The Pivot: Launching SGLang with EAGLE-3 on SM120 Blackwell GPUs

In the sprawling, multi-week journey to deploy speculative decoding for the 1-trillion-parameter Kimi-K2.5 INT4 model, message [msg 3131] marks a critical inflection point. After investing enormous effort into building a complete EAGLE-3 training pipeline—generating 10,000 synthetic reasoning samples, extracting hidden states at 3,165 tok/s, producing 828 GB of training data, and fine-tuning a drafter over five epochs—the assistant had hit a devastating roadblock. When tested with vLLM's EAGLE-3 integration, both the custom-trained drafter and the pre-trained AQ-MedAI baseline achieved only ~15% acceptance rate, yielding a net throughput of 0.66×—worse than running without speculation at all. This was not a training quality problem; it was a fundamental integration issue with vLLM's handling of Multi-Head Latent Attention (MLA) hidden state extraction during decode.

The user's directive was clear: pivot to SGLang, which has first-class EAGLE-3 support and is explicitly tested with Kimi-K2 drafters. Message [msg 3131] is the moment that pivot becomes operational—the first SGLang server launch attempt with EAGLE-3 enabled.

The Context of Desperation and Hope

To understand the weight of this message, one must appreciate what preceded it. The assistant had spent roughly 200 messages (across segments 20–23) building an EAGLE-3 pipeline from scratch: patching the speculators library for vLLM 0.16 API compatibility, rewriting a custom worker for the DeepSeekV2 forward pass, fixing collective_rpc bugs, and scaling training from 10 samples to 1,000 to 10,000. The synthetic data generation alone took 5.3 hours. The fine-tuning took another 2.6 hours. And the payoff was a drafter that loaded successfully but made inference slower.

The user's pivot to SGLang was not a casual suggestion—it was a strategic redirection born from evidence. SGLang's documentation explicitly claims first-class EAGLE-3 support with Kimi-K2 drafters, and the SGLang codebase had been verified to include SM120 (Blackwell) CUDA architecture support in its kernel compilation targets. The assistant had just spent messages [msg 3098] through [msg 3130]—over 30 messages—building sgl-kernel for SM120 from source, a process that took 48 minutes and required installing libnuma-dev, working around CMake compatibility issues, and discovering that the editable install didn't actually compile CUDA code (requiring a non-editable reinstall). The final verification showed sgl_kernel loaded successfully on SM120, and SGLang imported without errors.

The Launch Command: Anatomy of a Bet

The command issued in [msg 3131] is deceptively simple for the complexity it represents:

nohup /root/ml-env/bin/python3 -m sglang.launch_server \
    --model-path /shared/kimi-k2.5-int4 \
    --trust-remote-code \
    --tp-size 8 \
    --mem-fraction-static 0.90 \
    --host 0.0.0.0 --port 8000 \
    --speculative-algorithm EAGLE3 \
    --speculative-draft-model-path /data/eagle3/aq-medai-k2-drafter \
    --speculative-num-steps 3 \
    --speculative-eagle-topk 1 \
    --speculative-num-draft-tokens 4 \
    > /data/eagle3/synth_10k/sglang_eagle3_aqmedai.log 2>&1 &

Every parameter encodes a decision shaped by the preceding weeks of work. The --tp-size 8 reflects the hardware reality of eight RTX PRO 6000 Blackwell GPUs connected via PCIe—a topology that had already been identified as the dominant bottleneck in earlier profiling (AllReduce consuming 51.5% of decode time). The --mem-fraction-static 0.90 is an aggressive allocation, reserving 90% of GPU memory for the model weights and KV cache, leaving only 10% for activations and overhead—a gamble that assumes the 547 GB model will fit within the 8 × 96 GB = 768 GB available.

The speculative decoding parameters tell a story of conservative optimization: --speculative-num-steps 3 means the drafter generates three tokens per speculation step, --speculative-eagle-topk 1 restricts the tree to a single candidate path (the simplest possible tree), and --speculative-num-draft-tokens 4 limits the total draft length. These are the same parameters that had been tested with vLLM, allowing for a clean comparison of the inference engine rather than the speculation strategy.

Crucially, the assistant chose the AQ-MedAI pre-trained drafter (/data/eagle3/aq-medai-k2-drafter) rather than the custom-trained drafter from the 10,000-sample pipeline. This was a deliberate baseline decision: if the AQ-MedAI drafter—which had been explicitly designed and tested for Kimi-K2.5—also failed on SGLang, then the problem was with the drafter architecture itself rather than the training data quality. If it succeeded, the assistant could then test the custom-trained drafter to see if additional fine-tuning improved acceptance rates.

Assumptions Embedded in the Launch

The message carries several implicit assumptions, some of which would prove incorrect. The assistant assumed that SGLang's EAGLE-3 integration would work on SM120 hardware without modification, since sgl_kernel loaded successfully and the CMake build system explicitly included compute_120a,code=sm_120a targets. It assumed that the AQ-MedAI drafter's architecture was compatible with SGLang's EAGLE-3 implementation—a reasonable assumption given SGLang's documentation, but one that had not been tested. It assumed that the server would complete its initialization sequence (weight loading, CUDA graph compilation, warmup) and begin serving requests within a reasonable timeframe.

The assistant also made a strategic choice to skip the no-spec-decode baseline test and go directly to EAGLE-3. The reasoning, visible in the preceding message [msg 3128], was that baseline performance numbers were already well-characterized from the vLLM deployment. This was a time-saving decision that prioritized the speculative decoding experiment over redundant baselining—but it meant that if SGLang failed to start, the root cause could be either the base SGLang SM120 compatibility or the EAGLE-3 integration specifically.

What the Message Creates

The immediate output of [msg 3131] is a process ID (46294) and a log file at /data/eagle3/synth_10k/sglang_eagle3_aqmedai.log. But the knowledge created extends far beyond these artifacts. This message initiates the first-ever test of SGLang's EAGLE-3 integration on SM120 Blackwell GPUs with a 1T-parameter INT4 model. The results—whether success or failure—would determine the entire trajectory of the speculative decoding effort.

The subsequent messages ([msg 3132] through [msg 3146]) reveal what happened: SGLang loaded the 64 safetensors shards in an astonishing 34 seconds (compared to ~25 minutes in vLLM), consuming 76 GB per GPU. But then the server went silent. Zero CPU utilization. Zero GPU utilization. No listening port on 8000. The process was alive but stuck—a deadlock rather than a crash. Each tensor-parallel worker was showing 425 GB in virtual memory (the VSZ column), but physical RAM usage was only 33 GB total. The system wasn't out of memory; the processes were simply sleeping, waiting on something that never completed.

The Deeper Significance

Message [msg 3131] represents a pattern that recurs throughout this session: the tension between theoretical compatibility and practical deployment. The SGLang codebase supported SM120. The sgl-kernel compiled for SM120. The EAGLE-3 integration was documented as compatible with Kimi-K2.5. Yet the actual runtime behavior revealed a silent deadlock that none of the build-time verifications could detect.

This message also illustrates the asymmetric effort distribution in ML infrastructure work. The 30+ preceding messages spent on building sgl-kernel—installing dependencies, debugging CMake version incompatibilities, killing OOM'd compilation processes, discovering that editable installs don't compile CUDA—all of that was necessary groundwork for a single nohup command. The launch itself takes one line. The debugging that follows would take dozens more.

For the reader following this session, [msg 3131] is the moment where hope meets reality. The pivot to SGLang was the right strategic decision given the evidence of vLLM's MLA integration failure. But the deadlock that followed would force the assistant into yet another debugging spiral—checking CUDA graph compilation, trying verbose logging, disabling optimizations, and ultimately questioning whether SM120 support in SGLang was truly production-ready for this specific model architecture.