Reading the Artifacts of Prior Work: How a Directory Listing Shaped the Next Phase of MoE Kernel Tuning

In the middle of an intensive optimization session for GLM-5-NVFP4 inference on 8× NVIDIA RTX PRO 6000 Blackwell GPUs, the assistant receives a simple but pivotal instruction from the user: "Read info in ./ from a similar run which did tuning on a similar model" ([msg 663]). The assistant's response, message [msg 665], is deceptively straightforward — it reads the contents of four directories in a local repository. Yet this single message represents a critical turning point in the session. It is the moment when the assistant transitions from probing the live system and its running server to consulting prior art: a repository of findings, configurations, patches, and logs from a previous tuning campaign on the same hardware architecture (SM120 Blackwell) but for a different model (Kimi K2). The directory listings it retrieves will directly inform the next round of decisions about MoE kernel configuration, server parameters, and performance optimization strategy.

The Message in Full

The assistant issues three parallel read operations against the local filesystem at /home/theuser/glm-kimi-sm120-rtx6000bw/, listing the contents of four subdirectories: moe-configs/, source/, configs/, and logs/. The output reveals a rich collection of artifacts from a prior tuning run:

moe-configs/ contains five JSON files: a specific tuning result for E=161,N=192 with FP8 quantization, a default and an FP8 MoE configuration for the RTX 6000 Blackwell, and separate TP4 and TP8 configuration files. The naming convention E=161,N=192,device_name=NVIDIA_RTX_PRO_6000_Blackwell_Server_Edition,dtype=fp8_w8a8,per_channel_quant=True.json follows the pattern used by SGLang's fused MoE Triton kernel config system, where each file encodes the expert count, intermediate size, device name, data type, and quantization scheme.

source/ contains six Python files that appear to be patches or custom implementations: decode_attention.py, extend_attention_original.py, extend_attention_patched.py, mxfp4.py, server_args_sm120.py, and sm120-detection.py. These suggest the prior tuning effort involved modifying SGLang's attention kernels, adding MXFP4 quantization support, creating custom server arguments for SM120, and implementing SM120 architecture detection.

configs/ holds nine files covering hardware information (CPU, GPU, memory, topology), the Kimi K2-5 model configuration, a list of MoE configs, NVIDIA SMI output, Python package versions, and workspace contents — essentially a complete snapshot of the environment used in the prior run.

logs/ contains twelve log files documenting various stages of the previous tuning campaign: MoE tuning logs (both TP4 and full), SGLang server logs with different patches applied (PR #16975, SM120 fix, NVFP4), a thinking model final benchmark, int4 quantization attempts, and TP4+PP2 experiments.

Why This Message Matters

The message is not making any decisions. It is not executing any commands against the server. It is not modifying any configuration. It is purely an act of information gathering — but it is information gathering with profound strategic importance. The assistant has been working for many rounds trying to improve throughput from approximately 880 tok/s toward a target of several thousand tokens per second. It has examined the running server, probed GPU utilization, inspected the SGLang source code, and spawned a subagent to trace the MoE kernel code path. But the user's instruction to consult the local repository reframes the problem: instead of starting from scratch, the assistant can leverage weeks or months of prior work on the exact same hardware.

The prior run was for Kimi K2, another large Mixture-of-Experts model with similar architectural characteristics (E=161, N=192 for the MoE layers, running on SM120 Blackwell GPUs). The repository contains the fruits of that earlier campaign: tuned MoE kernel configurations that were empirically found to perform well on the RTX PRO 6000 Blackwell, patches that were developed to fix SM120-specific issues, and logs that document what worked and what didn't. For the assistant, this is like being handed a map of the territory after having spent hours bushwhacking.

The Assumptions Embedded in This Message

Several assumptions underpin the assistant's decision to read these directories. First, there is the assumption that the Kimi K2 tuning artifacts are transferable to GLM-5. Both models use MoE architectures, but they differ in key parameters: GLM-5 has E=256 experts with N=2048 intermediate size and top_k=8, while the Kimi K2 config in the repository shows E=161,N=192. The expert count and intermediate sizes are different, which means the MoE kernel configurations may not be directly applicable. However, the tuning methodology, the patches for SM120 support, and the server argument adjustments likely transfer.

Second, the assistant assumes that the local repository is complete and accurate — that the configs, patches, and logs reflect the final state of the prior tuning effort and not abandoned experiments. The presence of both moe-tuning.log and moe-tuning-tp4.log, along with multiple sglang-*.log files, suggests a methodical progression through different configurations, but the assistant must later determine which artifacts represent successful outcomes versus dead ends.

Third, there is an implicit assumption that the hardware environment is identical between the two runs. The configs/ directory contains GPU topology information, memory details, and CPU information that would need to be verified against the current system to ensure compatibility.

Input Knowledge Required

To fully understand this message, one needs to know the broader context of the optimization session: that the assistant is running GLM-5-NVFP4 on 8 RTX PRO 6000 Blackwell GPUs using SGLang with the flashinfer_cutlass MoE backend, that throughput has plateaued around 880–912 tok/s despite increasing concurrency from 256 to 512, and that GPU power draw remains low (~83W idle, well below the 600W TDP), indicating severe underutilization. One also needs to understand the architecture of MoE models — that the fused MoE kernel is a critical performance bottleneck, and that SGLang supports a system of pre-tuned kernel configurations stored as JSON files keyed by expert count, intermediate size, device name, and data type.

Additionally, understanding the significance of the file names requires knowledge of the SGLang codebase: that E=161,N=192,... files are MoE kernel tuning configurations used by the Triton-based fused MoE kernel, that TP4 and TP8 refer to tensor parallelism degrees, and that the source/ directory contains patches that were likely developed to work around SM120-specific issues in SGLang's attention and quantization code paths.

Output Knowledge Created

This message produces a structured inventory of prior work. The assistant now knows:

  1. That MoE kernel configs exist for E=161,N=192 (Kimi K2) but not for E=256,N=2048 (GLM-5), meaning new tuning will be needed.
  2. That TP4 and TP8 config files exist, suggesting both parallelism strategies were explored.
  3. That SM120-specific patches were developed for attention kernels (extend_attention_patched.py), MXFP4 quantization (mxfp4.py), server arguments (server_args_sm120.py), and architecture detection (sm120-detection.py).
  4. That the prior tuning campaign was extensive, with 12 distinct log files covering different configurations, patches, and parallelism strategies.
  5. That the prior run included experiments with TP4+PP2 (tensor parallelism 4 + pipeline parallelism 2), which the assistant had earlier identified as a promising direction. This knowledge will directly inform the assistant's next actions: it can examine the MoE config files to understand the tuning format, review the SM120 patches for applicability to the current GLM-5 deployment, and consult the logs to learn from the prior campaign's successes and failures.

The Thinking Process Revealed

The message itself contains no explicit reasoning — it is purely a set of tool calls. But the thinking process is visible in the sequence of actions leading up to it. The assistant has been systematically working through a plan: check server status, run benchmarks at higher concurrency, inspect the model configuration, examine the SGLang MoE kernel code path. The user's intervention to point at the local repository causes a shift in strategy. Instead of continuing to explore the live system blindly, the assistant now has a reference point — a body of prior work that can shortcut the exploration phase.

The decision to read directory listings rather than individual files is also strategic. The assistant is taking an inventory first, surveying what is available before diving into specific files. This is a classic information-gathering pattern: list directories to understand the scope, then read specific files based on relevance. The subsequent messages in the session will show the assistant reading individual config files and logs, building a detailed understanding of the prior tuning effort before applying those lessons to the current GLM-5 deployment.

Conclusion

Message [msg 665] is a quiet but essential moment in a complex optimization session. It does not contain flashy results or dramatic breakthroughs. It does not modify any code or produce any performance numbers. But it represents the assistant responding to the user's guidance by grounding its exploration in prior empirical work. The directory listings it retrieves will serve as a roadmap for the next phase of tuning — a map of what has been tried, what configurations worked, what patches were needed, and what strategies remain to be explored. In the craft of systems optimization, knowing what has already been attempted is often as valuable as knowing what to try next.