Chunk 67.0
In this chunk, the assistant successfully deployed **DeepSeek-V4-Flash (FP4/nvfp4)** on SGLang main across 8× RTX PRO 6000 (sm_120). After stopping the previous K2.6 service, freeing disk, and downloading the 146 GB checkpoint, the assistant built a fresh SGLang environment with all required dependencies (flashinfer 0.6.12, sglang-kernel 0.4.3, tilelang, NIXL, etc.). The model loaded and generated correctly on a single-node TP4 configuration, and the headline deliverable—**prefill-decode disaggregation**—was achieved: prefill TP4 on GPU0-3/NUMA0, decode TP4 on GPU4-7/NUMA1, with KV transfer via NIXL/UCX and a router on port 8000, all verified end-to-end. However, performance fell far short of expectations: only ~10 tok/s at bs=1 and ~25 tok/s at C=16, versus the user's target of ~1000 tok/s. The assistant systematically exhausted every config lever—NCCL LL+Ring tuning, CUDA graphs (already on), tilelang indexer fusion (JIT-compile failure on sm_120), non-marlin MoE backends (invalid for FP4 experts), and expert parallelism (worse due to PCIe all-to-all overhead)—none moved the needle. A definitive GPU profile traced 63% of decode time to a single kernel: `_tiled_sparse_decode_kernel`, the sm_120 Triton fallback for sparse MLA attention, which launches only 64 blocks (1 batch × 64 heads) on ~170 SMs, serially iterating all 512 top-k tokens. This is the same low-occupancy pathology that plagued the earlier K2.6 verify kernel. The core theme is the **hard ceiling of sm_120 fallback kernels**: the fast fused DSA/MoE stack (DeepGEMM, trtllm-gen, FP4 indexer) is arch-gated to SM100, and no amount of config tuning or even a single-kernel rewrite (which could yield ~2-3× overall) can close the ~40× gap to 1000 tok/s. The assistant documented the precise bottleneck, quantified the ceiling, and concluded that reaching the user's throughput target on this hardware would require a multi-week custom kernel effort akin to the earlier K2.6 work. The PD disaggregation orchestration itself works correctly, but decode remains the bottleneck regardless of resource isolation.
Message Articles
- The Architecture of a Status Report: How a Native DDTree Inference Engine Took Shape on Blackwell GPUs
- The Pivot to DeepSeek-V4-Flash: A Strategic Experiment in Prefill-Decode Disaggregation
- The Pivot: From Custom DDTree Kernels to DeepSeek-V4-Flash PD Disaggregation
- The Intelligence-Gathering Phase: Parallel Reconnaissance Before a Complex Model Deployment
- The Architecture Pivot: How One Message Uncovered a Fundamental Compatibility Gap in Deploying DeepSeek-V4-Flash on SGLang
- The Pivot Point: How a Single Message Rescued a DeepSeek-V4 Deployment from a Wrong Turn
- The Pivot from Planning to Execution: Deploying DeepSeek-V4-Flash with PD Disaggregation on Blackwell GPUs
- The Reality Check: Verifying State Before Deployment in an ML Infrastructure Session
- The Art of the Long Pole: Orchestrating a 160-GB Model Download and Environment Probe for DeepSeek-V4-Flash on Blackwell
- Gathering Intel: The Critical Information-Gathering Phase Before Deploying DeepSeek-V4-Flash on Blackwell
- The Moment of Verification: Confirming DeepSeek-V4 Support in SGLang Main
- The Moment the Model Arrives: A Pivotal Transition in Deploying DeepSeek-V4-Flash on Blackwell
- The Pivot Point: From Download to Deployment on Blackwell
- Mapping the Unknown: The Reconnaissance Message That Defined DeepSeek-V4-Flash Deployment on Blackwell
- The Pivot Point: Synthesizing an sm_120 Deployment Strategy for DeepSeek-V4-Flash
- The Art of the Prerequisite: Cloning a Virtual Environment as a Strategic Foundation for DeepSeek-V4 on Blackwell
- The Dependency Dilemma: Orchestrating a Fragile Installation on Blackwell GPUs
- The Dependency Reconnaissance: How One Message Uncovered the Hidden Costs of Deploying DeepSeek-V4 on Blackwell
- The Dependency Tightrope: Deploying DeepSeek-V4-Flash on Blackwell Through Careful Package Management
- The Dry-Run That Paid Off: A Case Study in Dependency Risk Management for ML Infrastructure
- The Shebang Trap: How a Hardcoded Python Path Nearly Derailed a DeepSeek Deployment
- The Editable Install: A Critical Transition in Deploying DeepSeek-V4-Flash on Blackwell
- The Moment of Verification: When an Editable Install Fails Its First Test
- When Verification Reveals Hidden Faults: A Post-Install Sanity Check for SGLang on Blackwell GPUs
- The Ghost in the Package: Diagnosing a Shadowed Editable Install
- The Phantom Directory: Debugging Python Import Shadowing in a Multi-GPU ML Deployment
- The Moment of Truth: Launching DeepSeek-V4-Flash on Blackwell
- The First Smoke Test: Launching DeepSeek-V4-Flash on Blackwell GPUs
- The Waiting Game: Monitoring DeepSeek-V4-Flash's First Breath on Blackwell
- The Moment of Truth: Validating DeepSeek-V4-Flash on Blackwell GPUs
- The Calm Before the Storm: A Milestone Checkpoint on the Road to DeepSeek-V4-Flash Deployment
- The Baseline That Changed Everything: Benchmarking DeepSeek-V4-Flash on Blackwell sm_120
- The Moment of Reckoning: Accepting the sm_120 Ceiling on DeepSeek-V4-Flash
- The Honest Baseline: Diagnosing SM120 Performance Ceilings in the DeepSeek-V4-Flash Deployment
- The Gatekeeper: Validating NIXL for Prefill-Decode Disaggregation on Blackwell
- The Transitional Moment: Orchestrating Prefill-Decode Disaggregation for DeepSeek-V4-Flash on Blackwell
- The Transitional Pivot: Cleaning State and Installing the Router for PD Disaggregation