Dominic Jainy has spent the better part of his career at the intersection of high-performance computing and the rapidly evolving world of autonomous agents. As an IT professional with deep roots in machine learning and blockchain infrastructure, he has witnessed the industry move from simple, static prompts to the complex, multi-turn “agentic” workflows that define the current landscape. Today, we are seeing a seismic shift in how AI is served, moving away from traditional benchmarks and toward systems that can handle massive context windows and frequent tool-calling. Dominic joins us to break down the latest performance data, the widening gap between hardware titans, and why the “software moat” around silicon is becoming the most decisive factor in the race for inference dominance.
The conversation centers on the emergence of new evaluation standards like AgentX, which move beyond theoretical speeds to measure real-world coding sessions and production stacks. We dive into the specific efficiencies of Nvidia’s latest architectures compared to AMD’s offerings, the critical role of high-bandwidth memory hit rates, and why the traditional “tokens per second” metric is becoming increasingly obsolete in a world dominated by long-context sessions and incremental tokenization.
How has the shift toward long-context, multi-turn agent sessions fundamentally changed the way we evaluate hardware performance compared to the old methods of measuring fixed-length prompts?
For the longest time, we were essentially testing race cars on a straight, empty track by feeding them a fixed 8,000-token input and asking for 1,000 tokens back. That just isn’t how the world works anymore in 2026. Today’s traffic is dominated by agentic sessions where a developer might be working through a coding problem over dozens of turns, creating a median input of 142,000 tokens while only needing about 444 tokens in response. This creates a massive imbalance where the system isn’t just “thinking”; it is constantly juggling, recalling, and updating its internal state. When an agent waits roughly 3.84 seconds for a tool to return data, the hardware has to decide what to do with all that expensive “memory state” it just built up. If the hardware can’t efficiently cache that data, you end up wasting incredible amounts of power and time re-processing the same information every single turn, which feels agonizingly slow for the end-user.
Could you walk us through what the AgentX benchmark actually reveals about real-world production traffic, especially considering its foundation on such a massive corpus of 610 billion tokens?
AgentX is a breath of fresh air because it uses a trace replayer rather than a synthetic prompt generator, capturing the actual “messiness” of human-agent interaction. By looking at over 8,000 sessions, including hundreds that spawn sub-agents, we see that modern AI isn’t a single stream of thought but a complex web of prefix relationships. The benchmark uses chained hash blocks of 64 tokens to track how much of a conversation can be reused from the cache versus what needs to be computed from scratch. This reveals a “hidden” reality of the data center: 175 out of every 393 sessions might involve sub-agents triggering even more background tasks. When you look at the sheer scale of 610 billion tokens being replayed against production stacks, you realize that the winner isn’t necessarily the chip with the highest theoretical FLOPS, but the one that handles these intricate, overlapping data dependencies without breaking a sweat.
Nvidia is currently showing cost efficiency leads of up to five times over its closest competitors at high output speeds. What is happening under the hood to create such a massive disparity in these agentic workloads?
It really comes down to how effectively the hardware minimizes “waste” during high-interactivity moments. At an operating point of 150 output tokens per second, Nvidia’s architecture is so efficient that SemiAnalysis suggests you could receive competing accelerators for zero dollars and still end up with a higher total cost per token once you factor in power and hosting. This isn’t just about raw speed; it’s about the 57% to 247% advantage the B200 holds over the MI355X as interactivity increases. When you push for higher tokens per second per user, the “overhead” of managing the session starts to overwhelm less optimized systems. Nvidia’s stack is designed to keep the engine running hot and clean, whereas other systems start to stutter as they struggle to route the next turn of a conversation back to the specific memory block where the previous context is stored.
When we look at memory architecture, specifically comparing the B300 and B200 configurations, how does the interplay between high-bandwidth memory and host DRAM affect the actual user experience?
This is where the “emotional” part of the user experience lives—the difference between a snappy, intelligent partner and a laggy, forgetful one. On a B300 configuration with 3 terabytes of DRAM, we see a staggering 91% hit rate in high-bandwidth memory (HBM), which is essentially the “gold standard” for keeping an agent responsive. Compare that to the B200 at lower concurrency, which falls to a 73% hit rate and has to lean on host memory for 20% of its needs. When a system has to reach out to host memory instead of keeping everything in HBM, the latency spikes, and as a user, you feel that “thinking” pause. It’s the difference between a system that feels like it’s following your every word and one that has to constantly flip through a notebook to remember what you said two minutes ago.
We have seen reports of processing times for conversation history dropping from 185 milliseconds down to just 11 milliseconds thanks to incremental tokenization. What does this breakthrough mean for the future of interactive AI?
Incremental tokenization is one of those “invisible” victories that changes everything for the developer. In the past, if you resent a long conversation history to an AI, the system would re-tokenize the entire thing, which is a massive waste of compute time. By adding boundary-aware incremental tokenization, Nvidia’s TensorRT-LLM can now recognize that it already knows the first 100,000 tokens and only needs to look at the new additions. Dropping that mean processing time per turn from 185.1 milliseconds to 11.3 milliseconds is a nearly 16-fold improvement in efficiency. It makes the conversation feel seamless, and more importantly, it frees up the hardware to handle more users simultaneously without those users ever realizing they are sharing a massive, multi-tenant GPU cluster.
Given that software optimizations can flip a performance ranking in just a week, how should organizations weigh the importance of software velocity against raw silicon specs?
We saw a perfect example of this on August 21st, when a series of upstream commits essentially flipped the leaderboard in a matter of days. This “software velocity” is arguably more important than the fabrication cycle of the chip itself because it determines how much of the silicon’s potential you can actually use. For instance, AMD’s ATOM engine showed some great price-performance wins in very specific scenarios, but because it hasn’t seen wide production adoption outside of a few niche units at Alibaba, most customers are stuck using the standard vLLM stack where the gap remains wide. Organizations need to look at who is merging optimizations into the main codebases. If your hardware is fast but the software community hasn’t optimized the “context-parallel paths” for it, you’re essentially driving a supercar with a speed limiter.
What are the most critical questions a buyer should be asking their cloud or hardware provider today to ensure they aren’t just buying a “tokens-per-second” metric that falls apart during a complex session?
Buyers need to stop looking at the “vendor slide” and start asking about the prefix hit rate at production concurrency. Specifically, ask your provider: “What is the hit rate you can sustain when I have 300 concurrent sessions running at 128,000 tokens each?” You also need to know how much host memory is backing each accelerator—the current recommendation is that the host tier should be 1.5 to 3 times the capacity of the high-bandwidth cache. Finally, ask about session routing. If the provider load-balances your session to a different worker every turn, they are forcing a “re-prefill” every single time, which kills performance. You want a provider that pins the session to the worker holding your prefix, ensuring that the “memory” of your conversation stays right where it needs to be.
What is your forecast for the competitive landscape as we move deeper into the Rubin and MI455X era later this year?
I expect the next several months to be a period of intense “cache-wars.” As we look toward the release of Nvidia’s Rubin generation later this August and the arrival of AMD’s MI455X and new Google TPUs later this year, the battle will move entirely into how we manage massive, persistent context. We are going to see a much heavier emphasis on “context-parallel” paths, where the hardware and software work in lockstep to split a single 200,000-token session across multiple GPUs without losing efficiency. For AMD, the path forward is clear: they have to mature their long-context parallelism and get those ATOM-style gains into the main software branches. If they can close the software gap, the hardware competition will get very interesting, but for now, Nvidia’s ability to innovate “above the silicon” gives them a moat that is very difficult to cross.