The SnapDeals OOM: A Crisis of VRAM on the Path to Multi-GPU Proving
Introduction
In the high-stakes world of zero-knowledge proof generation, few resources are as precious as GPU memory. When a proving engine exhausts its VRAM budget, the result is not a graceful degradation but a violent crash—a cudaMallocAsync failure, a panic, a SIGSEGV, and a systemd restart cycle. This article examines a single message in an opencode coding session—message index 432—where a user reports exactly such a crash on a SnapDeals workload, and poses a critical question: is this a new bug introduced by recent changes, or a pre-existing limitation of low-memory GPUs?
The message arrives at a pivotal moment in the conversation. The assistant and user have just spent several rounds debugging and fixing a multi-GPU data race in the CuZK proving engine, culminating in a proper architectural fix that threads a gpu_index parameter through the entire call chain. The fix was deployed to a test host and validated. But immediately after this success, a new host—p-dev-ngw-1, equipped with a 20 GB RTX 4000 Ada GPU—reports an out-of-memory crash when processing a SnapDeals proof with 16 partitions. The user's question, appended to a wall of journalctl logs, is deceptively simple: "can this be related to recent changes or was it already there (low vs high mem gpu?)"
This message is not merely a bug report. It is a diagnostic puzzle that forces a reassessment of the shared mutex fix, a confrontation with the VRAM constraints of consumer GPUs, and a pivot toward a deeper architectural solution. To understand why this message matters, we must first understand the context that produced it.
The Message in Full
The user's message consists of a lengthy block of journalctl logs from the cuzk daemon on host p-dev-ngw-1, followed by a concluding question. The logs trace the lifecycle of a single SnapDeals proof job with ID snap-180965-163, spanning from SRS loading at 04:18:53 to a catastrophic OOM at 04:20:17. Here is the message as it appears in the conversation:
[user] New issue - Mar 06 04:18:53 p-dev-ngw-1 cuzk[865104]: 2026-03-06T03:18:53.056024Z INFO cuzk_core::srs_manager: SRS loaded successfully circuit_id=snap-32g elapsed_ms=23056
Mar 06 04:18:53 p-dev-ngw-1 cuzk[865104]: 2026-03-06T03:18:53.056731Z INFO synth_single{proof_kind=snap-update}: cuzk_core::engine: dispatching per-partition SnapDeals synthesis job_id=snap-180965-163 num_partitions=16 partition_workers=5
Mar 06 04:18:53 p-dev-ngw-1 cuzk[865104]: 2026-03-06T03:18:53.065086Z INFO cuzk_core::engine: background PCE extraction starting for SnapDeals
Mar 06 04:18:53 p-dev-ngw-1 cuzk[865104]: TIMELINE,41034941,SYNTH_START,snap-180965-163,snap_partition=0
... (synthesis logs for partitions 0-4) ...
Mar 06 04:20:16 p-dev-ngw-1 cuzk[865104]: 2026-03-06T03:20:16.315910Z INFO cuzk_pce::recording_cs: RecordingCS: extracted pre-compiled circuit num_inputs=5 num_aux=80978488 num_constraints=81049504 a_nnz=190374912 b_nnz=81118127 c_nnz=172187492 a_aux_density=80743581 b_input_density=1 b_aux_density=20983607
Mar 06 04:20:16 p-dev-ngw-1 cuzk[865104]: 2026-03-06T03:20:16.316435Z INFO cuzk_core::pipeline: PCE extraction complete circuit_id=snap-32g extract_ms=83227 summary=PreCompiledCircuit { inputs: 5, aux: 80978488, constraints: 81049504, A: {nnz: 190374912, avg/row: 2.3}, B: {nnz: 81118127, avg/row: 1.0}, C: {nnz: 172187492, avg/row: 2.1}, total_nnz: 443680531, mem: 15.8 GiB }
... (more synthesis completions, GPU worker pickups) ...
Mar 06 04:20:17 p-dev-ngw-1 cuzk[865104]: CUZK_TIMING: d_a_cache allocated 4096 MiB on gpu 0
Mar 06 04:20:17 p-dev-ngw-1 cuzk[865104]: thread 'tokio-runtime-worker' panicked at /tmp/czk/extern/supraseal-c2/src/lib.rs:311:9:
Mar 06 04:20:17 p-dev-ngw-1 cuzk[865104]: cudaMallocAsync(&d_ptr, sz, stream)@sppark-0.1.14/sppark/util/gpu_t.cuh:73 failed: "out of memory"
Mar 06 04:20:17 p-dev-ngw-1 cuzk[865104]: note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
Mar 06 04:20:50 p-dev-ngw-1 systemd[1]: cuzk.service: Main process exited, code=dumped, status=11/SEGV
Mar 06 04:20:50 p-dev-ngw-1 systemd[1]: cuzk.service: Failed with result 'core-dump'.
Mar 06 04:20:50 p-dev-ngw-1 systemd[1]: cuzk.service: Consumed 1h 53min 16.713s CPU time, 169.6G memory peak, 15.6G memory swap peak.
Mar 06 04:21:00 p-dev-ngw-1 systemd[1]: cuzk.service: Scheduled restart job, restart counter is at 4.
Mar 06 04:21:00 p-dev-ngw-1 systemd[1]: Started cuzk.service - CuZK Proving Engine Daemon. --- On a rtx4000ada, 20GB, WindowPoSt works fine but Snap seems to OOM vram immediately post-synth; can this be related to recent changes or was it already there (low vs high mem gpu?)The user has truncated some repetitive synthesis-start lines for partitions 0 through 4, but the essential narrative is preserved: synthesis proceeds normally for all 16 partitions, PCE extraction completes successfully, and then—at the moment two GPU workers simultaneously enter the proving code—the GPU runs out of memory and the process crashes with a SIGSEGV.
The Context: A Multi-GPU Saga
To appreciate the significance of this message, one must understand the conversation that precedes it. The session has been wrestling with a subtle GPU data race for several rounds. The root cause, diagnosed earlier, was that the C++ GPU proving code in groth16_cuda.cu always routed single-circuit proofs to GPU 0, regardless of which Rust worker submitted them. On a multi-GPU system, workers assigned to GPU 1 would still execute their CUDA kernels on GPU 0, creating data races when multiple workers entered the GPU code simultaneously.
The initial "fix" was a shared mutex that serialized all partition proofs onto GPU 0. This was a pragmatic hack—it prevented the data race, but at the cost of wasting the second GPU entirely. The assistant and user confirmed this waste empirically: nvidia-smi showed GPU 0 at 100% utilization while GPU 1 sat at 0%.
The proper solution, developed over the course of several rounds, was to thread a gpu_index parameter through the entire call chain—from the C++ CUDA code, through the Rust FFI, through the bellperson prover functions, through the pipeline layer, and finally to the engine's GPU worker code. This allowed each worker to pass its assigned GPU ordinal to the C++ code, so that single-circuit proofs would actually execute on the correct GPU. The shared mutex hack was reverted. The build succeeded. The fix was deployed to the test host cs-calib (10.1.16.218) and validated: both GPUs showed active d_a_cache allocations, and workers were correctly load-balanced.
It is at this moment of triumph that the user presents the SnapDeals OOM crash from a different host—p-dev-ngw-1—equipped with a single 20 GB RTX 4000 Ada GPU. The timing is exquisitely inconvenient. The assistant has just committed a multi-GPU fix; the user now reports a crash that, on first glance, looks like it could be caused by the very same shared-mutex serialization that the fix was designed to eliminate.## Reading the Logs: A Forensic Analysis
The journalctl logs in the user's message are not raw data dump—they are carefully selected evidence. The user has chosen to include the complete timeline of the SnapDeals job, from SRS loading to the OOM crash, and the structure of these logs tells a story that the user wants the assistant to interpret.
The first thing to notice is the proof kind: snap-update. SnapDeals is a proof type used in the Filecoin network for updating sector commitments after snap-based data storage. Unlike PoRep (Proof of Replication), which uses relatively small circuits, SnapDeals circuits are enormous. The PCE extraction summary reveals the scale: 81,049,504 constraints, 80,978,488 auxiliary variables, and a total of 443,680,531 non-zero entries across the A, B, and C matrices. The extracted pre-compiled circuit consumes 15.8 GiB of memory. This is a massive circuit—far larger than the PoRep circuits that the assistant has been debugging.
The second critical observation is the timing. Synthesis for partitions 0 through 4 takes approximately 83–84 seconds each. But the crash does not occur during synthesis—it occurs during the GPU proving phase, immediately after two workers pick up synthesized partitions simultaneously. The logs show:
04:20:17— Worker 1 (assigned to GPU 1) picks up partition 2 and enters the GPU proving code.CUZK_TIMING: d_a_cache allocated 4096 MiB on gpu 0— Worker 1 allocates a 4 GiB cache on GPU 0.04:20:17— Worker 0 (assigned to GPU 0) picks up partition 4 and enters the GPU proving code.cudaMallocAsync failed: "out of memory"— The second allocation attempt exhausts VRAM. The fact that both workers enter the GPU code at the same millisecond is the crucial detail. Under the shared mutex fix, only one worker should be in the GPU code at a time. The fact that two workers are simultaneously executing CUDA allocations suggests that either (a) this host does not have the shared mutex fix deployed, or (b) the shared mutex is not functioning correctly for SnapDeals proofs. The user's question—"can this be related to recent changes or was it already there (low vs high mem gpu?)"—reveals their uncertainty about the root cause. They are asking whether the crash is a regression introduced by the multi-GPU fix (which would be embarrassing after all that work) or a pre-existing limitation of the 20 GB RTX 4000 Ada GPU when faced with SnapDeals' massive circuits.
The Reasoning Behind the Question
The user's decision to present this crash as a "new issue" at this precise moment is not accidental. The conversation had just concluded a successful multi-GPU fix deployment. The user could have simply said "SnapDeals is crashing on p-dev-ngw-1" and left it at that. Instead, they chose to include the full log dump and a pointed question about causality.
This framing accomplishes several things. First, it signals to the assistant that the user is aware of the recent changes and is concerned about their impact. The question "can this be related to recent changes" is a gentle probe—the user wants to know if the assistant's fix introduced a regression. Second, by mentioning "low vs high mem gpu," the user provides a hypothesis: perhaps the crash is simply because the RTX 4000 Ada has only 20 GB of VRAM, and SnapDeals requires more. This is a reasonable theory, given that the PCE extraction alone reports 15.8 GiB for the circuit, and the d_a_cache allocation adds another 4 GiB, totaling nearly 20 GiB before any actual proving work begins.
Third, the user's inclusion of the systemd restart counter ("restart counter is at 4") communicates urgency. This is not a one-time fluke; the service has crashed and restarted multiple times. The host is effectively unusable for SnapDeals proving until the issue is resolved.
Assumptions Embedded in the Message
The user's message makes several implicit assumptions that shape how the assistant will respond.
The first assumption is that the crash is related to GPU memory capacity. The user explicitly frames the question around "low vs high mem gpu," suggesting they believe the 20 GB RTX 4000 Ada may simply be insufficient for SnapDeals. This is a reasonable assumption—the PCE circuit alone is 15.8 GiB, and the d_a_cache adds 4 GiB, leaving almost no headroom for the actual proving computation. However, this assumption may be premature: WindowPoSt works fine on the same GPU, and the crash occurs specifically when two workers enter the GPU code simultaneously, not when a single worker attempts to prove.
The second assumption is that the crash might be related to the recent multi-GPU changes. This assumption is natural given the timing—the fix was just deployed, and now a new crash appears. But the user is careful not to assert causality; they ask "can this be related" rather than "this is caused by." This open-ended framing invites the assistant to investigate without defensiveness.
The third assumption is that the assistant has access to the host for debugging. The user provides the hostname p-dev-ngw-1 and the log output, implicitly granting permission to SSH in and investigate further. This is confirmed in subsequent messages when the user provides credentials and context about the host's setup.
A subtle but important assumption is that the crash is a new issue. The user says "New issue" at the top of the message, implying that SnapDeals proving was previously working on this host or that this is the first time it has been attempted. If the crash is actually a pre-existing condition—if SnapDeals has never worked on a 20 GB GPU—then the "new issue" framing is misleading. The user's question acknowledges this ambiguity.
The Thinking Process: What the Assistant Must Unravel
When the assistant receives this message, several lines of reasoning must be pursued simultaneously.
The first and most urgent question is: does this host have the shared mutex fix? The logs show two workers entering the GPU code simultaneously, which the shared mutex was designed to prevent. If p-dev-ngw-1 is running an older binary without the fix, then the crash is a pre-existing condition unrelated to the recent changes. If it has the fix, then the fix is not working correctly for SnapDeals.
The assistant's initial response (message 433) shows this reasoning in action. The assistant examines the timeline and notes: "Two GPU workers entered the C++ proving code at the same time. This is exactly the shared mutex fix working—they're serialized by the shared mutex now. But wait..." The "but wait" is the critical moment of doubt. If the shared mutex is working, only one worker should be in the GPU code. The fact that both are there suggests the mutex is not being held across both calls.
The assistant then attempts to SSH to 10.1.16.218 to check the config, but this is the wrong host—the crash is on p-dev-ngw-1. The assistant catches this mistake and pivots to reading the engine source code to understand the mutex logic.
The second line of reasoning concerns VRAM budgeting. Even with perfect serialization, a single SnapDeals partition might require more than 20 GiB of VRAM. The PCE extraction reports 15.8 GiB for the circuit alone. The d_a_cache allocation adds 4 GiB. That's 19.8 GiB before any proving work begins—dangerously close to the 20 GiB limit. If the proving kernels require additional memory, the OOM is inevitable regardless of concurrency.
The third line of reasoning is about the interaction between the PCE fast path and GPU memory. The logs show that partition 5 uses the "PCE fast path for synthesis" while partitions 0-4 use the standard synthesis path. The PCE fast path may have different memory characteristics that exacerbate the VRAM pressure.
The fourth line of reasoning concerns the d_a_cache allocation itself. The log shows d_a_cache allocated 4096 MiB on gpu 0. This is a 4 GiB cache that appears to be allocated per-worker, not shared. If each GPU worker allocates its own d_a_cache, and two workers are active simultaneously (even with the mutex, the allocations might overlap), the VRAM consumption doubles.
The Knowledge Required to Understand This Message
To fully grasp the significance of this message, one needs substantial background knowledge across several domains.
GPU proving architecture: Understanding that zero-knowledge proof generation involves both CPU-bound synthesis (constraint generation) and GPU-bound proving (multi-scalar multiplication, FFT, etc.) is essential. The crash occurs at the transition between these phases—synthesis completes, and the GPU proving begins.
CuZK's worker model: The engine uses a pool of GPU workers, each assigned to a specific GPU ordinal. Workers pick up synthesized proofs from a channel and execute the GPU proving code. The gpu_workers_per_device = 2 configuration means two workers per GPU, for a total of four workers on a dual-GPU system (or two workers on a single-GPU system like p-dev-ngw-1).
The shared mutex fix: The assistant's recent change added a shared mutex that serializes access to the C++ proving code for single-circuit (partitioned) proofs. This was designed to prevent data races when multiple workers target the same GPU. The SnapDeals crash tests whether this fix is deployed and effective.
PCE (Pre-Compiled Circuit Evaluator): The PCE system extracts the constraint system structure into a pre-compiled format that allows faster synthesis on subsequent runs. The extraction itself is memory-intensive—15.8 GiB for SnapDeals 32 GiB circuits.
VRAM constraints of consumer GPUs: The RTX 4000 Ada has 20 GiB of VRAM, which is generous for a consumer card but tight for large-scale zero-knowledge proving. Enterprise cards like the A6000 (48 GiB) are more typical for this workload. The user's mention of "low vs high mem gpu" reflects an awareness that SnapDeals may simply require an enterprise GPU.
SnapDeals proof structure: SnapDeals is a Filecoin proof type for updating sector commitments. It involves significantly larger circuits than PoRep or WindowPoSt, which is why the crash manifests here but not for other proof types.
Output Knowledge: What This Message Creates
This message, though it appears to be a simple bug report, generates substantial new knowledge for the conversation.
First, it establishes that SnapDeals proving on a 20 GB GPU is problematic, regardless of the multi-GPU fix. This is a critical piece of operational knowledge: the proving system cannot be deployed on consumer GPUs for SnapDeals workloads without either reducing memory usage or increasing the GPU budget.
Second, it reveals a potential flaw in the shared mutex approach. Even if the mutex is deployed, the fact that two workers can enter the GPU code simultaneously (as shown in the logs) suggests that the mutex may not cover all GPU entry points, or that there is a race condition in the mutex acquisition itself. This forces the assistant to re-examine the mutex implementation.
Third, it shifts the conversation's focus from multi-GPU load balancing to single-GPU memory management. The previous rounds were about utilizing both GPUs on a dual-A6000 system. This message introduces a new axis of concern: can the system function at all on a single, memory-constrained GPU?
Fourth, it creates a diagnostic challenge that tests the assistant's understanding of the entire proving pipeline. The assistant must trace the crash from the cudaMallocAsync failure in sppark (a CUDA library for multi-scalar multiplication) back through the Rust FFI, through the bellperson prover, through the pipeline layer, and ultimately to the engine's worker dispatch logic.
Fifth, it provides a concrete benchmark for SnapDeals performance. The logs show that synthesis takes ~84 seconds per partition, PCE extraction takes ~83 seconds, and the GPU phase fails immediately upon entry. This timing data is valuable for capacity planning and performance optimization.
Mistakes and Incorrect Assumptions
The user's message contains a subtle but important ambiguity. The logs show d_a_cache allocated 4096 MiB on gpu 0, which appears to be a single allocation on GPU 0. But the worker that made this allocation is Worker 1, which is assigned to GPU 1. This is the exact symptom of the original multi-GPU bug: the C++ code ignores the worker's GPU assignment and always uses GPU 0. If the shared mutex fix were properly deployed on this host, Worker 1 would either (a) use GPU 1 for its allocation, or (b) be blocked by the mutex until Worker 0 completes. Neither is happening—Worker 1 allocates on GPU 0 and Worker 0 enters simultaneously.
This suggests that p-dev-ngw-1 does NOT have the multi-GPU fix deployed. The user's assumption that the crash "can be related to recent changes" is therefore incorrect in the specific sense—the recent changes are not the cause, because they haven't been applied to this host. However, the user's broader point is valid: the crash is related to the absence of the fix, or more precisely, to the architectural flaw that the fix was designed to address.
The user also assumes that WindowPoSt working on the same GPU is relevant evidence. While it's true that WindowPoSt uses smaller circuits and succeeds, this doesn't directly inform the SnapDeals analysis. WindowPoSt might succeed because its circuits fit within the 20 GiB budget even with two concurrent workers, while SnapDeals fails because its circuits are 5-10x larger. The comparison is useful for ruling out hardware faults, but not for diagnosing the root cause.
Another potential mistake is the assumption that the crash is "immediately post-synth." The logs show that synthesis completes, the proof is sent to the GPU channel, the worker picks it up, and then the OOM occurs. The delay between synthesis completion and the crash is milliseconds—essentially instantaneous from a human perspective. But from a system perspective, there is a meaningful sequence of events: channel send, worker pickup, GPU entry, d_a_cache allocation, and then the failed cudaMallocAsync. The crash is not literally "immediately" after synthesis; it occurs after a chain of operations that each consume resources.
The Deeper Significance
This message is a turning point in the conversation. Before it, the assistant was focused on a relatively narrow problem: fixing a GPU data race on multi-GPU systems. The fix was elegant and satisfying—thread a parameter through the call chain, let each worker use its assigned GPU, and achieve true load balancing.
The SnapDeals OOM shatters this tidy narrative. It reveals that the real problem is not just load balancing, but fundamental VRAM pressure. Even on a single-GPU system, SnapDeals pushes the proving engine to its limits. The shared mutex fix, which was designed to prevent data races, turns out to be a band-aid on a deeper wound: the C++ code's inability to respect GPU assignments.
More importantly, this message forces the assistant to confront the heterogeneity of the deployment environment. The test host cs-calib has dual RTX A6000s (48 GiB each), while p-dev-ngw-1 has a single RTX 4000 Ada (20 GiB). The assistant's fix was validated on the high-memory system; the SnapDeals crash reveals that the fix may not be sufficient for memory-constrained environments.
The user's question—"can this be related to recent changes or was it already there?"—is ultimately answered in the subsequent messages. The assistant discovers that p-dev-ngw-1 is a different host running an older binary without the shared mutex fix. The crash is therefore a pre-existing condition, not a regression. But this answer is not entirely satisfying, because even with the fix, a single SnapDeals partition might still OOM on a 20 GiB GPU. The fix prevents concurrent GPU access, but it doesn't reduce the per-worker memory footprint.
Conclusion
Message 432 is a masterclass in diagnostic communication. The user presents a crash with surgical precision—enough logs to reconstruct the timeline, enough context to understand the proof type and GPU configuration, and a question that guides the investigation without dictating the answer. The message forces the assistant to synthesize everything learned in the previous rounds and apply it to a new, more challenging scenario.
The SnapDeals OOM is not just a bug report; it is a stress test of the entire proving architecture. It exposes the gap between the development environment (high-memory enterprise GPUs) and the production environment (memory-constrained consumer GPUs). It challenges the assumption that the multi-GPU fix is complete, revealing that the underlying issue—the C++ code's GPU selection logic—has implications beyond data races. And it sets the stage for the next phase of the conversation: not just fixing GPU assignment, but managing VRAM budgets across diverse hardware.
In the end, the user's question is answered: the crash is not caused by the recent changes, but it is related to them in a deeper sense. The recent changes exposed the architectural flaw that makes the crash possible. The fix is necessary but not sufficient. The real work—making SnapDeals proving viable on consumer GPUs—has only just begun.