The Triton Shared Memory Barrier: Diagnosing a Compiler-Level OOM on Blackwell GPUs

Introduction

In the high-stakes world of large-scale ML training, few errors are as deceptive as the one that appeared during an EAGLE-3 drafter training session on NVIDIA's RTX PRO 6000 Blackwell (SM120) GPUs. After a carefully orchestrated sequence of batch size and sequence length tuning to maximize GPU utilization, the training run crashed not with a familiar CUDA out-of-memory error, but with something far more subtle: a Triton shared memory overflow. Message 4279 in this opencode session captures the moment an AI assistant correctly diagnosed this esoteric failure, distinguishing between two fundamentally different memory constraints that often get conflated, and charted a path forward.

This article examines that single message in depth — the reasoning, the technical context required to understand it, the assumptions made, and the knowledge it produced. It is a case study in how deep GPU architecture knowledge, combined with careful log analysis, can unravel failures that would otherwise appear as mysterious crashes.

The Context: A Quest for GPU Utilization

To understand message 4279, one must first understand what led to it. The session's broader goal was training an EAGLE-3 speculative decoding drafter for the Kimi-K2.5 language model on an 8-GPU system. The training had been running with conservative settings (batch_size=1, max_seq_len=4096, TTT=3), but the user wanted deeper speculation (10-16 tokens) to amortize PCIe communication costs on a system without NVLink. The assistant recommended restarting with TTT=5 and, crucially, increasing the packed sequence length to improve GPU utilization.

The initial run at max_seq_len=8192 showed promising results: GPU power draw jumped from 250W to 412-476W, and VRAM usage reached 39.8 GB out of 96 GB available. But with 35,446 batches per epoch and most sequences being close to 8192 tokens, there was almost no packing benefit — each batch contained roughly one sample. The assistant correctly identified that increasing max_seq_len would allow the packing collate function to fit multiple shorter samples into each batch, reducing the number of steps and improving throughput.

What followed was a rapid-fire sequence of attempts and failures:

  1. max_seq_len=32768 → OOM (GPU VRAM exhausted)
  2. max_seq_len=24576 → OOM again
  3. max_seq_len=16384 → Not OOM, but crashed The user's report in <msg id=4276> — "Not oomed but crashed?" — captures the confusion perfectly. The training had been killed, but the error wasn't the familiar CUDA OOM. Something else was wrong.

The Subject Message: A Precise Diagnosis

Message 4279 is the assistant's response after investigating the crash logs. Let us quote it in full:

It's a Triton shared memory OOM in torch.compile/inductor — not GPU VRAM OOM. The compiled RMSNorm kernel (triton_per_fused__to_copy_add_div_expand_mul_pow_sum_view_1) needs 163,912 bytes of shared memory but SM120 only has 101,376 bytes per block. At seq_len=16384 the reduction dimension is too large for the Triton kernel's block size.

>

This is a Triton autotuning issue on SM120, not a memory issue. We can work around it by disabling torch.compile or setting max_autotune off. Let me try with TORCH_COMPILE_DISABLE=1: [bash commands to kill processes]

This message is remarkable for several reasons. First, it immediately draws a critical distinction that many practitioners would miss: between GPU VRAM (high-bandwidth memory, or HBM) and shared memory (on-chip SRAM within each streaming multiprocessor). The earlier OOMs at seq_len=32768 and 24576 were VRAM exhaustion — the model's parameters, activations, and optimizer states simply didn't fit in the 96 GB of HBM. But the crash at seq_len=16384 was different: it was a shared memory overflow within a single Triton kernel.

The Technical Deep Dive: Why Triton Shared Memory Matters

To appreciate the diagnosis, one must understand the GPU memory hierarchy. NVIDIA GPUs have multiple memory levels:

  1. Global memory (HBM): Large (96 GB on the RTX PRO 6000), high latency (~400-800 cycles), used for model parameters, activations, and optimizer states.
  2. Shared memory (SRAM): Small (typically 48-228 KB per SM depending on configuration), very low latency (~20-30 cycles), used for data sharing among threads in a block and as scratchpad for kernel computations.
  3. Registers: Tiny (64K 32-bit registers per SM), zero-latency, used for local variables. When PyTorch's torch.compile (powered by Triton) compiles a model, it generates GPU kernels that use shared memory as a fast scratchpad. The RMSNorm kernel — identified by the mangled name triton_per_fused__to_copy_add_div_expand_mul_pow_sum_view_1 — performs a reduction operation across the hidden dimension. At seq_len=16384, the reduction dimension becomes large enough that Triton's autotuner selects a kernel configuration requiring 163,912 bytes of shared memory. The SM120 architecture (Blackwell) provides 101,376 bytes of shared memory per block in its default configuration. The kernel needs 163,912 bytes — a 62% oversubscription. This is a hard limit: if a kernel requests more shared memory than available, it cannot launch. The error message from the logs (visible in <msg id=4278>) confirms this precisely:
RuntimeError: No valid triton configs. OutOfMemoryError: out of resource: 
triton_per_fused__to_copy_add_div_expand_mul_pow_sum_view_1 
Required: 163912 Hardware limit:101376
Reducing block sizes or `num_stages` may help.

The key insight in message 4279 is recognizing that this is a compiler autotuning failure, not a fundamental memory limitation. Triton's autotuner explores different kernel configurations (block sizes, number of pipeline stages, etc.) and selects one that minimizes runtime while respecting hardware constraints. On SM120, the autotuner's search space apparently included configurations that exceeded shared memory limits, and none of the valid configurations were selected — perhaps because the autotuner's cost model didn't properly account for SM120's shared memory ceiling, or because all valid configurations were deemed too slow.

Assumptions and Reasoning

The assistant's diagnosis rests on several assumptions, most of which are well-justified:

  1. The error is reproducible and deterministic: The assistant assumes that the Triton kernel compilation failure will occur consistently given the same model architecture and sequence length. This is reasonable — Triton's autotuning is deterministic for a given set of inputs and hardware.
  2. The problem is in RMSNorm specifically: The kernel name reveals it's an RMSNorm operation (the rms_norm pattern is visible in the mangled name components like pow, sum, div, add, mul, expand). RMSNorm performs a reduction across the hidden dimension to compute the root-mean-square statistic, which is then used to normalize the input. At large sequence lengths, the reduction dimension grows, requiring more shared memory for the intermediate results.
  3. Disabling torch.compile will work: The assistant proposes TORCH_COMPILE_DISABLE=1 as a workaround. This assumes that the eager-mode (non-compiled) implementation of RMSNorm uses a different kernel that doesn't hit the shared memory limit. This is a safe assumption — the eager-mode kernels in PyTorch are typically hand-tuned for each GPU architecture and are more conservative with shared memory usage.
  4. The SM120 shared memory limit is 101,376 bytes: This is a specific architectural detail. On Blackwell GPUs, each SM has 128 KB of shared memory, but it's partitioned. The 101,376 byte limit (approximately 99 KB) suggests the GPU is configured with a particular partitioning between shared memory and L1 cache. The assistant correctly identifies this hardware constraint.

Potential Mistakes and Limitations

While the diagnosis is sound, there are nuances worth examining:

Could the kernel be fixed instead of disabled? The error message itself suggests "Reducing block sizes or num_stages may help." A more sophisticated fix would involve constraining Triton's autotuning search space — perhaps by setting environment variables like TRITON_MAX_BLOCK_SIZE or using torch.compile with specific tuning flags. The assistant's approach of disabling compilation entirely is pragmatic but sacrifices the performance benefits of compilation for all operations, not just RMSNorm.

The workaround may not be optimal: Disabling torch.compile means the entire model runs in eager mode, which could be significantly slower than a selectively-compiled version. A better approach might have been to compile only specific submodules while leaving RMSNorm in eager mode, or to use torch.compile with mode="reduce-overhead" which uses more conservative kernel configurations.

The root cause is architectural: The SM120's 101,376 byte shared memory limit is a hardware constraint that won't change. If the model architecture requires RMSNorm at large sequence lengths, the Triton compiler's autotuner needs to be updated to properly handle this architecture. This is a known challenge with new GPU generations — compiler backends often need tuning to match the hardware's characteristics.

Input Knowledge Required

To fully understand message 4279, one needs:

  1. GPU memory hierarchy knowledge: Understanding the difference between HBM (VRAM) and shared memory (SRAM), and that they have vastly different capacities and constraints.
  2. Triton compiler internals: Knowing that torch.compile uses Triton to generate GPU kernels, that Triton performs autotuning to select optimal block sizes and pipeline stages, and that shared memory usage is a key constraint in kernel configuration.
  3. NVIDIA Blackwell (SM120) architecture: Knowing the shared memory limits of the specific GPU generation being used. The RTX PRO 6000 Blackwell has specific resource limits that differ from previous generations like Hopper (SM90) or Ada Lovelace (SM89).
  4. RMSNorm operation characteristics: Understanding that RMSNorm performs a reduction across the hidden dimension, which requires shared memory proportional to the sequence length times the hidden dimension.
  5. The training pipeline context: Knowing that the training uses packed sequences with flex_attention and block-diagonal masking, and that increasing max_seq_len packs more samples per batch to improve GPU utilization.

Output Knowledge Created

This message produces several valuable pieces of knowledge:

  1. A clear diagnostic methodology: The assistant demonstrates how to distinguish between VRAM OOM and shared memory OOM by examining the error message content. VRAM OOMs typically mention "CUDA out of memory" with memory allocation statistics, while Triton shared memory OOMs mention "out of resource" with specific byte counts and hardware limits.
  2. A practical workaround: Disabling torch.compile with TORCH_COMPILE_DISABLE=1 is a quick way to bypass Triton autotuning issues without modifying the training code. This is valuable for production scenarios where debugging compiler issues would cause unacceptable delays.
  3. A deeper understanding of the training bottleneck: The diagnosis reveals that at seq_len=16384, the bottleneck shifts from VRAM capacity to kernel compilation constraints. This informs future tuning decisions — perhaps the optimal max_seq_len is somewhere between 8192 and 16384, where VRAM is well-utilized but Triton kernels remain compilable.
  4. Documentation of an SM120-specific issue: For anyone training on Blackwell GPUs with torch.compile, this message serves as a reference for a known failure mode. The specific kernel name and shared memory requirements can be searched for in logs to identify similar issues.

The Thinking Process

The reasoning visible in this message follows a clear diagnostic pattern:

  1. Identify the error type: The assistant first recognizes that the error is not a standard CUDA OOM but a Triton compilation failure. The key clue is the phrase "No valid triton configs" combined with "out of resource" and specific byte counts.
  2. Parse the kernel signature: The mangled kernel name triton_per_fused__to_copy_add_div_expand_mul_pow_sum_view_1 is decomposed to identify the operation (RMSNorm-like reduction with pow, sum, div, add, mul operations).
  3. Map to hardware constraints: The required shared memory (163,912 bytes) is compared against the SM120 hardware limit (101,376 bytes). The assistant knows this limit from architectural knowledge of the Blackwell GPU.
  4. Connect to the triggering condition: The assistant links the failure to the sequence length increase (16384), reasoning that the reduction dimension grows with sequence length, requiring more shared memory.
  5. Propose a solution: The workaround (disabling torch.compile) is chosen for its simplicity and reliability. The assistant doesn't attempt to fix the Triton autotuning because that would require modifying compiler internals — a much more complex task.
  6. Execute immediately: Rather than waiting for confirmation, the assistant kills the crashed processes and prepares to relaunch with the fix. This reflects the time-sensitive nature of the training pipeline.

Conclusion

Message 4279 is a masterclass in GPU-level debugging. It demonstrates that not all OOMs are created equal — the distinction between VRAM exhaustion and shared memory oversubscription is critical for correct diagnosis. The assistant's ability to parse a Triton compiler error, map it to hardware constraints, and propose a targeted workaround saved what could have been hours of fruitless debugging.

For practitioners working with large-scale model training on modern GPU architectures, this message offers a valuable lesson: when a training run crashes with an unfamiliar error, look beyond the surface. The Triton compiler, for all its power, is still a relatively young piece of infrastructure, and its autotuning heuristics don't always account for the quirks of new hardware. Understanding the GPU memory hierarchy — and knowing which resource is actually exhausted — is the key to turning a cryptic crash into a solvable problem.