Why is Inference Efficiency Harder Than It Looks?

Inference efficiency is the total production spend divided by the successful outputs that satisfy user needs. That sounds simple enough, but evolving inference software and hardware make it a nontrivial exercise to determine. The term “tokenomics” has taken on a new meaning to represent the economics of the production and consumption of generative AI, including GPU/TPU depreciation, power and cooling, datacenter real estate, token volume (input + output + hidden reasoning tokens) and everything else involved in running an AI factory.

Innovations in inference are moving so quickly that benchmarks published last month already feel dated. The last 12-18 months have produced major advancements that are changing how we do inference. Agentic AI has been a significant driver of changes in both hardware and software.

Specialized chips have entered the market which have increased the tokens/watt we can achieve.  We’ve also seen aggressive innovations on KV cache management (KV cache is the internal representation of context used by all AI models). These include new memory hierarchies, compression advances, and new embedding models, radically changing how we design production agentic and RAG runtime environments.

New methods require new measurements. The simple “one prompt, one model, one GPU” mental model that we started the AI inference journey with is now obsolete.

2025–2026 Inference Inflection Points

We don’t have to look far back to find significant moments that have set a new path for how we approach AI inference:

• DeepSeek R1 – With the January 2025 release of R1, DeepSeek showed that long internal chain-of-thought reasoning would become the new normal. A query that used to cost ~200 output tokens now routinely generates 10K-20K+ hidden reasoning tokens before the final answer. Every one of those tokens is a full forward pass which means the cost per query jumped dramatically, and unpredictably.
• Inference disaggregation – the two phases of inference, prefill and decode, stress hardware differently. Prefill is compute-bound, whereas decode is memory-bound. By April 2026 when NVIDIA Dynamo hit production, it became common practice to run these two phases on separate GPU pools. This requires that the KV cache generated in prefill can be sent to decode efficiently across the network.
• Context / KV-cache reuse – Agentic workloads repeatedly return to the same context. NVIDIA’s own data showed coding agents re-using 85–97% of cached context after the first call and multi-turn agents hitting 99% cache hit rates on 30k-token prompts. Systems now manage the KV cache as a hierarchy (GPU -> CPU ->local flash -> shared storage) instead of recomputing it.
• Compression and embedding advances – DeepSeek V4 applies novel compression methods to its own KV cache, so a 1-million-token context costs roughly 1/10th the memory. Google’s Gemini Embedding 2 implemented Per-Layer Embeddings, making each layer of the model more efficient. Both techniques allow better models to run on more modest hardware.

To reliably measure inference economics, we need a framework that can predict the business impact of a specific operating pattern, not just report low-level metrics. This framework needs to account for today’s advancements including reasoning depth, prefill/decode disaggregation, KV cache reuse, compression, embedding strategies, and heterogeneous hardware. We also need the framework to adapt to future changes for model architectures, serving patterns, hardware capabilities, and workload behaviors that will change over time.

Modeling a Multivariate Inference Problem

The old way of measuring efficiency was to take one model, feed one prompt on one GPU, and measure tokens/second and TTFT (time-to-first-token). However, modern inference is a distributed, stateful, and multi-tier systems problem with:

• Variable reasoning depth (hidden tokens)
• Prefill vs. decode disaggregation
• KV cache reuse rates and physical location
• Compression applied at different layers
• Mixed workloads with highly variable context reuse patterns

A reliable and realistic cost framework needs to treat inference as an end-to-end system spanning compute, memory, storage, network, and retrieval layers while remaining flexible enough for future unknowns (new chips, new compression tricks, new model architectures).

The Production Reproduction Challenge

Production systems don’t produce clean “warmup then query” benchmarks. They are streams of new requests, repeated requests, short prompts, and long prompts all competing for the same scarce and costly HBM. This is why there are no universal “50× speedup” claims that can map to every workload.

Once KV state leaves HBM, the economics depend on whether your platform can retrieve it faster than it can recompute it. That is the core question for any meaningful efficiency model.

Better Modeling Tokenomics With Inference Efficiency

We are building a practical modeling framework that includes all the variables that impact cost and performance. Among them are reasoning depth, disaggregation, cache hierarchy depth and latency, compression ratios, embedding strategies, how we manage mixed workloads, and evolving hardware innovation.

There is a lot of exciting layers to unpack, so our next posts will dive into:

• How to measure real-world efficiency properly (why warmup/query and recompute baselines are useful but incomplete, plus the power of production-sourced interleaved traces)
• The broad set of variables and equations we need in the model, and why
• Trade-offs and sweet spots across chips, compression techniques, disaggregation strategies, and storage tiers
• Concrete examples of how to apply the framework to size infrastructure, choose serving stacks, and forecast cost and quality of the output

Welcome to the series. Let’s build our understanding, and a model that reflects reality even before you build!