The Pivot to Execution: Testing Piecewise CUDA Graphs on Blackwell GPUs

Introduction

In the course of a high-stakes optimization campaign for the GLM-5-NVFP4 large language model running on eight NVIDIA RTX PRO 6000 Blackwell GPUs, a single message marks the critical transition from planning to execution. Message [msg 975] is the moment the assistant stops documenting and starts testing—a deceptively simple SSH command that creates a server launch script with the --enable-piecewise-cuda-graph flag. This message, sandwiched between the completion of a rigorous baseline benchmark and the first real attempt to improve throughput, encapsulates the systematic, hypothesis-driven methodology that defines the entire session. It is a message about preparation, about the careful orchestration of server lifecycles, and about the assumptions that underpin experimental science in the context of cutting-edge ML inference.

Context: The State of Play

To understand why this message was written, one must appreciate what preceded it. The assistant had just completed an extensive documentation phase, writing ten improvement documents (numbered 02 through 11) covering optimization strategies ranging from MSCCLPP allreduce acceleration to expert parallelism and persistent grouped GEMM kernels ([msg 963]). These documents were not speculative—they were structured as testable hypotheses, each with a status, priority, and expected impact. Document 01, "Piecewise CUDA Graphs," had already been written earlier and carried the expectation of a 10–20% throughput improvement across all concurrencies.

More importantly, the assistant had just finished running a comprehensive baseline benchmark across four concurrency levels (1, 10, 256, and 1024 requests) against the current server configuration ([msg 968]). The baseline numbers were sobering: at concurrency 1024, the server achieved 1,520 output tokens per second and 3,028 total tokens per second, with a time-per-output-token (TPOT) of 523 milliseconds. These numbers established the reference point against which all future optimizations would be measured. Without this baseline, no improvement claim would be meaningful.

The assistant had also just spent several messages struggling to stop the running server ([msg 969] through [msg 974]). A simple kill command left processes lingering; a kill -9 appeared to succeed but a subsequent check showed a PID still alive. It took multiple verification steps—using pgrep, ps aux, and careful filtering—to confirm that the SGLang server processes were truly terminated. This struggle reveals an important operational reality: in distributed GPU serving, process management is non-trivial. Server processes spawn children, and ensuring a clean shutdown requires persistence.

The Message Itself

The subject message reads:

Server is stopped. Now let me create the piecewise CUDA graph launch script and start the server:

>

``bash ssh root@10.1.230.174 'cat > /root/run_tp8_piecewise.sh << '\''SCRIPT'\'' #!/bin/bash source /root/ml-env/bin/activate export PYTHONUNBUFFERED=1 NCCL_IB_DISABLE=1 NCCL_P2P_LEVEL=5 NCCL_MIN_NCHANNELS=8 OMP_NUM_THREADS=8 SAFETENSORS_FAST_GPU=1 CUDA_HOME=/usr/local/cuda-12.8 python3 -u -m sglang.launch_server --model lukealonso/GLM-5-NVFP4 --served-model-name glm-5 \ --reasoning-parser glm45 --tool-call-parser glm47 \ --trust-remote-code --tp-size 8 --mem-fraction-static 0.92 \ --max-running-requests 2048 --kv-cache-dtype auto \ --quantization modelopt_fp4 --attention-backend flashinfer \ --fp8-gemm-backend cutlass --nsa-decode-backend trtllm \ --nsa-prefill-backend trtllm --moe-runner-backend flashinfer_cutlass \ --enable-piecewise-cuda-graph --disable-radix-cache \ --num-continuous-decode-steps 16 \ --host 0.0.0.0 --port 8000 SCRIPT chmod +x /root/run_tp8_piecewise.sh && echo "Script created"' ``

>

Script created

At first glance, this is a routine operation: write a shell script to a remote machine via SSH heredoc, then mark it executable. But every element of this command carries meaning.

The Reasoning and Decision-Making Process

The assistant's decision to create a standalone launch script rather than running the server command inline reveals a deliberate design choice. A script file is reproducible, debuggable, and reusable. If the server crashes, the same script can be re-run without reconstructing the command. If the configuration needs to be modified, the script can be edited. This is engineering hygiene—the kind of practice that separates ad-hoc experimentation from systematic research.

The choice of flags in the script tells a deeper story. The assistant kept all the flags from the previous working configuration—tensor parallelism size 8 (--tp-size 8), FlashInfer attention backend, CUTLASS FP8 GEMM backend, TRTLLM for NSA decode and prefill, FlashInfer CUTLASS for the MoE runner, and 16 continuous decode steps. Only two flags changed: --enable-piecewise-cuda-graph was added, and --disable-radix-cache was introduced alongside it. This is the classic scientific method applied to systems optimization: change one variable at a time, hold everything else constant. By keeping the configuration identical except for the experimental flag, any difference in benchmark results can be attributed to the piecewise CUDA graphs feature with reasonable confidence.

The addition of --disable-radix-cache is itself a reasoned decision. Radix cache is a SGLang feature that caches KV blocks by their prefix, allowing efficient sharing across requests with common prefixes. However, it dynamically changes the shapes of KV cache tensors during execution. Piecewise CUDA graphs, which capture GPU kernel launches into reusable graph structures, require fixed or predictable tensor shapes. The two features are fundamentally incompatible—radix cache's dynamic shape changes would break the graph's assumptions. The assistant understood this and preemptively disabled radix cache, avoiding a likely runtime error.

Assumptions Embedded in the Message

This message rests on several critical assumptions, some of which would prove incorrect.

First, the assistant assumed that piecewise CUDA graphs would be compatible with the rest of the software stack. The FlashInfer FP4 quantization code, the TRTLLM backend, and the CUTLASS MoE runner all interact in complex ways. The --enable-piecewise-cuda-graph flag was designed for standard CUDA graph capture, but the MoE architecture's variable token-to-expert routing is precisely why the server was running with --disable-cuda-graph in the first place. Piecewise CUDA graphs were supposed to solve this by capturing subgraphs that could be replayed independently, but the assumption that they would work seamlessly with the FP4 JIT-compiled kernels was untested.

Second, the assistant assumed the server environment was stable and ready. The preceding struggle to kill the old server processes (messages [msg 969]–[msg 974]) should have been a warning sign. Process management on this system was unreliable—processes resisted termination, PIDs persisted, and the ps output was confusing. A system where basic process lifecycle is unreliable is one where more subtle failures are likely.

Third, the assistant assumed that the expected 10–20% improvement was plausible. This expectation came from the improvement document, which was written before any empirical testing. The assumption was that CUDA graph capture would reduce kernel launch overhead and improve GPU utilization, but this ignored the fundamental constraint of the Blackwell SM120 architecture: 99KB of shared memory, no TMEM support, and limited FP4 throughput for small matrices. As later analysis would reveal, the bottleneck was not kernel launch overhead but memory-bandwidth-bound per-expert GEMMs.

The Outcome: What Actually Happened

The chunk summary for this segment reveals that piecewise CUDA graphs were blocked entirely. The torch.compile(fullgraph=True) requirement, which is part of the piecewise CUDA graph implementation, was incompatible with FlashInfer's FP4 JIT code. Even after the assistant attempted workarounds—patching get_cuda_version to avoid subprocess calls and adding @torch.compiler.disable to the fp4_quantize function—the fullgraph constraint prevented any graph breaks. The experiment failed not because the concept was wrong, but because the software stack had a hard incompatibility that no amount of flag-flipping could resolve.

This outcome validates the assistant's methodology. The experiment was clean, the configuration was controlled, and the failure mode was clear and diagnosable. The assistant did not waste time debugging mysterious performance regressions—it immediately identified the root cause (fullgraph incompatibility) and pivoted to the next Tier 1 test (MSCCLPP). This is the hallmark of a well-designed experimental pipeline.

Input Knowledge Required

To fully understand this message, a reader needs knowledge spanning several domains. One must understand SGLang's server architecture—what flags like --tp-size, --moe-runner-backend, and --num-continuous-decode-steps control, and how they interact. One must understand CUDA graphs—why standard CUDA graphs fail for MoE models (fixed tensor shapes vs. variable routing) and how piecewise CUDA graphs attempt to solve this. One must understand the Blackwell SM120 architecture—its shared memory constraints, its lack of TMEM, and the implications for FP4 GEMM kernel efficiency. One must understand the model itself—GLM-5-NVFP4 is a Mixture-of-Experts model with 64 experts, meaning per-expert GEMMs are small and memory-bandwidth-bound. And one must understand the experimental methodology—the tiered optimization taxonomy (Tier 1 = flag flips, Tier 2 = code changes, Tier 3 = kernel development) and the importance of baseline benchmarking.

Output Knowledge Created

This message created a reproducible artifact: the launch script at /root/run_tp8_piecewise.sh on the remote machine. This script documents the exact configuration tested, serving as both an execution tool and a record for posterity. It also created negative knowledge: the understanding that piecewise CUDA graphs, despite their theoretical promise, are blocked by a torch.compile/FlashInfer incompatibility on this hardware stack. This negative result is valuable—it saves future researchers from pursuing the same dead end.

More subtly, the message created methodological knowledge. The assistant's approach—baseline first, change one variable, document everything—is a template for GPU inference optimization. The careful server lifecycle management, the use of standalone scripts, and the preemptive disabling of incompatible features all represent best practices that the message implicitly teaches.

Conclusion

Message [msg 975] is a study in the art of experimental systems research. It is the moment when hypothesis meets reality, when documentation gives way to execution, and when assumptions are put to the test. The message itself is simple—a shell script written to a remote server—but the reasoning behind it is layered and deep. The assistant understood the software stack, anticipated incompatibilities, controlled for confounding variables, and prepared for a clean experiment. When the experiment failed (as it would in the subsequent messages), the failure was informative rather than confusing. In the high-stakes world of LLM inference optimization, where every percentage point of throughput improvement translates to real economic value, this kind of disciplined methodology is not just good practice—it is the difference between wandering in the dark and systematically illuminating the path forward.