LLMs and hardware: NVIDIA on balancing accuracy, throughput, and responsiveness

On July 11, 2026, NVIDIA published an article on co-designing AI models and hardware. Its central thesis: LLM performance depends on three interrelated parameters that cannot be optimized independently of each other.

Three Dimensions of Performance

An AI system cannot be evaluated by a single performance metric. NVIDIA identifies three key dimensions:

• Accuracy — the quality of the model's reasoning and correctness of output
• Throughput — the number of tokens per second the system processes
• Interactivity — response speed for each user (latency from request to first token)

Each parameter is critical for practical application. LLM deployment is a constant balancing of all three simultaneously.

Why Compromise Between Parameters is Inevitable

There is a fundamental tension between the three parameters. High model accuracy loses value if the user must wait a long time for a response. In modern chat services, even a half-second delay is noticeable and frustrating.

Equally paradoxical is enormous throughput (thousands of tokens per second) if each individual user experiences significant wait time between request and response start.

NVIDIA emphasizes: practical systems must simultaneously optimize all three parameters, rather than trying to maximize one at the expense of the others.

"High accuracy is useless with a slow response, and raw throughput

means little if each user's response is slow"

How Model Architecture Affects Performance

NVIDIA's article focuses on how the choice of LLM architecture affects throughput and interactivity. Model design — the number of transformer layers, the magnitude of hidden size, the type of attention mechanism — directly determines how quickly the system processes concurrent requests and how quickly the first response token appears.

A large model with many layers can be more accurate than a small one, but slower. A model with architecture optimized for a specific GPU can serve more users, but incorrect design compromises accuracy.

Co-Design of Model and Hardware

NVIDIA recommends co-design of model architecture and hardware selection. Architects should know in advance the specifics of the target silicon: which mathematical operations are fast on the chosen GPU, which are slow, how to optimally structure computations for minimal latency.

The traditional approach is: a developer creates a model in PyTorch, then engineers attempt to optimize it. NVIDIA proposes a different methodology: design the architecture and hardware selection in parallel, from the start.

This approach requires greater collaboration between disciplines: model architects, optimization engineers, and hardware specialists. The result is significant improvement in all three parameters simultaneously.

What This Means

Co-design of AI models and hardware is becoming an industry standard. The era when architects worked in isolation is ending. Modern LLMs require close collaboration among all stakeholders to achieve optimal balance of accuracy, throughput, and interactivity.

Frequently Asked Questions

How Many Parameters Need to be Optimized Simultaneously?

Three: accuracy of responses, throughput (tokens per second), and interactivity (user response speed).

Why Can't You Maximize One Parameter?

Because it inevitably degrades the other two. High accuracy with slow response time is impractical, as is fast throughput with high user latency.