The Clean Slate: A Pivotal Restart in the DeepSeek-V4-Flash Optimization Campaign

In the high-stakes world of large language model inference optimization, few moments are as decisive as the clean restart. After a model has been deployed with suboptimal settings, benchmarked at a fraction of its potential throughput, and subjected to a cascade of failed kill commands and stale process trees, the ability to wipe the slate clean and relaunch with proven optimizations is the difference between spinning wheels and making progress. Message 12405 in this opencode session captures exactly that moment: the assistant, after a multi-message struggle to terminate lingering prefill-decode disaggregation (PD) processes, finally achieves a clean GPU state and writes the optimized server script that will define the next phase of benchmarking.

The Message

The message itself is deceptively brief. It opens with the assistant's reasoning:

The launch_server processes are done, so GPUs should be freeing up. I'll verify they're clear, then write out the serve_dsv4_tp4.sh script with NCCL configuration sourced from nccl_env.sh, and get it running.

Then, a direct action statement:

PD killed (0 processes). Now write the optimized single-node script and verify GPUs are freeing.

Followed by a bash command that writes the server script, makes it executable, and checks GPU memory:

timeout 30 ssh -o StrictHostKeyChecking=no root@10.1.230.171 'cat > /root/serve_dsv4_tp4.sh <<"EOF"
#!/bin/bash
source /root/dsv4_nccl_env.sh
exec /root/venv_sglang211/bin/python -m sglang.launch_server \
  --model-path /root/models/DeepSeek-V4-Flash --trust-remote-code \
  --tp 4 --moe-runner-backend marlin \
  --mem-fraction-static 0.85 \
  --cuda-graph-max-bs 32 \
  --num-continuous-decode-steps 4 \
  --host 127.0.0.1 --port 30000
EOF
chmod +x /root/serve_dsv4_tp4.sh
echo "script written"; nvidia-smi --query-gpu=memory.used --format=csv,noheader | tr "\n" " "'

The output confirms success:

script written
4 MiB 4 MiB 4 MiB 4 MiB 4 MiB 4 MiB 4 MiB 4 MiB

The Long Road to a Clean Kill

To understand why this message matters, one must appreciate the struggle that preceded it. The assistant had been trying to kill the PD servers for several messages. In [msg 12398], the assistant attempted to kill processes using pkill -f &#34;sglang_router.launch_router&#34; and pkill -f &#34;disaggregation-mode&#34;, but the command chain got truncated during the SSH heredoc, leaving the PD servers alive and the single-node script unwritten. In [msg 12402], the assistant tried pkill -9 -f &#34;launch_server.*DeepSeek-V4&#34; but again received no output, and subsequent checks in [msg 12403] showed the processes were still running with GPUs fully occupied at 71,876 MiB each. Finally, in [msg 12404], the assistant abandoned pattern matching and killed by explicit PID (kill -9 73216 73217), which succeeded.

This sequence reveals a critical lesson about remote process management: pattern-based killing with pkill -f can fail silently when the process command line contains special characters or when the SSH connection is unstable. The assistant's eventual pivot to explicit PID killing was the right call, but it cost several rounds of iteration. Message 12405 is the payoff: the GPUs show 4 MiB each — completely free, with no residual model allocations — confirming that the kill finally worked.

Why This Restart Matters

The script written in this message is not just any server launch. It represents the application of a complete optimization playbook that was painstakingly developed during the earlier Kimi K2.6 deployment. The key elements are:

NCCL LL+Ring PCIe tuning (sourced from dsv4_nccl_env.sh): The environment file, created in [msg 12398], contains a proven set of NCCL parameters including NCCL_PROTO=LL, NCCL_ALGO=Ring, NCCL_MIN_NCHANNELS=8, NCCL_MAX_NCHANNELS=16, NCCL_BUFFSIZE=16777216, and NCCL_NTHREADS=512. These settings were validated on the K2.6 deployment, which achieved 1,291 tok/s at TP8 on a larger model. The assistant's research (from the task in [msg 12396]) confirmed that the current dsv4 deployment had none of these optimizations — it was running with NCCL defaults over PCIe, which can be 5–10× worse for tensor-parallel all-reduce.

CUDA graphs (--cuda-graph-max-bs 32): The user explicitly emphasized CUDA graphs in [msg 12397], noting they delivered a 3.8× speedup on K2.6 decode. By setting a max batch size of 32, the assistant ensures that the C=16 benchmark workload will be fully captured in the graph, avoiding the fallback to eager execution that would destroy throughput.

Continuous decode steps (--num-continuous-decode-steps 4): This amortizes the per-step overhead of scheduling and kernel launches across multiple decode iterations, a proven technique for improving throughput at concurrency.

Memory fraction (--mem-fraction-static 0.85): Bumped from the default 0.70 to provide more headroom for KV cache at higher concurrency, though the assistant correctly notes that memory isn't the primary bottleneck — compute and communication are.

Marlin MoE backend (--moe-runner-backend marlin): The assistant had researched alternatives in [msg 12396] and found that on sm_120 (Blackwell), most backends hit shared-memory limits and fall back to fused_moe_triton. Marlin is the only safe option for FP4 experts that doesn't produce NaNs or require SM100 hardware.

The Assumptions Underlying This Message

Every optimization decision carries assumptions, and this message is built on several:

  1. That NCCL LL+Ring tuning will translate from K2.6 to DeepSeek-V4-Flash. The K2.6 model is larger and has different architecture (including MLA attention), so the NCCL gains may not be identical. However, the fundamental PCIe bottleneck — all-reduce communication for tensor parallelism — is the same, so the tuning should help.
  2. That marlin is the optimal MoE backend. The assistant's research concluded that alternatives either underperform or produce NaNs on sm_120, but this assumption would later be challenged when the NVFP4 checkpoint (introduced in chunk 1) routes MoE through tensor-core paths instead.
  3. That the environment file exists and is correct. The script sources dsv4_nccl_env.sh, which was created in an earlier truncated command. The assistant never verified its contents in this message, trusting that the earlier write succeeded.
  4. That single-node TP4 is the right configuration for initial benchmarking. The assistant chose to benchmark single-node TP4 before re-deploying PD disaggregation, reasoning that isolating the model's performance from PD complexity would give cleaner signal. This is a sound scientific approach.

What This Message Does Not Address

For all its importance as a restart point, this message does not resolve the fundamental performance mystery. The assistant is applying optimizations that were proven on K2.6, but DeepSeek-V4-Flash has different architectural characteristics — notably FP4 quantization and a different attention mechanism. The 40× gap between current throughput (~25 tok/s at C=16) and the user's expectation (~1,000 tok/s) cannot be closed by NCCL tuning and CUDA graphs alone. Later chunks reveal that the decisive bottlenecks are the sm_120 fallback kernels for sparse-MLA decode and MXFP4 MoE, which run on CUDA cores rather than tensor cores, and that even the NVFP4 checkpoint (which fixes the MoE path) only delivers ~28 tok/s.

The assistant does not yet know this. At this moment in the conversation, the assumption is that NCCL tuning and CUDA graphs will deliver the bulk of the improvement, and that MoE backend experimentation might fill the remaining gap. The clean restart is an act of faith in the optimization playbook.

The Thinking Process

The assistant's reasoning in this message is notably terse and action-oriented. There is no deep analysis, no exploration of alternatives, no consideration of edge cases. The thinking is:

  1. Verify the kill worked: "The launch_server processes are done, so GPUs should be freeing up."
  2. Plan the next steps: "I'll verify they're clear, then write out the serve_dsv4_tp4.sh script with NCCL configuration sourced from nccl_env.sh, and get it running."
  3. Execute: "PD killed (0 processes). Now write the optimized single-node script and verify GPUs are freeing." This is the thinking of an engineer who has already done the research, made the decisions, and is now executing a known-good plan. The brevity reflects confidence in the approach — the assistant has been through this exact optimization cycle before with K2.6 and knows what works. The only uncertainty was whether the kill would succeed, and once that's resolved, the execution is straightforward.

Input and Output Knowledge

Input knowledge required to understand this message includes: the NCCL tuning parameters for PCIe (LL protocol, Ring algorithm, channel/buffer sizes), the role of CUDA graphs in capturing decode iterations, the concept of continuous decode steps for amortizing overhead, the memory fraction setting for KV cache headroom, and the marlin MoE backend as the safe choice for FP4 on sm_120. Also required is the context of the preceding struggle to kill PD processes — without that, the relief of seeing "4 MiB" across all eight GPUs would be lost.

Output knowledge created by this message includes: a clean server script at /root/serve_dsv4_tp4.sh with NCCL optimization, confirmation that all 8 GPUs are free and ready for the new server, and a clear launch point for the next phase of benchmarking. The assistant can now proceed to launch the server and measure whether the optimizations deliver the expected throughput improvement.

Broader Significance

In the arc of the conversation, message 12405 is the pivot point between diagnosis and treatment. The preceding messages were about understanding the problem — researching optimizations, identifying the configuration gaps, and struggling with process management. This message marks the transition to execution: the optimizations are written, the GPUs are clean, and the server is ready to launch. Whether those optimizations will be sufficient remains to be seen, but the assistant has done the groundwork and is now in a position to measure the results.

The clean GPU state — 4 MiB on each of eight RTX PRO 6000 Blackwell GPUs — is a moment of possibility. Everything that follows in the optimization campaign will build on this restart, for better or worse.