AI Inference Hardware Guide
AI inference hardware has exploded beyond GPUs into a landscape of TPUs, custom inference chips, and specialized accelerators. This guide maps the options and shows how to compare them on the specs that actually determine inference performance.
The categories of inference hardware#
Datacenter GPUs — NVIDIA (H100, H200, B200, GB200), AMD (MI300X, MI325X). The default for almost all LLM inference thanks to mature software and high memory bandwidth.
TPUs — Google's Tensor Processing Units, available on Google Cloud. Strong for both training and inference, especially with JAX-based stacks.
Dedicated inference chips — Groq LPUs, Cerebras, AWS Inferentia, SambaNova. Purpose-built for inference, often optimizing for ultra-low latency or cost-per-token on specific model classes.
Workstation / local GPUs — RTX 4090/5090, L4, L40S. For development, small models, or budget deployments.
The specs that matter#
When comparing any inference accelerator, three numbers dominate:
| Spec | What it limits | Why it matters |
|---|---|---|
| Memory capacity (GB) | Largest model you can load | Hard ceiling — weights + KV cache must fit |
| Memory bandwidth (TB/s) | Token generation speed (decode) | The real bottleneck for most LLM serving |
| Compute (FLOPS) | Prompt processing speed (prefill) | Matters for long prompts, embeddings, image gen |
For LLM inference specifically, memory bandwidth is usually the binding constraint — decode reads the full model from memory for every token generated. A chip with massive FLOPS but mediocre bandwidth will underperform on chat-style workloads.
During autoregressive decode, the GPU re-reads all model weights from VRAM to produce each token. The arithmetic per byte read is low, so the memory system — not the compute units — sets the pace. This is the single most important fact in inference hardware selection.
Comparing NVIDIA's datacenter lineup#
The interactive tool above lets you compare memory, bandwidth, and compute across NVIDIA generations. A few rules of thumb:
- H100 — the workhorse. Great all-rounder for 7B-70B models.
- H200 — same compute as H100 but far more memory (141 GB) and bandwidth. Better for large models and decode-heavy workloads.
- B200 / GB200 (Blackwell) — next-gen, dramatically higher memory and bandwidth for frontier models and large MoE.
- A100 — previous generation, still cost-effective for many workloads.
- L4 / L40S — economical for small models and budget deployments.
Training vs inference hardware#
A common confusion: the best training chip isn't necessarily the best inference chip.
- Training demands maximum sustained compute, high-precision arithmetic, and fast interconnect across many chips for long runs.
- Inference prioritizes memory bandwidth (for decode), low latency, cost-efficiency, and often runs at lower precision (FP8/INT8/INT4).
This is why dedicated inference chips exist — they drop training-oriented features to optimize for serving economics.
Dedicated inference chips: worth it?#
Chips like Groq (ultra-low-latency token streaming) or AWS Inferentia (cost-optimized) can beat GPUs on specific metrics for specific models. But GPUs remain the most flexible and best-supported option, with the broadest model coverage and software ecosystem.
You have a stable, high-volume workload where a specific latency target or cost-per-token would justify the reduced flexibility and smaller software ecosystem. For most teams, GPUs are still the pragmatic default.
Choosing your hardware#
The decision comes down to: model size (does it fit in memory?), workload shape (decode- or prefill-heavy?), latency requirements, and budget.
Try it: Model-to-Hardware Recommender →
Input your model and SLO to get ranked hardware configurations with reasoning.
Key Takeaway
For LLM inference, rank accelerators by memory capacity (can the model fit?) and memory bandwidth (how fast can it decode?) before compute. GPUs are the flexible default; TPUs and dedicated inference chips can win on specific cost or latency profiles.
Frequently asked questions
What hardware is best for AI inference?
For most LLM inference, NVIDIA datacenter GPUs (H100, H200, B200) are the default due to mature software and high memory bandwidth. AMD MI300-series and Google TPUs are competitive alternatives. Dedicated inference chips (Groq, Cerebras, AWS Inferentia) can win on specific latency or cost profiles.
What's the difference between training and inference hardware?
Training needs maximum compute and high-precision arithmetic across many GPUs for long runs. Inference prioritizes memory bandwidth (for decode), low latency, and cost-efficiency, and often uses lower precision (FP8/INT8/INT4). Some chips are inference-only.
Are dedicated inference chips better than GPUs?
Sometimes. Dedicated inference silicon can deliver lower latency or better cost-per-token for specific models and workloads, but GPUs remain the most flexible and best-supported option. The right choice depends on your model, latency SLO, and scale.
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