In technology, numbers have always been more than numbers. They are signals — of performance, of design, of trustworthiness. In networking, we looked at throughput, jitter, and packet loss not as sterile statistics, but as proof points of whether a circuit was usable. In storage, IOPS and latency weren’t just benchmarks; they were contracts between infrastructure and workloads.

Now, in the age of AI-native infrastructure, the same applies. Numbers tell us stories. They tell us how far we can push a system, how consistently it performs, and whether users will trust it to serve them when it matters.

Recently, I put this lens on two very different AI stacks: our on-prem gpt-oss:120B cluster and the cloud-based gpt-4o-mini from OpenAI. Both models were tested under controlled benchmarks using a matrix runner I designed, which explored three realistic scenarios:

• Short/Short: minimal prompt, short output.
• Long/Short: large prompt, short output.
• Long/Long: large prompt, longer output.

The methodology was simple: sweep concurrency (4 → 100), fix bursts at 80, and measure not just raw throughput but also latency distributions (p50, p95, p99), tokens in/out, and error taxonomies. The goal wasn’t to crown a “winner.” It was to understand: What happens to trust when we push these systems under load?

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The Shift:

For decades, stress testing meant one thing: push until the system breaks, then write down the maximum throughput. Network engineers did it with line cards. Database administrators did it with OLTP workloads. System architects did it with web servers. The mindset was: find the red line, avoid crossing it, and declare victory.

But AI isn’t like a switch or a database. AI is interactive. People type a question, they wait, and they expect an answer. No one cares that your system can theoretically sustain 500 requests per second if, in practice, their single request hangs for 20 seconds. Stress testing for “maximums” made sense in the old paradigm. In the AI paradigm, it misses the point.

The shift is from stress numbers to trust numbers. From “what’s the max throughput?” to “at what load does latency stay within what people find acceptable?” That’s the mindset change we need if we’re to operate AI as enterprise-grade infrastructure.

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The Old Way:

Let’s be honest: the old way was comforting. Running a load generator, watching the RPS climb, and then recording the moment it flattened felt like a rite of passage. You could plot it on a chart, take it to management, and say: “This is our capacity.”

But this approach had two flaws. First, users rarely drive systems to maximum load. Most of the time, they operate in a steady state with bursts. Second, maximum throughput often comes at the expense of latency. You can keep feeding requests into the pipeline, but if each request waits in line for 20 seconds, what have you achieved? A number, yes. Trust, no.

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The New Reality:

When I ran the benchmarks, the story was crystal clear.

On gpt-oss:120B, the token throughput ceiling was around 90 tokens/sec. It didn’t matter if I ran 4 concurrent users or 60. The ceiling was absolute. As concurrency rose, throughput stayed flat, but p95 latency climbed: 2 seconds at light load, 5–8 seconds at moderate load, and over 20 seconds at high load. It was decode-bound. Stable, predictable — but capped.

On gpt-4o-mini, the shape was entirely different. Concurrency scaled nearly linearly. At 20 concurrent users, it pushed 210 tokens/sec. At 40, it broke past 360. At 80, it reached 440 tokens/sec. And p95 latency? Steady at ~1.1s almost the entire way. The system was elastic, highly optimized, and engineered for predictability at scale.

Neither story was surprising. On-prem systems, even large models like gpt-oss:120B, are constrained by GPU decode bandwidth. Cloud systems hide those limits with massive parallelism and clever batching. But seeing the numbers side by side reframed the conversation.

It wasn’t about which is faster. It was about what kind of trust each one builds.

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The Architecture:

This is where the analysis matters. The matrix runner didn’t just give me RPS lines; it told me exactly how to operate these systems in production.

For gpt-oss:120B, the conclusion was simple:

• Concurrency 8 → sweet spot for SLO-A (p95 ≤ 5s). RPS ~2.4 per instance, stable latency, predictable UX.
• Concurrency 12–20 → SLO-B (p95 ≤ 8s). Slightly more throughput, but tails stretch longer.
• Beyond 24 → pointless. Throughput doesn’t improve, latency explodes.

This means each replica of gpt-oss:120B delivers ~90 t/s. Need more seats? Scale horizontally. Deploy more replicas, distribute load, enforce concurrency caps.

For gpt-4o-mini, the story was about elasticity. It scaled well into high concurrency, delivering predictable sub-2s latency up to 80+ users. That’s the power of architectural maturity: optimized schedulers, batching, and token pipelines.

To make this actionable, we distilled the findings into a Capacity Playbook:

Figure: gpt-oss:120B Capacity Playbook — defining concurrency guardrails, scaling triggers, runtime optimizations, and operational SOPs for predictable AI performance.

This playbook formalizes what the benchmarks taught us:

• Set per-instance guardrails (conc=8, reject beyond wait budgets).
• Scale out linearly when load persists.
• Invest in runtime optimizations (Flash-Attention v2, paged KV, BF16, TensorRT-LLM schedulers).
• Monitor the right KPIs: p95 latency, TPS_out, queue depth, batch efficiency, GPU utilization.
• Operate with discipline: weekly load sweeps, daily dashboards, enforce queue budgets.

These aren’t theoretical. They are the direct architectural consequences of the benchmark numbers.

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The AI/Trust Layer:

Here’s the deeper truth: users don’t care about throughput. They care about consistency. A chatbot that answers in 2 seconds every time earns trust. A system that answers in 1 second sometimes but 22 seconds at others does not.

That’s why trust is built not at the maximum, but at the median and the 95th percentile. People notice outliers. They don’t forgive unpredictability.

In this light, the comparison is profound. gpt-oss:120B is consistent within its limits. It can’t scale past ~90 t/s, but within concurrency 8–12, it delivers steady performance. gpt-4o-mini is consistent across wide scales. It delivers elasticity, with predictably low latency, even under heavy load.

Both build trust — but in different ways. One through sovereignty and predictable ceilings. The other through elasticity and user experience.

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The Reflection:

The lesson is clear: benchmarking AI isn’t about maximums anymore. It’s about predictability.

Enterprises must stop asking “how much can it handle?” and start asking “at what concurrency does it stay trustworthy?” That’s the benchmark that matters.

For gpt-oss:120B, the reflection is to design around guardrails, scale horizontally, and invest in runtime efficiency. For gpt-4o-mini, the reflection is to appreciate elasticity, but also recognize its dependency on external cloud control.

In the end, this isn’t about tokens per second. It’s about trust. Just as in networking, a stable 10 Mbps line builds more confidence than a bursty 100 Mbps one, so too in AI: predictable latency builds more trust than peak throughput ever could.

That’s the story the charts told me. And it’s the story I believe we must all start telling: the shift from stress numbers to trust numbers, the old way of chasing peaks to the new reality of engineering for predictability, the architectures that adapt to decode ceilings, and the reflection that consistency, not maximums, is the real foundation of AI trust.

Because in the end, it’s not about tokens. It’s about trust.

#AI #GenAI #LLM #EnterpriseAI #Benchmarking #AIOps #Infrastructure #Trust