Chunk 42.0
## Summary This chunk focused on deploying **Qwen3.5-122B-A10B-FP8** across **2× NVIDIA GB10 DGX Spark** systems (SM121 Blackwell, ARM Cortex-X925, 120GB unified memory each, InfiniBand RoCE interconnect). The assistant explored both nodes, found an existing Docker-based vLLM setup running GLM-4.7-Flash, stopped it to free GPU memory, downloaded the 119GB FP8 model from HuggingFace, and rsynced it to the second Spark at ~640MB/s over the IB link. After discovering that SGLang's official spark image lacked Qwen3.5 support and its multi-node NCCL initialization hung indefinitely, the assistant pivoted to the `hellohal2064/vllm-qwen3.5-gb10` image (vLLM 0.17.1rc1, specifically built for Qwen3.5 on GB10) and built a Ray-based multi-node deployment. The multi-node setup required solving several networking and resource management challenges: Ray's auto-detection used the external IP (10.1.230.180) which was unreachable from the second Spark, so the assistant forced node IPs to the IB subnet (192.168.200.x) with `--node-ip-address` and set `GLOO_SOCKET_IFNAME`/`NCCL_SOCKET_IFNAME` to the correct RoCE interface. Ray's OOM killer (95% memory threshold) killed the process during CUDA graph capture on the head node, requiring `RAY_memory_monitor_refresh_ms=0` and reduced `--gpu-memory-utilization`. The final deployment uses NCCL over `NET/IBext_v11` for inter-node tensor parallelism (TP=2), achieving ~27 tok/s single-request throughput with correct reasoning output and tool calling enabled. The user then requested a relaunch, but both the Proxmox RTX PRO 6000 setup and the Spark containers were unreachable (network issue and reboot respectively), leaving the Spark services running the old GLM containers instead.
The Dual DGX Spark Deployment: From Reconnaissance to Multi-Node Inference on Blackwell ARM
Message Articles
- The Postmortem That Defined a Boundary: Why Blackwell GPUs Cannot Cross the IOMMU Translation Barrier
- The Weight of Fifteen Words: Trust, Autonomy, and the Handoff Signal in AI Collaboration
- The Pivot Point: Assessing a Stable Deployment Before Pursuing Low-Hanging Fruit
- The Pause Before Optimization: A Status Check at the Inflection Point
- The Planning Pivot: How a Structured Todo List Guided the Next Phase of LLM Inference Optimization
- The Baseline Imperative: Why a Seasoned Engineer Benchmarks Before Optimizing
- The Baseline Benchmark: A File Read That Anchors an Optimization Campaign
- The Baseline Imperative: Why a Simple File Copy Reveals the Soul of Rigorous Engineering
- The Baseline Benchmark: Measuring Before Optimizing
- The Confirmation That Unlocks Optimization: A Deep Dive Into a Single Transition Message
- The Pivot to Codebase Investigation: How One Task Call Unlocked SGLang's Hidden Optimizations
- The Moment of Verification: Checking Model Configuration Before Optimization
- The Threshold Decision: Systematic Optimization Through Flag Compatibility Analysis
- The Art of Optimization Triage: How One Message Transforms Research into Action
- The Moment of Convergence: Applying Optimization Flags to a Production SGLang Deployment
- The Deployment Pivot: From Research to Action in a Single SCP Command
- The Moment of Activation: Applying Optimizations to a Production SGLang Deployment
- The Verification Checkpoint: Confirming Service Health After Optimization Flag Deployment
- The Moment of Verification: Waiting for Optimization Flags to Prove Themselves
- The Diagnostic Pivot: When Optimization Flags Reveal Hidden Trade-offs in LLM Inference
- The Optimization That Wasn't: Diagnosing Fused MoE and Allreduce Fusion on Blackwell GPUs
- When Optimizations Fail Silently: Debugging SM120 Compatibility in SGLang's Allreduce Fusion
- The Dead-End Optimization That Revealed a Hidden Cost: Diagnosing SM120 Incompatibility in SGLang's FlashInfer AllReduce Fusion
- The Art of the Rollback: A Case Study in ML Infrastructure Optimization
- The Undoing: How a Failed Optimization Was Rolled Back in a Single Command
- The Pivot to MoE Kernel Autotuning: A Strategic Optimization Decision in Blackwell Inference
- The Missing Triton Config: A Detective Story in MoE Kernel Optimization
- The Triton Version That Wasn't There: A Detective Story in One Command
- The Missing Config: A Pivot Point in SGLang MoE Kernel Optimization
- The Art of Reverting: A Post-Mortem on a Failed Optimization
- The Diagnostic That Unraveled an Assumption: Verifying SGLang Configuration After a Failed Optimization
- The Case of the Missing Running Requests: Debugging SGLang Configuration on Blackwell GPUs
- The Art of Diagnostic Reasoning: Tracing a Performance Regression in SGLang
- The Art of the Single Hypothesis Test: Deploying a Service File to Isolate a Performance Regression
- The Diagnostic Pivot: Isolating a Performance Regression in SGLang's Blackwell Deployment
- The Pivot: When Optimization Flags Fail, MoE Kernel Tuning Beckons
- The Pivot to MoE Kernel Autotuning: A Strategic Optimization Decision
- Reading the Blueprint: How a Single `cat` Command Unlocked MoE Kernel Autotuning for Blackwell GPUs
- The Pivot Point: When Optimization Flags Fail, MoE Kernel Tuning Begins
- Reading the Tail: The Methodical Pursuit of MoE Kernel Autotuning
- The Pivot to MoE Kernel Autotuning: A Methodical Optimization Turn
- A Single `ls` Command: The Pivot Point in MoE Kernel Optimization
- The Art of the Targeted Grep: How a Single Bash Command Uncovered the MoE Kernel Tuning Pipeline
- The Config File Naming Convention: A Moment of Methodical Verification in MoE Kernel Tuning
- The Turning Point: MoE Kernel Autotuning on Blackwell GPUs
- The Art of the Clean Slate: GPU Memory Reclamation in Multi-Host ML Infrastructure
- The Pivot Point: Examining the MoE Kernel Tuning Script
- The MoE Kernel Autotuning Gambit: A Decision Under Uncertainty
- Debugging the MoE Kernel Autotuner: A Deep Dive into Model Config Extraction
- The Architecture Before the Redirect: Debugging a Config Extraction Bug in SGLang's MoE Kernel Autotuning
- Patching the MoE Kernel Autotuner: A Surgical Fix for Qwen3.5's Nested Config Architecture
- The Critical Patch: Fixing MoE Kernel Autotuning for Blackwell GPUs
- The 10-Minute Threshold: A Pivot Point in MoE Kernel Autotuning
- The Clean Slate: A Single Bash Command That Resets an ML Tuning Pipeline
- The Pragmatic Pivot: When Brute-Force Autotuning Meets Engineering Reality
- Strategic Pivot: Transferring MoE Kernel Configs Between Blackwell Generations
- The Critical Verification: Why a One-Line GPU Name Check Saved Hours of MoE Tuning
- The Underscore That Makes or Breaks a Config: Precision Verification in MoE Kernel Tuning
- The Verification That Saved a Silent Failure: Tracing a Single Bash Command in SGLang MoE Tuning
- The Pragmatic Pivot: Borrowing B200 MoE Kernel Configs for RTX PRO 6000 Blackwell
- The Verification Checkpoint: Confirming a Server Restart with Adapted MoE Kernel Configurations
- The Silent Verification: How a Single grep Confirmed — and Contradicted — a MoE Kernel Optimization
- The Quiet Verification: A Single Grep That Confirmed a MoE Kernel Optimization
- The Pragmatic Autotuner: Borrowing B200 MoE Kernel Configs for RTX PRO 6000 Blackwell
- The Autotuning Dead End: When Borrowing GPU Kernel Configs Falls Flat
- The Investigation That Revealed Nothing Was Wrong: Decoding SGLang's Default MoE Kernel Configuration
- The Art of Knowing When to Pivot: A Deep Dive into MoE Kernel Analysis
- The Pivot That Paid Off: Tuning Speculative Decoding for Qwen3.5-122B on Blackwell GPUs
- The Cleanup Before the Pivot: A Single Bash Command's Role in ML Infrastructure Tuning
- The Preparatory Read: Understanding a Systemd Service File in an ML Inference Pipeline
- The Pivot That Doubled Throughput: One Edit to Unlock Speculative Decoding's Full Potential
- The Deployment That Unlocked Performance: A Single SCP Command That Changed Everything
- The Art of Verification: A 100-Second Wait That Confirms a Production ML Deployment
- The Verification That Almost Wasn't: A Single grep and the Art of Disciplined Optimization
- The Speculation Depth Tuning: When Two Steps Beat One in Multi-Token Prediction
- The 51% Speculative Decoding Breakthrough: When One Parameter Change Transforms Throughput
- The Cleanup Before the Leap: A Methodological Bridge in Speculative Decoding Optimization
- The Pivot Point: One Edit That Unlocked 90% Throughput Improvement
- The Deployment Gesture: A Single Command in a Speculative Decoding Optimization Loop
- The Verification Step: Incremental Speculative Decoding Tuning on Qwen3.5-122B
- The Speculation Depth Trade-Off: Pushing EAGLE MTP to Three Steps on Blackwell
- The Power of Speculative Depth: Benchmarking MTP Steps on Qwen3.5-122B
- The Hidden Art of Cleanup: A Single Bash Command in the Service of Speculative Decoding Optimization
- The Art of Incremental Optimization: A Single Edit That Embodies Iterative Tuning
- The Art of Incremental Optimization: Deploying Speculative Decoding Step 4
- The Verification Step: A Single Curl in an Optimization Loop
- The Power of Speculation: Pushing Multi-Token Prediction to Its Limits on Blackwell
- The Speculative Decoding Tuning Frontier: Pushing Qwen3.5-122B to 277 tok/s
- The Art of Speculative Decoding Tuning: Pushing to Steps=5
- The Art of Systematic Optimization: A Single Deployment Command in a Speculative Decoding Tuning Campaign
- The Quiet Checkpoint: A Single Curl Command in the Pursuit of Optimal Speculation
- The Diminishing Returns of Speculation: Benchmarking Steps=5 on Qwen3.5-122B
- The Art of Diminishing Returns: Tuning Speculative Decoding at the Frontier
- The Pivot Point: Checking Constraints Before Pushing Speculative Decoding Further
- The Plateau Question: A Single Line That Probes the Limits of Speculative Decoding
- Probing the Upper Bound: The Quest for Optimal Speculation Depth in EAGLE-3 Decoding
- The Art of the Incremental Edit: Tuning Speculative Decoding Depth in SGLang
- The Art of the Deployment Command: A Single Line That Launches an Experiment
- The 100-Second Wait: A Health Check in the Service of Speculation Tuning
- The Silent Crash: Understanding a Two-Word Status Update in a High-Stakes ML Deployment
- The Status Check: A Diagnostic Pivot in Speculative Decoding Tuning
- When Speculation Goes Too Far: Diagnosing a Crash in SGLang's EAGLE Speculative Decoding
- The Moment of Discovery: Unraveling a Crash That Wasn't
- The Auto-Restart Signal: A Diagnostic Pivot in Speculative Decoding Tuning
- When Speculation Goes Too Far: Diagnosing an OOM Crash in SGLang's EAGLE Decoding
- Reading the Crash Logs: A Lesson in Diagnostic Discipline
- The Systemd SIGKILL That Wasn't an OOM: Debugging Speculative Decoding Deployment
- The Patience of Benchmarking: Waiting for a Server at the Edge of Speculation
- The Breath-Holding Moment: A Systemd Status Check That Tells a Deeper Story
- Breaking the Restart Loop: A Moment of Debugging Clarity in High-Stakes ML Deployment
- The Nuclear Cleanup: Restoring GPU Access After a Restart Loop
- The Art of Recovery: A Moment of Clarity After GPU Restart Loops
- The Art of Waiting: A 120-Second Sleep as a Debugging Milestone
- The Two-Word Bug Report: "crashing in a loop"
- The Weight of Three Words: "Crashing in a Loop"
- The Diagnostic Pause: Reading Systemd Status in a Crash Loop
- The Diagnostic Pivot: Reading the Crash Logs in a Restart Loop
- The Speculation Ceiling: Diagnosing an OOM Crash Loop and Making Data-Driven Decisions in LLM Inference Optimization
- The Decisive Edit: How a Single File Change Crystallized Hours of Speculation Tuning
- The Deployment That Almost Wasn't: Recovering from OOM to Lock In the Optimal Speculation Depth
- The Verification That Broke the Crash Loop: A Single Curl That Restored Order
- The Speculation Tuning That Doubled Inference Speed: A Deep Dive into EAGLE-3 Optimization
- The Meta-Debugger: When a User Questions the Tool Call Parser
- When Tool Calls Break: Debugging Parser Mismatches in Multi-Model Inference Stacks
- Diagnosing the Wrong Layer: How an AI Assistant Correctly Identified a Client-Side Tool Call Parsing Error
- The Pivot: Deploying Qwen3.5 Across Two NVIDIA DGX Sparks
- The Mission Brief: Deploying Qwen3.5 Across Two DGX Sparks
- Reconnaissance Before Deployment: Exploring Dual DGX Spark Systems for Multi-Node Inference
- Reconnaissance Before Deployment: Mapping the DGX Spark Battlefield
- The Reconnaissance Phase: Gathering Intelligence Before Multi-Node DGX Spark Deployment
- The Synthesis Moment: Orchestrating Multi-Node Inference Across Two DGX Sparks
- The Art of Infrastructure Archaeology: Deploying Qwen3.5 Across Dual DGX Sparks
Subagent Sessions
- Tracing the Optimization Frontier: A Systematic Exploration of SGLang's MoE Flags
- The Archaeology of Inference: Tracing Truth Through SGLang's Configuration Codebase
- The Blackwell Compatibility Audit: Tracing SM120 Support Through SGLang's Codebase
- The Art of MoE Kernel Autotuning: A Systematic Exploration of SGLang's Fused MoE Triton Infrastructure
- Mapping Parser to Model: How an AI Assistant Systematically Matched SGLang's Tool Call Parsers to Qwen3.5's Native Format