The Negative Memory Mystery: Debugging SGLang's KV Cache Allocation on PCIe-Connected Blackwell GPUs

In message [msg 5175], the assistant executes a delayed bash command to inspect debug output from a freshly launched SGLang server, revealing a startling numerical finding: the KV cache allocation formula produces a negative rest memory value of −20.603 GiB, yet the baseline server (using NCCL allreduce) somehow succeeds with the same parameters. This single debug line crystallizes a week-long investigation into why custom allreduce on PCIe-connected 8×RTX PRO 6000 Blackwell GPUs fails while the NCCL-based baseline works, and it exposes a fundamental puzzle in SGLang's memory management that the assistant must resolve before speculative decoding can be made profitable.

The Message in Full

The assistant runs the following command after a 660-second (11-minute) delay to allow the server to fully initialize:

sleep 660 && ssh root@10.1.230.174 'grep "DEBUG profile_max_num_token\|Not enough memory\|KV Cache" /data/eagle3/synth_100k/logs/custom_ar_pcie_debug.log | head -20'

The output reveals:

[2026-02-27 12:40:05 TP5] DEBUG profile_max_num_token: avail=21.697 GiB, total=93.999 GiB, fraction=0.55, reserved=42.300 GiB, rest=-20.603 GiB, cell_size=70272, max_tokens=-314811
[2026-02-27 12:40:05 TP0] DEBUG profile_max_num_token: avail=21.697 GiB, total=93.999 GiB, fraction=0.55, reserved=42.300 GiB, rest=-20.603 GiB, cell_size=70272, max_tokens=-314811
[2026-02-27 12:40:05 TP2] DEBUG profile_max_num_token: avail=21.697 GiB, total=93.999 GiB, fraction=0.55, reserved=42.300 GiB, rest=-20.603 GiB, cell_size=70272, max_tokens=-314811

All tensor-parallelism ranks (TP0, TP2, TP5, etc.) report identical values, confirming the computation is deterministic across GPUs. The formula used by SGLang's profile_max_num_token function is:

rest_memory = available_gpu_memory - total_gpu_memory × (1 − mem_fraction_static)

Plugging in the numbers: 21.697 − 93.999 × 0.45 = 21.697 − 42.300 = −20.603 GiB. This negative value propagates to max_tokens = −314811, which triggers the "Not enough memory" error and causes the server to crash.

The Context: A Week of Allreduce Optimization

To understand why this message matters, one must appreciate the broader saga. The assistant has been systematically optimizing the verify step of EAGLE-3 speculative decoding on an 8×RTX PRO 6000 Blackwell system connected via PCIe. The verify step performs 122 NCCL allreduce operations per iteration, each taking ~30ms, creating a bottleneck that makes speculative decoding slower than the baseline (54.1 tok/s vs 89.5 tok/s).

The assistant explored multiple approaches to reduce this overhead: FlashInfer allreduce fusion (failed because its JIT compiler lacks SM120 support), Torch symmetric memory (failed because SM120 is absent from its architecture table), Expert Parallelism with flashinfer A2A (hit assertion errors and OOM), and finally custom allreduce using IPC shared memory buffers. Each approach was systematically tested and eliminated, as documented in the optimization plan.

The custom allreduce approach was the last hope for a software-only fix. It creates small IPC shared memory buffers (~8MB each for metadata and data) that allow direct peer-to-peer GPU communication, bypassing NCCL's multi-step protocol. On NVLink-connected GPUs this is highly effective, but on PCIe the all-to-all communication pattern creates massive bus contention. Despite this, the assistant hoped the small-tensor allreduces in the verify step might still benefit.## The Reasoning Behind the Debug Print

The assistant did not arrive at this debug print casually. It was the culmination of a meticulous forensic investigation spanning messages [msg 5150] through [msg 5174]. The chain of reasoning began when the custom allreduce server crashed with "Not enough memory" even though the NCCL-based baseline worked fine with identical --mem-fraction-static 0.55 and --cuda-graph-max-bs 128 flags.

The assistant's first hypothesis was that the custom allreduce's IPC buffer allocation was consuming extra GPU memory, reducing the available pool below a critical threshold. It traced through the SGLang codebase to understand the memory accounting. In message [msg 5151], it discovered that get_available_gpu_memory returns free memory in GiB (dividing by 2^30), while total_gpu_memory elsewhere is in raw bytes. It followed the call chain from init_memory_pool → profile_max_num_token → the formula that computes rest_memory.

The assistant then manually computed the formula using the observed values: 21.71 GiB available after weight loading, 94 GiB as the initial available memory (captured before weights), and mem_fraction_static = 0.55. The result was deeply negative: −20.59 GiB. This was perplexing because the working baseline did allocate a KV cache of 10.42 GB (159K tokens). Something was fundamentally inconsistent.

Assumptions and Incorrect Hypotheses

The assistant made several assumptions during this investigation, some of which turned out to be incorrect:

1. That total_gpu_memory represents the initial available memory (before weight loading). The parameter name suggests total physical GPU memory, but the code passes min_per_gpu_memory from init_torch_distributed(), which is the minimum free memory across all GPUs at initialization time (~94 GiB). This is not the total physical memory (96 GiB), but rather the available memory before weights are loaded.
2. That the custom allreduce's IPC buffers consume meaningful GPU memory. The assistant calculated ~24 MB per GPU for the buffers themselves, but then realized that IPC mappings on PCIe consume BAR (Base Address Register) space. With 8 GPUs each mapping 14 IPC handles (7 peers × 2 buffers), this could consume ~224 MB of BAR space per GPU, potentially reducing the memory reported by CUDA.
3. That the formula must produce a positive value for the baseline to work. The debug output conclusively shows the formula produces −20.603 GiB even for the custom allreduce run. Yet the baseline succeeds. This contradiction suggests either: (a) the baseline uses a different code path that the assistant hasn't found, (b) the total_gpu_memory value differs between runs (the custom allreduce's IPC allocations reduce initial available memory enough to change the calculation), or (c) there's a minimum floor or clamping in the KV cache allocation that the assistant hasn't discovered.

The Input Knowledge Required

To fully understand this message, one needs knowledge of:

• SGLang's memory management architecture: How profile_max_num_token computes KV cache capacity using available_gpu_memory, total_gpu_memory, and mem_fraction_static. The formula reserves a fraction of total memory for static allocations (weights, activations) and uses the remainder for KV cache.
• The distinction between total physical memory and available memory: total_gpu_memory in this context is not the 96 GiB physical VRAM but the ~94 GiB that was free at initialization time (before model weights were loaded). This subtle naming mismatch is a source of confusion.
• CUDA memory allocation behavior: How IPC (Inter-Process Communication) handles consume BAR space on PCIe, and how this can reduce the memory pool available to CUDA allocations even if the actual data buffers are small.
• The tensor parallelism (TP) architecture: All 8 GPUs report identical values because TP requires symmetric memory distribution. The min_per_gpu_memory is the minimum across all GPUs, ensuring uniform KV cache sizing.
• The broader optimization context: This debug print is one step in a systematic effort to make EAGLE-3 speculative decoding profitable on PCIe-connected Blackwell GPUs, where the verify step's 122 NCCL allreduces create a ~30ms bottleneck.## The Output Knowledge Created This message produces several critical pieces of knowledge: 1. Empirical confirmation of the negative memory computation: The debug print proves that rest_memory = −20.603 GiB and max_tokens = −314811 for the custom allreduce run. This is not a hypothesis but a measured fact. 2. The inconsistency with the working baseline: Since the baseline succeeds with identical parameters, the negative value cannot be the whole story. There must be a difference in the initial total_gpu_memory value between runs, or a code path divergence that the assistant has not yet discovered. 3. The uniform behavior across TP ranks: All GPUs report identical values, confirming that the memory computation is deterministic and symmetric. This rules out per-GPU variation as the cause of the failure. 4. The need for a deeper investigation: The debug output reveals that the assistant's understanding of SGLang's memory management is incomplete. The formula as written cannot produce a positive KV cache allocation for the baseline, yet the baseline works. This contradiction demands further exploration—perhaps the baseline uses a different total_gpu_memory value (clamped to physical memory?), or the formula has been modified in the version the assistant is working with.

The Thinking Process Revealed

The assistant's reasoning in the preceding messages (particularly [msg 5158]–[msg 5173]) reveals a methodical, forensic approach. The assistant:

• Traced the call chain from init_torch_distributed() → min_per_gpu_memory → init_memory_pool() → profile_max_num_token() to understand how total_gpu_memory flows through the system.
• Performed manual calculations using the observed values, computing 21.71 − 94.0 × 0.45 = −20.59 and expressing confusion that the baseline works despite this negative result.
• Considered alternative interpretations, such as whether total_gpu_memory might be the physical total (96 GiB) rather than the initial available memory. It tested this hypothesis and found it also produced a negative result.
• Formulated and discarded hypotheses about IPC buffer memory consumption, BAR space exhaustion, and mem_fraction_static interpretation.
• Added instrumentation by patching the SGLang source code with a debug print, demonstrating a willingness to modify the running system to gain visibility into its internal state.
• Used a 660-second sleep before reading the log, showing an understanding of the server's initialization timeline and patience to wait for full startup. This is the thinking of a systems engineer who has exhausted high-level hypotheses and is now descending to the lowest level of instrumentation to resolve a contradiction between observed behavior and understood code.

Conclusion

Message [msg 5175] is a pivotal moment in a much larger optimization saga. It captures the moment when the assistant's model of SGLang's memory management breaks down—the formula says one thing, but reality says another. The debug output it produces is the raw data that will drive the next phase of investigation. Whether the resolution lies in a different total_gpu_memory value for the baseline, a code path divergence, or a misunderstanding of the formula, this message marks the transition from hypothesis-driven debugging to data-driven discovery. It is a testament to the importance of instrumentation in complex systems: when your mental model contradicts observed behavior, add prints and let the machine tell you what is actually happening.