) | CPU Offloading (e.g., AMD EPYC) |
|---|---|---|---|
| Memory Type | HBM2e | HBM3 | DDR5 System RAM |
| Memory Bandwidth | 2.0 TB/s | 3.35 TB/s | ~100-200 GB/s (varies by config) |
| Tokens/sec (Llama 70B) | ~1,148 | ~3,311 | ~1-5 |
| Latency per Token | Low (<200ms) | Very Low (<100ms) | High (2-5 seconds) |
| Best For | Small/Medium Models, Mixed Workloads | Large Models, High Concurrency | Prototyping, Batch Processing, Low Budget |
| Optimization Effort | Low (1-3 days) | Medium (2-4 weeks for FP8) | High (5-7 days for stable perf) |
Decision Framework: Which Option Fits Your Needs?
To help you choose, consider these three key factors:
- Model Size: If you are running models under 13B parameters, the A100 is often sufficient and more cost-effective. For 70B+ models, the H100’s bandwidth and Transformer Engine provide substantial advantages.
- Concurrency Requirements: How many users will hit your API simultaneously? If you expect >100 concurrent users, the H100 handles load balancing and memory paging far better. For <10 users, CPU offloading might suffice for non-real-time tasks.
- Budget & Timeline: Do you need immediate deployment with minimal engineering effort? A100 has the most mature tooling. Are you willing to invest 2-4 weeks in optimizing FP8 pipelines for long-term savings? Then H100 is the winner. Is budget extremely tight? Start with CPU offloading via llama.cpp or vLLM, but plan to migrate to GPU later.
Future-Proofing Your Infrastructure
Looking ahead, the gap between A100 and H100 will only widen. McKinsey’s AI Infrastructure Report suggests that organizations standardizing on H100 for new deployments will achieve 3-5 years of relevance before the next major architectural shift. A100 deployments face obsolescence within 2-3 years as models grow beyond 1T parameters and require even higher bandwidth. Meanwhile, CPU offloading will remain a niche solution for edge devices and development environments, unlikely to penetrate high-volume enterprise inference.
Keep an eye on emerging alternatives like Google Cloud’s TPU v5p, which offers competitive performance for specific architectures, and AMD’s MI300X, though current benchmarks show it hasn’t yet closed the efficiency gap for transformer workloads. For now, if you are building for scale and speed, the H100 is the clear leader. If you are balancing cost and capability for moderate loads, the A100 remains a dependable choice. And if you are just getting started, CPU offloading lets you experiment without breaking the bank-just don’t expect blazing speed.
Is the NVIDIA H100 worth the extra cost over the A100 for LLM inference?
Yes, for large models (70B+ parameters) and high-concurrency applications, the H100 is often more cost-effective per token despite higher hourly rates. Its 3.35 TB/s memory bandwidth and FP8 precision support deliver 2-3x faster inference speeds, reducing total compute time and operational costs over time.
Can I run a 70B parameter model on a CPU using offloading?
Yes, but with significant latency penalties. Using tools like vLLM or llama.cpp, you can run 70B models on CPUs with 64GB+ RAM. Expect response times of 2-5 seconds per token, making it suitable for batch processing or prototypes but impractical for real-time user interactions.
What is FP8 precision and why does it matter for H100?
FP8 (8-bit floating point) is a data format supported by the H100’s Transformer Engine. It allows dynamic switching between FP8, FP16, and INT8 during inference, optimizing memory usage and computational efficiency. This enables faster processing and lower memory consumption for transformer-based models without significant loss in accuracy.
Which inference engine should I use for CPU offloading?
vLLM and llama.cpp are top choices. vLLM’s PagedAttention efficiently manages memory paging between CPU and GPU, while llama.cpp is highly optimized for CPU-only inference on consumer hardware. Both require some tuning to achieve stable performance for large models.
How does memory bandwidth affect LLM inference speed?
Memory bandwidth determines how quickly model weights can be loaded from VRAM to the processor. Since LLM inference is memory-bound rather than compute-bound, higher bandwidth (like H100’s 3.35 TB/s vs A100’s 2.0 TB/s) directly translates to faster token generation speeds, especially for large models.
