The 38 Tok/s Verdict: When Custom Allreduce Collides with PCIe Topology

Introduction

In the high-stakes world of large language model serving, every token per second counts. When you're running an 8×GPU setup with RTX PRO 6000 Blackwell cards, the difference between a well-optimized inference stack and a poorly configured one can be a factor of two or more in throughput. Message [msg 5201] captures a pivotal moment in a long optimization saga: the moment when a promising optimization technique—custom allreduce on PCIe—was empirically proven to be a catastrophic dead end. The benchmark result of 38.2 tok/s, compared to a baseline of ~89.5 tok/s, was the quantitative verdict that ended one line of inquiry and forced a strategic pivot to a completely different approach.

The Message

The assistant executed a benchmark against a running SGLang server configured with custom allreduce forced onto a PCIe topology:

[assistant] [bash] ssh root@10.1.230.174 '/root/ml-env/bin/python3 /tmp/benchmark_eagle3.py --server-url http://localhost:30000 --max-tokens 256 --num-runs 5'
Benchmarking http://localhost:30000
  max_tokens=256, num_runs=5, warmup=2

  Warmup 1/2 done (41 tokens)
  Warmup 2/2 done (41 tokens)

  Run 1/5: 256 tokens in 6.75s = 37.9 tok/s (prompt: 32 toks)
  Run 2/5: 256 tokens in 6.72s = 38.1 tok/s (prompt: 31 toks)
  Run 3/5: 256 tokens in 6.59s = 38.8 tok/s (prompt: 26 toks)
  Run 4/5: 256 tokens in 6.64s = 38.5 tok/s (prompt: 28 toks)
  Run 5/5: 256 tokens in 6.75s = 37.9 tok/s (prompt: 34 toks)

  ========== Results ==========
  Overall tok/s: 38.2

The numbers are brutally consistent: five runs, each with different prompt lengths (26–34 tokens), all producing 256 tokens, and every single run landing between 37.9 and 38.8 tok/s. The overall average of 38.2 tok/s tells an unambiguous story.

Context: The Optimization Odyssey

To understand why this message was written, one must appreciate the journey that led to it. The assistant had been engaged in a multi-day effort to optimize speculative decoding for the Kimi-K2.5 model running on an 8×RTX PRO 6000 Blackwell system. The core problem was that EAGLE-3 speculative decoding, which should have provided a speedup over the base model, was actually slower than running without speculation. The bottleneck was the "verify step"—the phase where the draft model's predictions are checked against the target model—which required 122 NCCL allreduce operations per step, consuming ~30ms of overhead.

The assistant had systematically tested and eliminated multiple optimization approaches. FlashInfer allreduce fusion failed because its JIT compiler lacked support for the SM120 (Blackwell) architecture. Torch symmetric memory was unavailable for the same reason. Expert Parallelism with flashinfer's A2A backend crashed with assertion errors and out-of-memory conditions. Each dead end was methodically documented in an optimization plan document.

A genuine breakthrough had been discovered: reducing --cuda-graph-max-bs from 512 to 128 improved baseline throughput from 82 to 89.5 tok/s—a 9% gain—by freeing GPU memory for KV cache. But this still left the verify-step bottleneck unresolved, and EAGLE-3 speculation remained at only 54.1 tok/s.

The custom allreduce approach was the next candidate. The idea was compelling: replace NCCL's allreduce with a custom kernel that uses IPC (Inter-Process Communication) shared memory for low-latency small-tensor allreduce. This could potentially bypass the PCIe bus contention that NCCL suffers from. The assistant had patched SGLang's communicator code to force-enable custom allreduce on PCIe topologies (which are normally excluded from this optimization, as custom allreduce is designed for NVLink-connected GPUs).

Why This Message Was Written

The message was written to answer a specific, high-stakes question: Does custom allreduce on PCIe actually improve throughput? The assistant had just spent significant effort modifying SGLang's source code to enable this feature, reverting previous changes, debugging server crashes, and fixing a configuration error (using --mem-fraction-static 0.55 instead of the auto-detected 0.88). After finally getting the server to start successfully with custom allreduce enabled, the immediate next step was to measure its performance.

The benchmark was the moment of truth. The assistant needed empirical evidence—not theoretical analysis—to determine whether this optimization path was viable. The five-run benchmark with warmup was designed to produce statistically reliable results: warmup runs to stabilize GPU state, multiple runs to measure variance, and consistent output length (256 tokens) for comparability.

Decisions and Assumptions

Several decisions and assumptions are visible in this message:

Decision to benchmark immediately after server startup: The assistant launched the benchmark script as soon as the server was confirmed ready (the previous message showed the server log indicating it had started). This was the right call—there was no reason to delay the measurement.

Decision to use 5 runs with warmup: The benchmark script was configured with --num-runs 5 and --warmup 2. This shows an understanding of the need for statistical reliability in benchmarking. Warmup runs eliminate cold-start effects, and multiple runs help identify variance.

Assumption that the benchmark script was already on the target machine: The assistant checked for the script's existence in message [msg 5196] (ls -la /tmp/benchmark_eagle3.py), confirming it was present. This was a prudent verification step.

Assumption that 38 tok/s was definitively bad: The assistant didn't need to run a separate baseline benchmark in this message because the baseline was already well-established from previous work (~89.5 tok/s). The comparison was implicit: 38.2 tok/s is less than half of 89.5 tok/s.

Assumption that the custom allreduce was actually active: The server log from the previous message confirmed SGLANG_FORCE_CUSTOM_AR_PCIE=1: Enabling custom allreduce on PCIe topology, so the assistant could be confident the feature was engaged.

Input Knowledge Required

To fully understand this message, one needs:

  1. Knowledge of the baseline performance: The reader must know that the baseline (NCCL allreduce, no custom AR) achieved ~89.5 tok/s. Without this reference point, 38.2 tok/s might seem like a reasonable number.
  2. Understanding of PCIe vs. NVLink topology: The RTX PRO 6000 Blackwell GPUs in this system are connected via PCIe, not NVLink. This is crucial because custom allreduce is designed for NVLink-connected GPUs where direct GPU-to-GPU communication is fast. On PCIe, all communication must go through the CPU's PCIe root complex, creating a bottleneck.
  3. Knowledge of the allreduce communication pattern: The verify step in speculative decoding requires 122 allreduce operations per step, each involving small tensors. The all-to-all communication pattern (every GPU talking to every other GPU) creates massive PCIe bus contention.
  4. Familiarity with SGLang's custom allreduce implementation: The SGLANG_FORCE_CUSTOM_AR_PCIE environment variable and the IPC shared memory mechanism are SGLang-specific concepts.
  5. Understanding of the benchmark methodology: The benchmark script measures single-stream token generation throughput, which is the relevant metric for latency-sensitive applications.

Output Knowledge Created

This message created several important pieces of knowledge:

  1. Quantitative proof that custom allreduce on PCIe is 2.3× slower than NCCL: 38.2 vs. 89.5 tok/s is a definitive result. The custom kernel, despite its theoretical advantages for small-tensor allreduce, was catastrophically worse on this hardware.
  2. Confirmation of PCIe bus contention as the bottleneck: The massive performance degradation is consistent with the hypothesis that PCIe bus contention from the all-to-all communication pattern is the root cause. Each allreduce operation requires all 8 GPUs to exchange data, and on PCIe, this saturates the bus.
  3. Evidence that IPC shared memory doesn't help on PCIe: The custom allreduce uses IPC shared memory for low-latency communication, but this optimization is ineffective when the underlying bus is the bottleneck.
  4. Closure on one optimization path: This result allowed the assistant to definitively eliminate custom allreduce from consideration and pivot to the next approach—upgrading CUDA to version 13 to enable Blackwell-native optimizations.
  5. A reproducible benchmark methodology: The five-run benchmark with warmup provides a template for future performance measurements.

The Thinking Process

The thinking process visible in this message and its surrounding context reveals a systematic, scientific approach to optimization. The assistant:

  1. Formulated a hypothesis: Custom allreduce on PCIe might reduce the verify-step overhead because IPC shared memory could be faster than NCCL's PCIe communication.
  2. Implemented the experiment: Patched SGLang's communicator.py to force-enable custom allreduce on PCIe topologies, set the SGLANG_FORCE_CUSTOM_AR_PCIE=1 environment variable.
  3. Debugged failures: The server initially crashed or failed to start (messages [msg 5176]–[msg 5194]). The assistant discovered that the --mem-fraction-static 0.55 setting was incorrect—the working baseline used auto-detected 0.88. This was a valuable debugging lesson: always verify configuration parameters match the known working state.
  4. Verified the experimental setup: Checked server logs to confirm custom allreduce was actually enabled, verified the benchmark script existed, waited for the server to be fully ready.
  5. Ran the experiment: Executed the benchmark with proper methodology (warmup, multiple runs).
  6. Interpreted the results: The 38.2 tok/s result was immediately recognized as a failure—less than half the baseline.
  7. Documented the outcome: The result was recorded in the optimization plan document (as mentioned in the chunk summary), providing a clear record for future reference.

Mistakes and Incorrect Assumptions

Several mistakes and incorrect assumptions are evident:

Mistake: Forcing custom allreduce on PCIe without understanding the hardware topology: The fundamental assumption was that custom allreduce's IPC shared memory mechanism would be faster than NCCL on PCIe. In hindsight, this was naive. Custom allreduce is designed for NVLink-connected GPUs where direct peer-to-peer communication is possible. On PCIe, all traffic must go through the CPU's PCIe root complex, and the all-to-all pattern of allreduce operations creates a bottleneck that no amount of IPC optimization can fix.

Mistake: The --mem-fraction-static configuration error: In the previous messages, the assistant used --mem-fraction-static 0.55 which caused the server to fail with "Not enough memory" errors. The working baseline used auto-detected 0.88. This was a configuration mismatch that wasted significant debugging time. The lesson: when reproducing a known working configuration, use identical parameters.

Assumption that custom allreduce would help with small tensors: The verify step involves 122 small-tensor allreduce operations. Custom allreduce is optimized for small tensors, but the optimization assumes fast inter-GPU connectivity. On PCIe, even small tensors suffer from bus contention because of the all-to-all communication pattern.

Assumption that the code changes were correct: The assistant had to revert debug print statements and verify that the source code was clean. This highlights the risk of ad-hoc code modifications during debugging.

The Broader Significance

This message represents a critical inflection point in the optimization journey. The 38.2 tok/s result was the final nail in the coffin for the entire line of PCIe-based allreduce optimization. After this, the assistant pivoted to upgrading CUDA from version 12.8 to 13.1, which would unlock Blackwell-native optimizations like FlashInfer allreduce fusion (with SM120 support), Torch symmetric memory, and other features that were previously unavailable.

The message also illustrates a fundamental principle of systems optimization: measure, don't guess. The custom allreduce approach had theoretical appeal—IPC shared memory should be faster than NCCL's PCIe communication. But the actual measurement revealed a 2.3× slowdown. Without this empirical evidence, the team might have continued down this path, wasting more time on a fundamentally flawed approach.

Conclusion

Message [msg 5201] is a textbook example of the scientific method applied to systems optimization. A hypothesis was formulated, an experiment was designed and executed, and the results were measured and interpreted. The outcome—38.2 tok/s versus a baseline of 89.5 tok/s—was unambiguous. The custom allreduce approach on PCIe was not just unhelpful; it was actively harmful, more than halving the throughput.

This message also demonstrates the importance of understanding hardware topology when designing optimization strategies. What works for NVLink-connected GPUs can be disastrous for PCIe-connected ones. The assistant's willingness to run the experiment, accept the negative result, and pivot to a new approach is the hallmark of effective engineering. The 38 tok/s verdict closed one chapter and opened another, leading to the CUDA 13 upgrade that would eventually unlock the Blackwell-native optimizations needed to make speculative decoding profitable.