The Wall of PCIe: When Software Tuning Meets Hardware Reality

Introduction

In the high-stakes world of deploying trillion-parameter language models on multi-GPU systems, there comes a moment when software optimization exhausts its bag of tricks and the fundamental physics of the hardware takes over. Message [msg 2385] captures exactly such a moment. It is a short, almost laconic assistant message containing benchmark results for the INT4 quantized Kimi-K2.5 model running on eight NVIDIA RTX PRO 6000 Blackwell GPUs. On the surface, it is a routine performance measurement: six successful requests at concurrency level 1, 81.7 tokens per second, latency averaging 6.27 milliseconds. But beneath these numbers lies the quiet conclusion of an intensive tuning campaign—a campaign that systematically tested NCCL algorithms, protocol variants, channel counts, thread configurations, and vLLM compilation flags, only to arrive back at essentially the same throughput as the very first baseline. This message is the point at which the assistant, and by extension the reader, must confront an uncomfortable truth: the bottleneck is not software, but hardware.

The Message

The subject message reads in its entirety:

[assistant] Ready. Let me benchmark: [bash] python3 /home/theuser/glm-kimi-sm120-rtx6000bw/benchmark.py Warming up...

>

Benchmark results: ------------------------------------------------------------------------------------------------ C= 1 | 6/ 6 ok (0 err) | wall= 37.6s | 81.7 tok/s | lat avg= 6.27 p50= 6.27 p99= 6.28 C= 2 | 8/ 8 ok (0 err) | wall= 30.5s | 134.3 tok/s | lat avg= 7.62 p50= 7.61 p99= 7.68 C= 4 | 12/ 12 ok (0 err) | wall= 26.0s | 236.6 tok/s | lat avg= 8.65 p50= 8.65 p99= 8.70 C= 8 | 16/ 16 ok (0 err) | wall= 19.7s | 415.3 tok/s | lat avg= 9.85 p50= ...

The format is a standard multi-concurrency throughput benchmark. Concurrency levels (C) from 1 to 8 are tested, with each level running a batch of requests equal to 2×C (so C=1 runs 6 requests sequentially, C=2 runs 8, etc.). The wall time, tokens per second, and latency percentiles are reported. The results are clean: no errors, tight latency distributions (p99 only slightly above p50), and smooth scaling from 81.7 tok/s at C=1 to 415.3 tok/s at C=8.

The Road to This Benchmark

To understand why this message matters, one must trace the path that led to it. The session had been an odyssey through model deployment on PCIe-connected Blackwell GPUs. Earlier segments covered the installation of NVIDIA drivers, CUDA toolkits, and the painful resolution of flash-attn build issues. The team had deployed and debugged the GLM-5-NVFP4 model, then pivoted to Kimi-K2.5 in NVFP4 format, achieving ~61 tok/s single-stream before identifying the PCIe allreduce bottleneck for the 61-layer MLA (Multi-head Latent Attention) architecture. They then pivoted to MiniMax-M2.5 FP8, a 230B GQA model that achieved up to 4,000 tok/s with expert parallelism, proving that GQA + smaller active parameters was vastly superior on PCIe-bound hardware.

The final pivot was to the native INT4 Kimi-K2.5 (moonshotai/Kimi-K2.5), a 547GB behemoth that took 36 minutes to load across 8 GPUs. The initial baseline benchmark ([msg 2364]) showed 81.4 tok/s single-stream—already well above the user's target of 40-50 tok/s. The user had asked to "Run benchmarks and try to get to single stream >40~50; If not there try NCCL LL alg and other safe-ish tricks" ([msg 2359]). The assistant, having already exceeded the target, nonetheless embarked on a tuning campaign to push further.

The tuning attempts included:

The Assumptions Under Test

The tuning campaign reveals several implicit assumptions that were being tested and ultimately disproven:

Assumption 1: NCCL configuration matters for this workload. The assistant tried Ring algorithm, LL protocol, multiple channel counts, and increased thread counts. The assumption was that PCIe allreduce communication was the bottleneck and that NCCL tuning could improve it. The results show that NCCL configuration has essentially zero impact on throughput for this model on this hardware. This suggests either that the allreduce communication is not the dominant bottleneck, or that the default NCCL configuration is already near-optimal for this workload.

Assumption 2: Compilation optimizations can unlock hidden throughput. The --compilation-config '{"level": 3}' flag was the last attempt before the subject message. The assumption was that more aggressive torch.compile optimizations could reduce kernel launch overhead or fuse operations more effectively. The result: 81.7 tok/s vs 81.4 tok/s baseline—a 0.4% improvement that is within measurement noise.

Assumption 3: The bottleneck is in the software stack, not the hardware. This is the most fundamental assumption being tested. The entire tuning campaign was predicated on the belief that some software configuration could squeeze more throughput out of the system. The subject message is the evidence that disproves this assumption. The bottleneck is hardware: the PCIe bus bandwidth, the memory bandwidth of the GPUs, or the allreduce synchronization cost across 8 GPUs.

The Thinking Process

The assistant's thinking, visible in the preceding messages, shows a methodical approach to bottleneck identification. After the first tuned benchmark at [msg 2373] showed 81.9 tok/s (essentially identical to baseline), the assistant wrote: "Nearly identical — 81.9 vs 81.4 tok/s single-stream. The NCCL Ring/LL/channels tuning didn't meaningfully change things. The bottleneck isn't NCCL algorithm choice."

This is a critical moment of reasoning. The assistant correctly identifies that the NCCL tuning had no effect, but does not immediately conclude "bottleneck is hardware." Instead, it continues testing with new hypotheses: checking whether torch.compile is being used ([msg 2376]), examining the compilation config ([msg 2377]), and trying --num-scheduler-steps ([msg 2379]). Each new hypothesis is a reasonable next step in the diagnostic process.

The assistant also shows awareness of the hardware constraints, noting at [msg 2377]: "The model is already at 76-82 tok/s single-stream for a 1T parameter model on PCIe, which is quite good. The bottleneck is fundamental — 61 layers of MLA attention with allreduce across 8 PCIe GPUs." This insight—that the bottleneck is "fundamental"—is the correct diagnosis, but the assistant continues tuning anyway, perhaps hoping that a software trick could squeeze out a few more percent.

Input Knowledge Required

To fully understand this message, one needs:

  1. The history of the session: The previous model deployments (GLM-5 NVFP4, MiniMax-M2.5 FP8, Kimi-K2.5 NVFP4) and their benchmark results provide context for what "good" looks like. The MiniMax-M2.5 FP8 achieving 4,000 tok/s with EP8 sets a high bar that the MLA-based Kimi-K2.5 cannot reach.
  2. Understanding of MLA vs GQA architectures: The Kimi-K2.5 uses Multi-head Latent Attention (MLA), which requires allreduce synchronization across all GPUs for every decode step. This is fundamentally more communication-intensive than Grouped Query Attention (GQA), which can be sharded more efficiently.
  3. Knowledge of PCIe vs NVLink: The 8 GPUs are connected via PCIe, not NVLink. This means inter-GPU communication goes through the CPU's PCIe root complex, which has limited bandwidth and higher latency compared to direct GPU-to-GPU NVLink connections.
  4. Understanding of NCCL tuning knobs: The flags being tested (NCCL_ALGO, NCCL_PROTO, NCCL_MAX_NCHANNELS, etc.) are NCCL environment variables that control how collective communication operations are implemented.
  5. vLLM compilation architecture: The --compilation-config flag controls how vLLM uses torch.compile to optimize model execution, with higher levels enabling more aggressive fusion and kernel optimization.## What the Numbers Actually Mean The benchmark results in the subject message deserve closer scrutiny. At 81.7 tok/s single-stream for a ~1 trillion parameter model, the throughput is remarkably good in absolute terms. To put it in perspective: generating 512 tokens takes about 6.3 seconds, and 2048 tokens takes about 27 seconds (as measured in [msg 2375]). For interactive use cases like chat or code generation, this is a perfectly usable speed—well within the range where a human reader perceives the model as "fast." The scaling with concurrency is also healthy: from 81.7 tok/s at C=1 to 415.3 tok/s at C=8, the throughput scales roughly 5× with 8× concurrency. This is sub-linear scaling (expected due to increased contention for GPU compute and memory bandwidth), but not catastrophically so. The latency increases modestly from 6.27ms at C=1 to 9.85ms at C=8, indicating that the system is not heavily oversubscribed. What makes these numbers remarkable is that they are achieved on PCIe-connected GPUs. With NVLink, the allreduce overhead would be substantially lower, and the throughput would likely be significantly higher. The message thus serves as a data point for what is achievable on PCIe-bound Blackwell hardware with a 1T-parameter MLA model.

Mistakes and Incorrect Assumptions

While the message itself is correct—the benchmark results are accurate and the methodology is sound—the broader context reveals some questionable assumptions:

The assumption that tuning would help. The user's instruction at [msg 2359] was to "try to get to single stream >40~50; If not there try NCCL LL alg and other safe-ish tricks." The assistant had already achieved 81.4 tok/s in the first baseline, which was double the target. Yet the assistant spent multiple rounds tuning anyway. This reflects a common engineering pattern: when a system is already meeting requirements, the instinct to optimize can lead to diminishing-returns efforts. The assistant could have declared victory after the first baseline and moved on to deployment, but instead pursued marginal gains.

The assumption that compilation level 3 would help. The --compilation-config '{"level": 3}' attempt was based on the hypothesis that more aggressive torch.compile optimizations could improve throughput. In practice, the improvement was negligible (0.4%). This is likely because vLLM's V1 engine already uses CUDAGraph and AOT compilation for the critical paths, and the compilation level primarily affects less performance-sensitive code paths.

The assumption that the V1 engine supports --num-scheduler-steps. This was a clear error—the assistant tried a flag that doesn't exist in the V1 engine, causing the process to die at startup (<msg id=2380-2383>). This wasted time and required a restart. The error was caught quickly, but it illustrates the risk of trying flags without verifying compatibility.

Output Knowledge Created

This message creates several pieces of actionable knowledge:

  1. A confirmed baseline for INT4 Kimi-K2.5 on 8× Blackwell PCIe: 81.7 tok/s single-stream, scaling to 415.3 tok/s at C=8. This is a reference point for anyone deploying similar models on similar hardware.
  2. Evidence that compilation tuning has negligible impact: The --compilation-config &#39;{&#34;level&#34;: 3}&#39; experiment shows that vLLM's default compilation settings are already near-optimal for this model/hardware combination.
  3. Confirmation that the bottleneck is hardware, not software: The systematic failure of all tuning attempts to meaningfully improve throughput proves that the system is operating at the hardware limit. This is valuable knowledge because it tells the operator where to invest next: better interconnects (NVLink), faster GPUs, or different model architectures.
  4. Validation that the INT4 variant outperforms NVFP4: The INT4 Kimi-K2.5 achieves 81.7 tok/s vs the NVFP4 variant's ~61 tok/s ([msg 2365]), a 34% improvement. This confirms that INT4 quantization is the better choice for this model on this hardware, likely because the reduced weight bandwidth for quantized MoE experts alleviates a memory-bound bottleneck.

Conclusion

Message [msg 2385] is a quiet turning point in the session. It is the moment when the assistant, after a thorough and methodical tuning campaign, accepts that software optimization has reached its limit. The benchmark numbers are clean, the methodology is sound, and the conclusion is inescapable: the system is running at the speed of its hardware, not its software.

For the reader, this message is a case study in the discipline of performance engineering. The best optimizers know not only which knobs to turn, but also when to stop turning them. The 81.7 tok/s reported in this message is not a failure—it is a successful identification of the hardware ceiling, achieved through systematic hypothesis testing and measurement. The session would go on to deploy this model as a production systemd service, confident that the hardware is being fully utilized.

In the end, the message teaches us that sometimes the most important result of a benchmark is not a higher number, but the certainty that no higher number is possible without changing the hardware. That is a form of knowledge just as valuable as a speedup.