ComputeEval 2025.2 expands to 232 CUDA challenges, upping LLM test difficulty
ComputeEval 2.0 shows up this year as the newest checkpoint in our attempt to see how far large language models have come with low-level GPU code. The benchmark started out as a simple test for AI-generated CUDA snippets, and for a while it was the go-to reference for anyone curious about code-synthesis limits. In the last twelve months developers have been coaxing models to turn plain English prompts into working kernels, but most results still stuck to basic patterns.
This release stretches things out - it adds a whole set of problems that mirror the messier reality of today’s hardware. Tasks now expect the model to know about Tensor Cores, fiddly shared-memory tricks, and warp-level tricks, so it can’t just copy textbook examples any more. It likely reflects a deliberate move to check whether LLMs can actually keep up with the fast-changing toolbox GPU programmers use.
The dataset now contains 232 CUDA and CUDA Compute Core Libraries (CCCL) problems, with tougher challenges that push models toward modern features like Tensor Cores, advanced shared memory and warp-level techniques.
With this release, the dataset has grown to a total of 232 of CUDA and CUDA Compute Core Libraries (CCCL) problems. We deliberately raised the bar by adding more difficult challenges that require LLMs to use modern CUDA features, such as Tensor Cores, advanced shared memory patterns, and warp-level primitives. The new problems test the ability to correctly orchestrate features like CUDA Graphs, Streams, and Events.
All within the context of real-world applications like dynamic simulations. LLM performance on CUDA programming Our team evaluated several leading LLMs on ComputeEval to establish baseline performance metrics and understand the current state of AI-assisted CUDA programming (Table 1). We observed that scores for all models declined with the move to ComputeEval 2025.2.
ComputeEval’s newest version lists 232 CUDA and CCCL problems - that’s roughly a hundred more than before. The added tasks lean into newer CUDA tricks: Tensor Cores, tighter shared-memory patterns, even warp-level primitives. Whether today’s AI coding assistants can keep up is still up in the air.
The team behind it points out the suite is open source and meant to gauge how well models handle real-world GPU code. At the same time, the higher difficulty could surface flaws that earlier releases missed. Now researchers can check if generated kernels just compile or actually hit performance targets.
In my view, the benchmark’s usefulness will hinge on how consistently models turn those new constructs into fast code. The community will need to look at results on a range of GPUs before calling it a win. Until we see systematic studies, it’s unclear if the extra challenges will push real progress or merely expose the same old limits.
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Common Questions Answered
What new features does ComputeEval 2025.2 include to increase LLM test difficulty?
ComputeEval 2025.2 adds over a hundred new CUDA and CCCL problems that require the use of modern GPU features such as Tensor Cores, advanced shared‑memory patterns, and warp‑level primitives. The benchmark also tests orchestration of CUDA Graphs, Streams, and Events within realistic application scenarios.
How many total CUDA and CCCL challenges are now in the ComputeEval benchmark?
The latest release of ComputeEval contains a total of 232 distinct CUDA and CUDA Compute Core Libraries (CCCL) problems. This expansion more than doubles the number of challenges compared to earlier versions, providing a broader assessment of model capabilities.
Why do the authors emphasize that ComputeEval 2025.2 is open source?
The authors highlight the open‑source nature of ComputeEval to encourage community contributions and transparent evaluation of AI coding assistants on real‑world GPU programming tasks. Open access allows researchers to extend the benchmark, verify results, and collectively improve model performance on low‑level CUDA synthesis.
What real‑world GPU programming aspects are specifically targeted by the new ComputeEval challenges?
The new challenges focus on practical GPU programming constructs such as Tensor Core utilization, sophisticated shared‑memory layouts, warp‑level synchronization, and the coordination of CUDA Graphs, Streams, and Events. These elements reflect the complexities developers face when optimizing performance‑critical applications on modern NVIDIA hardware.