Hardware-Software Co-Design: A Guide to Building Hardware-Friendly LLM Architectures

Introduction: The Three-Dimensional Balance of AI Performance

As large language model (LLM) parameter counts continue to balloon — from GPT-3's 175 billion parameters to today's MoE architectures routinely reaching hundreds of billions or even trillions of parameters, where model weights alone in FP16 precision require hundreds of gigabytes of VRAM, far exceeding the physical capacity of a single GPU — optimizing performance under constrained hardware resources has become a central challenge in AI infrastructure. NVIDIA's developer blog introduced the concept of AI Model Co-Design, which tightly couples model architecture decisions with underlying hardware characteristics, offering a new path that balances accuracy with efficiency.

AI performance is fundamentally a three-dimensional trade-off: Accuracy, Throughput, and Latency. Accuracy measures the quality of model outputs; throughput represents the number of tokens generated per unit of time; latency determines how long users wait for a response. These three dimensions are often in tension — maximizing accuracy may sacrifice throughput, while minimizing latency can hurt batch processing efficiency. Understanding this iron triangle is the logical starting point for hardware-software co-design.

Why Co-Design Matters

The Limitations of Traditional Model Design

For a long time, model researchers and hardware engineers operated in relative silos. Algorithm teams focused on improving accuracy, stacking parameters and deepening network layers on GPU clusters, with little regard for how these design choices affected memory bandwidth utilization or compute unit efficiency. The result: many models that shine on benchmark leaderboards struggle in real-world inference with out-of-memory errors, underutilized compute, or unacceptable latency.

The core idea behind co-design is to break down this barrier — incorporating hardware constraints early in the model architecture design process. Does the attention mechanism's computation pattern align well with GPU Tensor Cores? Can the model's layer structure fully leverage High Bandwidth Memory (HBM)? These questions should not be addressed reactively at deployment time, but planned proactively from the outset.

Principles of Hardware-Friendly Design

A hardware-friendly LLM design typically adheres to the following key principles:

• Compute Density Alignment: Sizing matrix operations to fall within the optimal operating range of GPU compute units, avoiding the overhead of frequently dispatching small operators. This principle has deep hardware roots: the Tensor Cores NVIDIA introduced starting with the Volta architecture deliver nearly 20× the peak throughput of standard CUDA Cores at FP16 precision, but operate efficiently only when matrix dimensions are aligned to multiples of 16 or 8 — directly influencing design choices for hidden layer dimensions, attention head counts, and other hyperparameters;

• Memory Access Optimization: Reducing data movement between VRAM and compute units through proper tiling and data layout strategies. On modern AI hardware, memory bandwidth often becomes the bottleneck before raw compute does. The H100's HBM3 delivers up to 3.35 TB/s of bandwidth, yet during LLM autoregressive decoding, every generated token requires loading the full model weights from VRAM. At small batch sizes, the GPU spends a large fraction of time waiting on data — a phenomenon known as the Memory Wall;

• Native Support for Sparsity and Quantization: Building in adaptability for structured sparsity and low-precision computation (e.g., FP8, INT4) at design time, enabling significant throughput gains with controlled accuracy loss. For example, FP8 cuts data size in half compared to FP16, and H100's FP8 Tensor Cores deliver roughly 2× the throughput of FP16 (approximately 1979 TFLOPS) — though this requires techniques like dynamic activation scaling to keep quantization error in check.

Practical Strategies Under the Three-Dimensional Trade-Off

Holistic Evaluation from Accuracy to Deployment Efficiency

Evaluating an LLM should go beyond benchmark accuracy rankings to comprehensively assess overall performance on target hardware. A model with a marginally higher accuracy score but several times higher inference cost may not be the better choice in production.

This requires developers to build evaluation frameworks centered on metrics like "tokens per dollar" or "inference performance per watt" when selecting models. Through co-design, models can maintain competitive accuracy while delivering several times higher throughput, or compressing latency to levels acceptable for real-time interaction.

Architecture-Level Co-Optimization Strategies

In terms of concrete technical choices, co-design spans everything from attention variants to mixture-of-experts architectures — each innovation directly addressing specific hardware constraints:

The evolution of attention mechanisms is a classic example of memory pressure driving design change. In standard Multi-Head Attention (MHA), the KV Cache grows linearly with sequence length and batch size, making VRAM consumption extremely significant in long-context scenarios. In response, MQA (Multi-Query Attention) has all query heads share a single set of Key-Value pairs to dramatically compress the cache, while GQA (Grouped-Query Attention) groups attention heads to share KV pairs, striking a balance between compression efficiency and expressive capacity. This approach has been adopted by mainstream models including LLaMA 3 and Mistral, and its fundamental motivation is to relieve the HBM bandwidth bottleneck.

MoE architectures use sparse activation to allow models to maintain large parameter counts while activating only a small subset of experts during each forward pass (e.g., Mixtral 8x7B activates 2 experts per token), achieving both parameter efficiency and compute efficiency. However, challenges like load imbalance from expert routing and cross-device expert communication latency require dedicated hardware support — NVIDIA's deep optimization of MoE sparse scheduling in the Blackwell architecture is a concrete embodiment of the co-design philosophy.

These architectural innovations share a common goal: reducing compute and memory overhead without significantly sacrificing accuracy. However, how well they deliver on that promise depends heavily on how well the underlying hardware supports the corresponding computation patterns:

• MoE architectures run efficiently only with hardware deeply optimized for sparse activation and expert routing;
• FP8 training and inference are viable precisely because Tensor Cores natively accelerate low-precision formats — Hopper's Transformer Engine can dynamically switch precision between FP8 and FP16 layer by layer, while Blackwell further introduces FP4 for inference and unifies 72 GPUs into a single addressable compute pool in the GB200 NVL72 rack form factor.

This is the essence of "co-design" — architectural innovation and hardware capability are mutually enabling. Neither can succeed without the other.

Implications for AI Infrastructure

Hardware-software co-design signals that AI engineering is evolving from an "algorithm-first" mindset to system-level optimization. For enterprises and developers, this means three key shifts:

1. Model teams and infrastructure teams need to collaborate earlier and more closely, bringing deployment constraints into the architecture design phase rather than applying fixes after the fact;
2. Break free from leaderboard thinking — when selecting models, evaluate the holistic profile of accuracy, throughput, latency, and cost, replacing single-metric accuracy comparisons with "tokens per dollar" or "inference performance per watt";
3. Proactively embrace hardware-native features like quantization and sparsification, building space for efficient inference at design time rather than treating FP8 quantization and MoE routing optimization as afterthought engineering patches.

As inference cost increasingly becomes a central concern in large-scale LLM deployment, hardware-aware model design is becoming a critical differentiator for the next generation of AI systems. Those who can achieve deeper algorithm-hardware co-optimization will be first to gain the upper hand in the three-way competition between accuracy, throughput, and latency.

Key Takeaways