"gpu done": The Terse Milestone That Validated an Entire Architecture

On February 17, 2026, at approximately 15:35 CET, a user typed two words into a terminal: "gpu done." This message, message index 229 in the conversation, appears at first glance to be a trivial status update—barely worth a second thought. But in the context of the cuzk proving engine project, those two words represented the culmination of weeks of architectural design, implementation, and debugging. They signaled that the first real end-to-end Groth16 proof had successfully completed on an NVIDIA RTX 5070 Ti (Blackwell architecture) through a brand-new proving daemon that had never been run against real hardware before. This article unpacks why that message was written, what it meant, and what knowledge it created.

The Context: A Proving Pipeline Comes to Life

To understand "gpu done," we must first understand what preceded it. The cuzk project (short for "CUDA ZK") was a pipelined SNARK proving daemon designed to replace the existing Filecoin proof generation workflow with a persistent, memory-efficient service. The core insight driving the architecture was that the existing approach suffered from nine structural bottlenecks (documented in earlier optimization proposals), chief among them the repeated loading of ~45 GiB of Structured Reference String (SRS) parameters from disk for every proof. The cuzk daemon solved this by keeping the SRS resident in GPU memory across proof submissions, eliminating the I/O overhead.

Phase 0 of the implementation had just been completed: a Rust workspace with six crates, a gRPC API, a priority scheduler, and a prover module wired to filecoin-proofs-api. The build had succeeded with --features cuda-supraseal, enabling GPU acceleration. The daemon had been started with FIL_PROOFS_PARAMETER_CACHE=/data/zk/params and a 51 MB PoRep C1 output had been submitted via gRPC.

What followed was a tense waiting period. The first proof required loading 45 GiB of SRS parameters from disk, which took approximately 15 seconds—surprisingly fast thanks to Linux page cache. Then came the CPU-bound circuit synthesis phase, where the system built approximately 130 million constraints for a 32 GiB PoRep sector. The assistant monitored this process through a series of status checks ([msg 222] through [msg 227]), watching the daemon consume 203 GiB of RSS and 14,253% CPU (approximately 142 cores running in parallel). The GPU sat idle at 4% utilization during this phase, waiting for the CPU to finish constructing the circuit.

The user's earlier message at [msg 228], "gpu use now," indicated that synthesis had completed and the GPU kernels had begun executing. Now, at message 229, the user reported "gpu done."

Why This Message Was Written

The user wrote "gpu done" for several interconnected reasons, each revealing something about the workflow and the relationship between the human operator and the automated system.

First, the message served as a real-time status beacon. The daemon was running as a background process with logs streaming to a file (/tmp/cuzk-daemon.log). While the assistant had been periodically polling the log and GPU state via nvidia-smi, the user had direct access to the terminal where the daemon was running and could see the GPU activity in real time. When the GPU kernels finished, the user communicated that fact to the assistant, who was not colocated at the same terminal. This was a human bridge between two monitoring contexts.

Second, the message was a milestone declaration. The entire cuzk project—from the initial deep-dive investigation into the SUPRASEAL_C2 pipeline (Segment 0), through the micro-optimization analysis (Segment 1), the constraint-structure exploitation proposals (Segment 2), the architectural design of the daemon (Segment 3), and the implementation and debugging (Segment 4)—had been building toward this moment. "gpu done" was the first confirmation that the entire chain worked end-to-end on real hardware. It was the equivalent of a launch controller saying "we have liftoff."

Third, the message was an implicit handoff. By reporting that the GPU was done, the user was signaling that the assistant could proceed to the next step: checking whether the proof was valid, measuring the timing, comparing first-proof vs. second-proof performance, and beginning the hardening work. The user was not just reporting a fact; they were unblocking the next phase of work.

The Assumptions Embedded in Two Words

Despite its brevity, "gpu done" rests on several assumptions that are worth examining.

Assumption 1: The GPU phase was the critical path. The user assumed that GPU completion was the most important status to communicate. This was correct—the GPU kernels (NTT, MSM, batch addition) were the most computationally intensive and time-consuming part of the proof generation. If the GPU phase completed without error, the proof was almost certainly valid (assuming no bugs in the CPU-to-GPU data transfer or the kernel implementations).

Assumption 2: The assistant would understand the implication. The user assumed that the assistant had enough context to know what "gpu done" meant without elaboration. This was a reasonable assumption given the shared history—the assistant had been deeply involved in every aspect of the pipeline design and had just been monitoring the GPU status moments earlier ([msg 227]). The two-word message was sufficient because the shared context was rich.

Assumption 3: No error had occurred. The user did not say "gpu done with errors" or "gpu failed." The unadorned "gpu done" implicitly communicated success. This assumption turned out to be correct—the proof completed successfully and passed internal verification.

Potential mistake: The message conflated "GPU kernels finished" with "proof complete." In the Groth16 pipeline, GPU completion is followed by a final CPU step: proof aggregation and serialization. The user's "gpu done" might have been slightly premature if they were only observing GPU utilization dropping to zero, not waiting for the daemon log to confirm proof serialization. However, in practice, the post-GPU CPU work is negligible compared to the synthesis phase, so the distinction is minor. The assistant's subsequent actions (checking the daemon log for the proof result) correctly handled this edge case.

Input Knowledge Required

To interpret "gpu done" correctly, the assistant needed a substantial body of prior knowledge:

The Groth16 proof pipeline architecture: Understanding that C2 proof generation proceeds through C1 deserialization → SRS loading → circuit synthesis (CPU) → witness computation (CPU) → NTT/MSM (GPU) → proof serialization (CPU). The message referred specifically to the GPU phase of this pipeline.
The supraseal CUDA backend: Knowing that the cuda-supraseal feature flag enabled GPU acceleration via sppark, which delegates to nvcc for JIT compilation. The RTX 5070 Ti (Blackwell, sm_120) required this path because the older rust-gpu-tools fatbin kernels might not support the new architecture.
The monitoring setup: Understanding that the daemon logs were going to /tmp/cuzk-daemon.log and that GPU state was visible via nvidia-smi. The user had access to a terminal showing real-time GPU activity.
The performance expectations: Knowing that the first proof would be slower due to SRS loading, and that subsequent proofs would benefit from SRS residency. The "gpu done" message would later be contextualized against the 116.8-second first-proof time and the 92.8-second second-proof time.
The verification requirement: Understanding that GPU completion did not guarantee proof validity—the proof still needed to pass internal verification. The assistant's next actions included checking the daemon log for the verification result.

Output Knowledge Created

The "gpu done" message created several pieces of actionable knowledge:

First, it confirmed that the entire Phase 0 pipeline was functional. The cuzk daemon could accept a C1 input via gRPC, deserialize it, invoke filecoin-proofs-api with the correct parameters, load SRS from the correct path, execute CPU synthesis on all available cores, dispatch GPU kernels to the RTX 5070 Ti, and produce a valid Groth16 proof. This was the fundamental validation that the architecture worked.

Second, it established a baseline performance measurement. The first proof completed in 116.8 seconds (including ~15 seconds for SRS loading). The second proof, with SRS already resident in memory, completed in 92.8 seconds—a 20.5% improvement. This validated the core thesis of the Persistent Prover Daemon proposal: keeping SRS in memory across proof submissions eliminates a significant overhead.

Third, it demonstrated that the Blackwell GPU (RTX 5070 Ti) was compatible with the supraseal CUDA path. This was a non-trivial finding. Earlier in the session ([msg 204]), the assistant had expressed concern about whether the precompiled fatbin kernels in rust-gpu-tools would support sm_120 (Blackwell's compute capability). The successful GPU execution proved that the nvcc JIT-compilation path in sppark handled the new architecture correctly.

Fourth, it validated the memory architecture. The daemon's peak RSS of 203 GiB during synthesis, including the 45 GiB SRS and the circuit witness, was consistent with the ~200 GiB peak memory documented in the earlier background analysis. The system did not run out of memory, and the GPU's 16 GiB VRAM (with 1690 MiB used during synthesis, growing to 3822 MiB during GPU compute) was sufficient.

The Thinking Process: What Happened Between "gpu use now" and "gpu done"

The interval between the user's two messages—"gpu use now" ([msg 228]) and "gpu done" ([msg 229])—was the GPU compute phase. This phase involved:

Host-to-device transfer: The CPU-synthesized circuit data (a/b/c vectors, witness values) was transferred from host memory to GPU VRAM. This is a significant data movement step, as the circuit for a 32 GiB PoRep contains millions of constraints.
NTT (Number Theoretic Transform) kernels: The GPU performed NTT operations on the polynomial representations of the circuit. This is a bandwidth-bound operation that benefits from the GPU's high memory bandwidth.
MSM (Multi-Scalar Multiplication) kernels: The GPU performed elliptic curve multi-scalar multiplications, which are the computational heart of the Groth16 proof. MSM is compute-bound and heavily optimized in the supraseal CUDA implementation.
Proof aggregation: The GPU combined the results of the NTT and MSM operations to produce the final Groth16 proof components (π_A, π_B, π_C). The user's observation that the GPU was "done" likely came from watching nvidia-smi output showing GPU utilization dropping from near-100% back to single digits. The assistant, upon receiving this message, would then check the daemon log to confirm proof completion and verification.

The Broader Significance

"gpu done" is a case study in how critical information is communicated in high-stakes engineering workflows. The message is terse because the context is rich. It is effective because the shared understanding between the user and the assistant is deep. It is a milestone because it validates months of work.

In the larger arc of the cuzk project, this message marked the transition from "does it work?" to "how well does it work?" The assistant's immediate next actions—adding tracing spans, timing breakdowns, Prometheus metrics, GPU detection, and graceful shutdown—were all enabled by the confidence that the core pipeline was functional. Without "gpu done," those hardening efforts would have been premature optimizations on an untested foundation.

The message also demonstrates a pattern common in human-AI collaboration: the human provides real-time sensory feedback (what they see on their terminal), while the AI provides analytical depth (understanding what it means in the context of the architecture). The two-word message and the assistant's ability to interpret it correctly represent a well-tuned communication channel built on shared context and mutual understanding.

In the end, "gpu done" was not just a status update. It was the first evidence that the cuzk proving engine was real—that it could take real data, run real computations on real hardware, and produce real proofs. Everything else—the optimizations, the benchmarks, the production deployment—would build on that foundation.