Taming the Memory Beast: Diagnosing a CUDA OOM During MTP Speculative Decoding Graph Capture

In the high-stakes world of large language model deployment on cutting-edge hardware, the difference between a working system and a crashed one often comes down to a single number: the memory fraction. Message [msg 12477] captures a pivotal moment in an intensive optimization campaign—the instant when a promising speculative decoding configuration crashes with a CUDA out-of-memory error, and the assistant must diagnose the root cause, calculate a fix, and relaunch. This message, though brief in execution, is dense with reasoning about GPU memory budgeting, CUDA graph capture mechanics, and the hidden costs of speculative decoding that only surface at deployment time.

The Broader Campaign: Chasing Throughput on Blackwell

To understand the significance of this message, one must appreciate the context of the larger effort. The assistant had been systematically optimizing DeepSeek-V4-Flash on 8× RTX PRO 6000 Blackwell GPUs (sm_120 architecture) across multiple segments spanning days of work. The target was ambitious: ~1000 tok/s throughput. The reality was sobering: the baseline deployment achieved only ~23 tok/s at concurrency 16, bottlenecked by sm_120 fallback kernels for sparse MLA attention and MXFP4 MoE execution that ran on CUDA cores instead of tensor cores.

The optimization campaign had already exhausted many levers. FP8 block-GEMM autotuning consumed 27 minutes of GPU time but delivered only a ~6% improvement at single-request throughput—because the FP8 matmul accounted for just 6% of decode time. NCCL protocol tuning (LL/Ring) had negligible effect since communication was only 2% of the profile. The real bottleneck, confirmed by GPU profiling, was the _tiled_sparse_decode_kernel consuming 63% of decode time, a Triton fallback that launched only 64 blocks on ~170 SMs.

Enter MTP (Multi-Token Prediction) speculative decoding, also called EAGLE in the SGLang framework. The idea is elegant: use a lightweight NextN draft head (a single transformer layer) to predict multiple future tokens in a single forward pass, then verify them against the full model in parallel. If the draft tokens are accepted at a high rate, the effective throughput increases because fewer full-model forward passes are needed per output token. The assistant had prepared two configurations: Config A (base-optimized, no MTP) and Config B (identical base + MTP/EAGLE with conservative parameters: num-steps=1, eagle-topk=1, num-draft-tokens=2).

Config A had been measured successfully, yielding the sobering ~23 tok/s baseline. Now it was Config B's turn. The MTP server was launched, the NextN draft head loaded successfully in 8.82 seconds, and then—silence. The server crashed.

The Crash: Reading the OOM Error

The previous message ([msg 12476]) had revealed the critical error:

torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 7.50 GiB. 
GPU 1 has a total capacity of 94.97 GiB of which 2.30 GiB is free. 
Including non-PyTorch memory, this process has 92.67 GiB memory in use.

This is a classic deployment failure mode. The model weights loaded fine. The NextN head loaded fine. But when the server attempted to capture CUDA graphs for the speculative decoding path, it ran out of memory with only 2.30 GiB free on GPU 1. The requested allocation of 7.50 GiB was impossible, and the process crashed.

The error message itself contains crucial diagnostic information: 92.67 GiB of memory already in use, leaving only 2.30 GiB free from a 94.97 GiB capacity. The memory fraction was set to 0.70, meaning SGLang reserved 70% of GPU memory (approximately 66.5 GiB) for its memory pool. The remaining 30% (28.5 GiB) was supposed to cover the model weights, CUDA graphs, and overhead. But the weights alone consumed approximately 36 GiB across 4 GPUs (about 9 GiB per GPU), and the CUDA graph capture for speculative decoding needed additional headroom that simply wasn't there.

The Reasoning: Why MTP Doubles Graph Memory

The assistant's reasoning in [msg 12477] demonstrates a sophisticated understanding of how SGLang's speculative decoding interacts with CUDA graph capture. The key insight is that speculative decoding with CUDA graphs requires capturing execution traces for both the draft path and the verify path, each at extended sequence lengths because the draft tokens effectively lengthen the context.

CUDA graphs are a performance optimization that captures a sequence of GPU kernel launches into a reusable graph object, bypassing the CPU launch overhead on subsequent executions. For speculative decoding, the system must capture graphs for:

1. The draft step: running the NextN head to generate draft tokens
2. The verify step: running the full model to verify those draft tokens in parallel Each of these steps needs graph entries for multiple batch sizes and sequence lengths. With --cuda-graph-max-bs 32 (the default in the original script), the system would attempt to capture graphs for batch sizes 1 through 32, for both draft and verify paths, at extended sequence lengths that include the draft tokens. This effectively doubles the memory footprint of the graph cache compared to the non-speculative case. The assistant correctly identified that the memory pressure came from this graph capture phase, not from model loading or inference itself. The NextN layer had loaded successfully (confirmed by the log: "Load weight end. elapsed=8.82 s, type=DeepseekV4ForCausalLMNextN"), but the subsequent graph capture exhausted the available memory.

The Decision: A Calculated Trade-Off

The fix required a careful balance of competing constraints. The assistant considered two parameters:

1. Memory fraction (--mem-fraction-static): Lowering from 0.70 to 0.60 would free approximately 9.5 GiB per GPU (10% of 94.97 GiB). This reduces the KV cache pool size but provides headroom for graph capture. The assistant judged this acceptable for short-context benchmarking (256-token input, 256-token output).

2. CUDA graph max batch size (--cuda-graph-max-bs): Reducing from 32 to 16 halves the number of graph entries to capture, directly reducing memory consumption. The assistant noted this still covers the C=16 concurrency level needed for the benchmark.

The mental math is instructive:

• Total GPU memory: ~95 GiB per GPU
• At 0.70 fraction: ~66.5 GiB reserved for SGLang pool, ~28.5 GiB for weights + graphs + overhead
• At 0.60 fraction: ~57 GiB reserved for SGLang pool, ~38 GiB for weights + graphs + overhead
• Weights: ~36 GiB total across 4 GPUs ≈ 9 GiB per GPU
• Available for graphs at 0.60: ~38 GiB - 9 GiB = ~29 GiB per GPU
• Estimated graph memory needed: ~20 GiB (based on the OOM showing 7.5 GiB allocation failing with only 2.3 GiB free, implying the total graph memory was near the ~20 GiB range) The assistant explicitly considered the alternative of only reducing cuda-graph-max-bs without changing the memory fraction, but correctly concluded that both adjustments were needed: the memory fraction frees general headroom, while the max-bs cap limits the worst-case graph memory consumption.

Assumptions and Potential Pitfalls

Every deployment fix rests on assumptions, and this one is no exception. The assistant assumed that:

• The KV cache pool at 0.60 fraction is sufficient for the benchmark workloads (256-token input, 256-token output, up to C=16 concurrency). This is a reasonable assumption for short-context serving, but would likely fail for long-context scenarios (e.g., the 200k context-length deployments seen in earlier segments).
• The CUDA graph memory estimate of ~20 GiB is accurate. This was derived indirectly from the OOM error (which showed 2.3 GiB free when trying to allocate 7.5 GiB, implying the graph capture was near completion but needed one more allocation). If the actual graph memory requirement is higher, the fix might still fail.
• cuda-graph-max-bs=16 covers the C=16 measurement. This is correct for the benchmark script, which sends at most 16 concurrent requests. However, if the benchmark's internal scheduling creates temporary spikes above 16, the server might fall back to non-graphed execution paths, reducing performance.
• The weights are approximately 36 GiB. This is an estimate for DeepSeek-V4-Flash, which is a quantized model (FP4/nvfp4). If the actual weight memory is higher, the headroom calculation would be off. There's also an implicit assumption that the MTP configuration itself is correct. The assistant had confirmed the MTP recipe from the AMD test file (--speculative-algorithm EAGLE --speculative-num-steps 1 --speculative-eagle-topk 1 --speculative-num-draft-tokens 2) and verified that the NextN head auto-loads from the base model checkpoint. But the interaction between MTP and CUDA graphs at this specific configuration had never been tested on Blackwell hardware before—hence the crash.

Input Knowledge Required

To fully understand this message, one needs:

• CUDA graph mechanics: Understanding that CUDA graphs capture kernel launch sequences and consume GPU memory proportional to the number of captured configurations (batch sizes × sequence lengths × execution paths).
• SGLang's memory management: The --mem-fraction-static parameter controls what fraction of GPU memory SGLang reserves for its KV cache and internal pools. The remainder is used for model weights, CUDA graphs, and any other allocations.
• Speculative decoding architecture: The distinction between the draft model (NextN head) and the base model, and the fact that both participate in the decode path. The draft model generates candidate tokens, and the base model verifies them in parallel.
• Blackwell GPU memory capacity: The RTX PRO 6000 has 96 GiB of VRAM, but the usable capacity reported by PyTorch is 94.97 GiB due to reserved memory for driver and runtime.
• The model's memory footprint: DeepSeek-V4-Flash is a quantized mixture-of-experts model. The assistant had previously determined its weight size through deployment experience.

Output Knowledge Created

This message produces several valuable artifacts:

1. A corrected launch script (serve_dsv4_mtp.sh) with the adjusted memory fraction and max-bs parameters, ready for benchmarking.
2. A documented failure mode: CUDA graph capture for speculative decoding on Blackwell GPUs requires additional memory headroom beyond the standard deployment. This is a concrete data point for future deployments.
3. A reusable diagnostic pattern: The assistant's reasoning chain—check that the model loaded, identify the crash phase (graph capture), calculate the memory budget, adjust parameters, and relaunch—is a template for debugging similar OOM issues.
4. Confirmation that the FP8 configs load correctly: The log output from the failed launch showed "dtype=fp8_w8a8,block_shape=[128, 128].json for W8A8 Block FP8 kernel," confirming that the earlier FP8 autotuning was producing correct configuration files.

The Thinking Process: A Window into Debugging Under Pressure

The assistant's reasoning in this message reveals a methodical, measurement-driven approach to debugging. The chain is:

1. Observe the symptom: The MTP server crashed during startup.
2. Read the error: CUDA OOM on GPU 1 during what appears to be graph capture.
3. Identify the phase: The NextN layer loaded successfully (8.82 seconds), so the crash is after model loading, during CUDA graph initialization.
4. Hypothesize the root cause: Speculative decoding requires graphs for both draft and verify paths, doubling memory requirements.
5. Calculate the fix: Lower memory fraction from 0.70 to 0.60 (frees ~9.5 GiB), cap max-bs at 16 (reduces graph count).
6. Validate assumptions: Check that the KV pool at 0.60 is sufficient for short contexts, and that max-bs=16 covers the C=16 benchmark.
7. Execute: Rewrite the script, verify GPUs are free, relaunch. What's notable is what the assistant does not do: it doesn't blindly increase the memory fraction (which would make the problem worse), it doesn't disable CUDA graphs entirely (which would hurt performance), and it doesn't reduce the MTP parameters (which would change the experimental configuration). Instead, it finds the minimal adjustment that preserves the experimental design while fixing the memory issue.

Broader Significance

This message exemplifies a recurring theme in the larger session: the gap between what should work in theory and what actually works on novel hardware. The Blackwell sm_120 architecture, being relatively new, lacks the optimized kernel libraries (cuDNN, cuBLAS) that make deployment predictable on older architectures. Every configuration parameter becomes a potential failure point, and every optimization introduces new constraints.

The memory fraction adjustment here is a microcosm of the entire optimization campaign: a series of trade-offs where each gain in one dimension (throughput via MTP) creates pressure in another dimension (memory for graph capture). The assistant's role is to navigate these trade-offs systematically, measuring at each step and adjusting based on empirical evidence rather than intuition.

The fix also highlights the importance of understanding the full lifecycle of a serving system. Model loading, graph capture, and inference each have different memory profiles. A configuration that works for inference may fail during graph capture, and vice versa. The memory fraction must be set to accommodate the peak memory phase, not just the steady-state inference phase.

Conclusion

Message [msg 12477] captures a critical moment of diagnosis and recovery in a complex deployment campaign. Faced with a CUDA OOM crash during MTP speculative decoding graph capture, the assistant correctly identified the root cause—doubled memory requirements from draft and verify graph paths—and applied a calibrated fix: lowering the memory fraction from 0.70 to 0.60 and capping the CUDA graph batch size at 16. The reasoning demonstrates deep knowledge of GPU memory budgeting, CUDA graph mechanics, and the SGLang serving stack, all applied under the pressure of a failed deployment.

The fix itself is elegant in its minimalism: it changes only two parameters, preserves the experimental configuration for benchmarking, and is grounded in a clear mental model of where the memory is going. Whether the fix succeeds or fails will be determined in the next message, but the diagnostic process itself is a masterclass in systematic debugging of large model deployments.