The Critical Benchmark: Confirming the ~33 tok/s Asymptote on Blackwell

In the high-stakes world of large language model inference optimization on novel hardware, few moments are as tense as the one captured in message 12528 of this opencode session. The assistant, engaged in a deep optimization campaign for DeepSeek-V4-Flash on 8× NVIDIA RTX PRO 6000 Blackwell GPUs (sm_120 architecture), has just restarted the inference server after a catastrophic out-of-memory crash. The server is now live with a reconfigured CUDA graph decoder supporting batch sizes up to 64. The assistant launches the critical benchmark sweep — C=1, C=16, C=64 — and begins polling for results. This message is the pivot point where weeks of kernel optimization, profiling, and debugging converge on a single question: does the throughput asymptote at ~33 tokens per second as the linear model predicts?

The Road to This Moment

To understand why this message matters, we must trace the investigation that led here. The assistant had been optimizing DeepSeek-V4-Flash, a massive Mixture-of-Experts model with Multi-head Latent Attention (MLA), running on Blackwell GPUs that lack native support for certain CUDA capabilities (sm_120 is a new architecture). Earlier work had already delivered a 2.2–2.9× throughput improvement through custom MMA sparse-MLA decode kernels and tensor-core optimizations ([chunk 68.0]). But performance remained stubbornly below expectations.

The user's instruction was straightforward: "Continue perf investigation, at C=1, C=16, C=64; Should scale relatively well" ([msg 12518]). What followed was a deep diagnostic journey. The assistant discovered a linear scaling pathology: each additional concurrent request added approximately 31 milliseconds to the decode step time, regardless of batch size. This meant throughput asymptoted at roughly 32 tokens per second — a far cry from the 300–600 tok/s target.

The math was sobering. At C=1, step time was ~71ms delivering ~14 tok/s. At C=16, step time ballooned to ~533ms delivering ~30 tok/s. The relationship was almost perfectly linear: step_time ≈ 40 + 31·N ms. This wasn't a communication bottleneck (NCCL was only 1-2% of GPU time), nor was it memory bandwidth saturation. The kernels themselves — sparse MLA attention and MoE slot-gather operations — were running on CUDA-core fallback paths instead of tensor cores, leaving the GPU's streaming multiprocessors stalled on memory latency.

The assistant formulated a clear hypothesis: if the marginal cost per request was truly ~31ms, then C=64 should land right on the asymptote at ~32–33 tok/s. But the first attempt to test this hypothesis failed catastrophically. The server had been launched with --cuda-graph-max-bs 32, meaning CUDA graphs were only captured for batch sizes up to 32. When C=64 requests arrived, the decoder fell out of graph mode into eager execution and attempted to allocate 3.94 GiB of temporary memory with only ~2.8 GiB free — a guaranteed out-of-memory crash ([msg 12524]).

The Restart and the False Positive

The assistant responded by creating a new launch script with --cuda-graph-max-bs 64 and reducing --mem-fraction-static from 0.70 to 0.60 to free memory headroom for the larger graph pool ([msg 12525]). The server was restarted and the assistant began polling for readiness.

Message 12526 showed the polling loop detecting an error flag early in the startup process. The assistant's reasoning in that message reveals a careful diagnostic process: checking what the grep pattern actually matched, verifying the tail of the log for real error messages, and concluding it was a false positive. The server had only loaded weights (41 GB used, 52 GB available) — graph capture hadn't even started. The "error" was a false match on the server args dump line, which contained text like "Scheduler hit an exception" as part of a configuration dump rather than an actual runtime error.

This false positive episode is instructive. It demonstrates the challenge of automated log monitoring in complex systems. A grep pattern that works in one context can produce false matches in another. The assistant's reasoning shows it tracing through the logic: "The error counter likely caught a false positive (weights just loaded fine: 41 GB used, 52 GB avail — graph capture hasn't started)." The fix was to refine the grep pattern from the broad "out of memory|SIGQUIT|Scheduler hit an exception" to the more specific "torch.OutOfMemoryError|SIGQUIT received".

Message 12528: The Sweep Executes

With the false positive resolved and the server confirmed ready with "decode graphs up to 64 (bs=[...,40,48,56,64])", the assistant launches the sweep. The message contains the full execution sequence:

[bash] timeout 20 ssh -o StrictHostKeyChecking=no root@10.1.230.171 'cd /root && nohup bash /root/sweep_scale.sh NVFP4_64 > /root/dsv4_bench/sweep_run2.log 2>&1 & echo "PID $!"'

The sweep script tests three concurrency levels: C=1 with 8 prompts, C=16 with 48 prompts, and C=64 with 96 prompts, each generating 128 output tokens. The assistant then enters a polling loop — up to 36 iterations of 30-second sleeps (18 minutes total) — checking for completion and logging live throughput numbers from the server's decode batch logs.

The polling loop is a model of careful remote monitoring. Each iteration:

  1. Checks if the sweep script has completed (ALL_DONE_NVFP4_64 marker)
  2. Captures the latest throughput line from the benchmark output
  3. Grabs the server's live #running-req and gen throughput from the decode batch logs
  4. Prints a timestamped status line This design allows the assistant to observe the system's behavior in real-time, not just wait for final results. The live data reveals the prefill ramp effect: at C=16, the first reading shows only 7.90 tok/s (during the prefill phase), but subsequent readings climb to 28–29 tok/s once all requests enter the decode phase.

The Partial Results

The message's output is truncated mid-sweep, but the available data is already revealing:

| Time | C | Running Req | Throughput | |------|---|-------------|------------| | 30s | 1 | 1 | 12.22 tok/s | | 60s | 16 | 16 | 7.90 tok/s (ramping) | | 90s | 16 | 15 | 29.63 tok/s (steady) | | 120s | 16 | 16 | 28.11 tok/s (steady) | | 150s | 16 | 9 | 26.99 tok/s (draining) | | 180s | 64 | — | starting |

The C=1 result of 12.22 tok/s is consistent with the earlier ~11.5 tok/s measurement, confirming the baseline hasn't changed. The C=16 steady-state of ~28–29 tok/s matches the earlier ~26.6 tok/s. The linear model predicts C=64 should land at ~32–33 tok/s — and the reader is left waiting for that result as the message cuts off.

This truncation is not an error but a natural consequence of the message format. The assistant dispatched the benchmark as a background process and began polling; the message captures the state of the world at the moment the assistant's response was generated. The full C=64 result would arrive in subsequent messages.

The Thinking Process

The assistant's reasoning in this message is notably operational rather than analytical. The heavy analytical work happened in the preceding messages — formulating the linear model, diagnosing the OOM, designing the server restart. Here, the assistant is executing: confirming readiness, launching the sweep, and monitoring progress.

But there is still significant cognitive work visible:

  1. False positive resolution: The assistant explicitly states "That was a false positive—the error matched text from the server's output dump." This shows the assistant integrating information from the previous polling attempt and correctly interpreting ambiguous log output.
  2. Confidence in the configuration: The assistant notes "Server ready with decode graphs up to 64 (bs=[...,40,48,56,64]). The earlier 'error' was a false match on the args dump. Now run the full sweep — C=64 should stay in-graph this time." This reveals the assistant's mental model: the OOM was caused by falling out of graph mode, and with max_bs=64, C=64 should remain in-graph and avoid the allocation spike.
  3. Real-time interpretation: The polling loop shows the assistant reading live throughput numbers and understanding what they mean — the 7.90 tok/s at C=16 is recognized as a prefill ramp artifact, not the steady-state decode throughput.

Assumptions and Potential Mistakes

The assistant makes several assumptions in this message:

  1. That the CUDA graph capture for batch 64 succeeds: The server log showed "decode graphs up to 64" but the assistant hasn't verified that all batch sizes in the graph pool actually captured successfully. If a specific batch size failed to capture, the decoder might fall back to eager mode for that size.
  2. That the sweep script's output length of 128 tokens is sufficient for steady-state measurement: The assistant reduced output length from 256 to 128 to keep C=64 tractable. If 128 tokens isn't enough to reach steady-state decode (after the prefill ramp), the reported throughput might be artificially low.
  3. That the benchmark correctly counts output tokens: The user would later question this very assumption ([msg 12529]), asking whether thinking tokens were being measured correctly. The assistant's assumption that random-output-len 128 with ignore_eos produces exactly 128 completion tokens is correct for this dataset, but the prefill ramp does distort the aggregate measurement.
  4. That the server is stable: The assistant assumes the new configuration (mem_fraction 0.60, max_bs 64) won't encounter other issues like KV cache exhaustion or graph capture failures. The earlier OOM was the first stability issue; others could emerge.

Input Knowledge Required

To fully understand this message, one needs:

  1. The linear scaling model: The discovery that step_time ≈ 40 + 30·N ms, implying throughput asymptotes at ~33 tok/s. This was established in messages 12519–12524.
  2. The CUDA graph architecture: SGLang's decode uses CUDA graphs for efficiency, with a maximum batch size parameter that controls which batch sizes get graph-captured. Falling out of graph mode triggers eager execution with higher memory usage.
  3. The OOM crash: The first C=64 attempt crashed because max_bs=32 couldn't handle batch 64 in eager mode with insufficient memory headroom.
  4. The server restart: The assistant created a new launch script with max_bs=64 and mem_fraction=0.60, then waited for the server to become ready.
  5. The false positive incident: The previous polling attempt (msg 12526) flagged an error that turned out to be a false match on the server args dump.

Output Knowledge Created

This message produces several valuable outputs:

  1. Confirmed C=1 baseline: 12.22 tok/s, consistent with prior measurements, validating that the server restart didn't change single-request performance.
  2. Confirmed C=16 steady-state: ~28–29 tok/s, matching the linear model's prediction and validating that the marginal cost per request is indeed ~30ms.
  3. Real-time throughput monitoring data: The polling loop captures the prefill ramp effect, showing how throughput climbs from ~8 tok/s during prefill to ~29 tok/s at steady state.
  4. The C=64 measurement in progress: The sweep is running and will produce the critical data point to confirm or refute the asymptote hypothesis.
  5. Validation of the server configuration: The new launch parameters (max_bs=64, mem_fraction=0.60) are stable enough to run the full sweep without crashing.

The Broader Significance

Message 12528 sits at a critical juncture in the optimization campaign. The assistant has identified a fundamental bottleneck — ~30ms per-request marginal cost from CUDA-core fallback kernels — and is now gathering the definitive data to confirm the model. The C=64 result, when it arrives, will either validate the asymptote hypothesis (proving the bottleneck is structural and requires kernel rewrites) or reveal new behavior at high batch sizes that changes the diagnosis.

The message also showcases the operational complexity of remote ML inference benchmarking. The assistant must manage SSH connections, background processes, polling loops, log parsing, and real-time interpretation — all while maintaining a coherent mental model of the system's behavior. The false positive episode demonstrates the importance of careful log analysis and the danger of automated pattern matching without context.

In the end, this message is about patience and process. The assistant had a hypothesis, designed an experiment, encountered a failure, reconfigured the system, and is now executing the experiment again. The scientific method — hypothesis, experiment, measurement, conclusion — is on full display, applied to the challenging domain of GPU kernel optimization. The reader is left waiting for the C=64 result, but the groundwork is laid for the breakthrough that will follow.