The Benchmark Script That Revealed the Bottleneck

In the long-running opencode session to deploy and optimize large language models on 8x NVIDIA RTX PRO 6000 Blackwell GPUs, message [msg 2415] represents a quiet but pivotal moment: the creation of the macro benchmark script that would ultimately reveal the true bottleneck in the Kimi-K2.5 INT4 inference pipeline. This single message — consisting of a brief narrative statement and a file write operation — sits at the inflection point between planning and execution, between hypothesis and measurement.

Context: The Road to Profiling

The session had been a grueling journey through model deployment. After successfully deploying GLM-5-NVFP4 and then pivoting to Kimi-K2.5 (see [segment 17]), the team had achieved a respectable ~60 tok/s single-request throughput. But the question that had been nagging throughout the session remained unanswered: where exactly are the milliseconds going?

In [msg 2404], the assistant had proposed a systematic profiling campaign, noting that "measure first, optimize second" was the smart approach. The assistant laid out a three-phase plan in [msg 2407]: macro-level benchmarks using vLLM's HTTP profiling API, micro-level GEMM characterization, and a deep nsys timeline analysis. The plan was grounded in what the team already knew from the earlier GLM-5 NVFP4 profiling — that 69% of decode time had been consumed by dtype casting (unrolled_elementwise_kernel). But Kimi-K2.5 INT4 used a completely different kernel path: Marlin W4A16 kernels, which fuse INT4 dequantization directly into the GEMM computation. The assistant hypothesized that this architectural difference might eliminate the dtype-cast overhead entirely, potentially revealing a completely different bottleneck distribution.

The user's response in [msg 2408] was direct and unambiguous: "proceed with all benchmarks, write down results into k25b6000bench1.md as you gather them." This instruction set the stage for the subject message.

The Message Itself: A Transition to Execution

Message [msg 2415] reads in full:

[assistant] Now let me create the macro benchmark script and the micro benchmark scripts. I'll run the macro benchmarks first since they use the running server, and prepare the micro benchmarks to run on the container: [write] /home/theuser/glm-kimi-sm120-rtx6000bw/bench_k25_macro.py Wrote file successfully.

>

LSP errors detected in other files: <diagnostics file="/home/theuser/glm-kimi-sm120-rtx6000bw/source/server_args_sm120.py"> ERROR [1:1] Unexpected indentation ...

At first glance, this message appears unremarkable — a simple file write with some noisy LSP diagnostics from an unrelated file. But its position in the conversation reveals its significance. The assistant had just discovered in [msg 2413] that the vLLM instance was started without --profiler-config, making the HTTP profiler API unavailable ({&#34;detail&#34;:&#34;Not Found&#34;}). This was a critical discovery: the elegant "zero-downtime" profiling approach envisioned in the plan was blocked. The assistant had to pivot to a different strategy — writing custom benchmark scripts that would measure throughput and latency from the client side, rather than capturing kernel-level traces from the server.

Why Write a Custom Script?

The decision to write bench_k25_macro.py rather than restarting vLLM with profiling enabled reflects a pragmatic trade-off. Restarting the server with --profiler-config would require:

  1. Stopping the running vLLM service (which had only been up for ~24 minutes)
  2. Waiting for model reload (a 402GB GGUF model takes significant time to load)
  3. Potentially disrupting any concurrent users or tests
  4. Losing the current serving state By writing a client-side benchmark instead, the assistant could gather meaningful throughput and latency data immediately, without any server downtime. The script would use the OpenAI-compatible chat completions API that vLLM exposes — the same API that any production client would use. This approach measures end-to-end performance including network latency, server scheduling, and kernel execution, which is arguably more representative of real-world usage than a profiler trace that captures only GPU kernel time. The assistant's reasoning, visible in the narrative text, shows a clear ordering strategy: "I'll run the macro benchmarks first since they use the running server, and prepare the micro benchmarks to run on the container." This sequencing acknowledges that the macro benchmarks are non-invasive (they're just HTTP clients) while the micro benchmarks would require GPU access — and all 8 GPUs were occupied by the running vLLM instance at 96.9GB each.

Assumptions Embedded in the Script

The macro benchmark script (which was written in this message but executed in subsequent messages) made several implicit assumptions:

  1. The vLLM server would remain stable under concurrent load. The script was designed to test concurrency levels from 1 to 256, sending multiple simultaneous requests. This assumed the server had sufficient memory and thread pool capacity to handle this load without crashing.
  2. The OpenAI-compatible API would be sufficient for measuring decode performance. The script used /v1/chat/completions with max_tokens to control generation length. This assumed that the API overhead (HTTP parsing, tokenization, sampling) was negligible compared to actual GPU compute time.
  3. Single-stream latency at max_tokens=32 would be representative of per-token decode time. The script measured total time for generating 32 tokens and divided by 32 to get TPOT (time per output token). This assumed that the first token (prefill) latency was amortized across the full generation, and that there was no significant variance between early and late tokens.
  4. The model was already warm. The script included a warmup request but assumed that after one request, the CUDA graphs and NCCL communicators were fully initialized. In practice, some NCCL tuning happens lazily and later requests might see slightly different performance.

What the Script Actually Measured

The script that was written in this message and executed in [msg 2420] produced a clear picture: single-stream TPOT of ~12.4ms (~80 tok/s), with throughput scaling up to ~1536 tok/s at C=128 concurrency before plateauing. This was the first concrete performance data for the Kimi-K2.5 INT4 deployment — data that would later be compared against torch.profiler results to understand the bottleneck distribution.

The throughput plateau at C=128 was itself a significant finding. It suggested that the system was hitting a fundamental limit — likely either PCIe bandwidth for AllReduce operations, or the GPU compute capacity being saturated. This plateau became the central puzzle that drove the rest of the profiling campaign.

Input Knowledge Required

To fully understand this message, one needs:

  1. The session history: That vLLM was running Kimi-K2.5 INT4 on 8x RTX PRO 6000 Blackwell GPUs, that the profiler HTTP API was unavailable, and that the user had explicitly requested benchmarks.
  2. The model architecture: Kimi-K2.5 is a 1T-parameter MoE model with 61 layers, MLA (Multi-head Latent Attention), and INT4 quantization via Marlin kernels. The hidden size is 7168, and with TP=8, each GPU handles shards of the expert matrices.
  3. The hardware constraints: PCIe Gen5 x16 interconnect between GPUs (no NVLink), which limits AllReduce bandwidth. Each GPU has 96GB of VRAM, all occupied by the model.
  4. The earlier GLM-5 findings: That dtype casting had been the dominant bottleneck in the previous model, and that Marlin kernels might eliminate it.

Output Knowledge Created

This message created the bench_k25_macro.py script, which became the primary data-gathering tool for the macro-level phase of the profiling campaign. The script's output (executed in subsequent messages) produced the first concrete performance numbers for the Kimi-K2.5 INT4 deployment, establishing baseline throughput and latency metrics that would be essential for later optimization decisions.

More importantly, the message established a methodological pattern: when the ideal tool (the vLLM profiler API) was unavailable, the assistant improvised with a practical alternative that still produced actionable data. This pattern of pragmatic adaptation — rather than blocking on missing infrastructure — characterized the entire session.

The LSP Diagnostics: A Minor Distraction

The LSP error diagnostics at the bottom of the message are worth noting. They refer to an unrelated file (source/server_args_sm120.py) that had pre-existing syntax errors from earlier in the session. These diagnostics are a form of ambient noise — the assistant's IDE reporting errors in a file that was not part of the current operation. The assistant correctly ignored them, focusing on the successful write of the benchmark script. This is a common pattern in long coding sessions: the environment accumulates minor issues in peripheral files that don't affect the current task.

Significance in the Broader Narrative

In retrospect, [msg 2415] is the message where the assistant committed to a measurement-driven approach. The macro benchmark script it created would reveal the throughput plateau at ~1536 tok/s, which would then lead to the torch.profiler deep dive (in later segments) that ultimately identified AllReduce as consuming 51.5% of decode time. Without this script — without the decision to write custom benchmarks when the profiler API was unavailable — the team might have spent days speculating about bottlenecks rather than measuring them.

The message embodies a key principle of systems optimization: when you cannot instrument the system from the inside, measure it from the outside. The macro benchmark was the external measurement that framed every subsequent internal investigation.