Model Detail
CUDA-Autocomplete
—CUDA-Autocomplete is a code generation model with 7B parameters released by NVIDIA. The model is registered under the text-generation pipeline tag on Hugging Face, distributed under a other license.
CUDA-Autocomplete ships with 7B parameters. Total weight footprint is approximately 7.6 GB, which is the relevant figure when planning local-inference VRAM. Distribution is governed by the other license — review the exact terms before commercial deployment.
CUDA-Autocomplete is best fit for code completion, repository-scale Q&A, and pair-programming integrations. It is a less obvious choice for one-shot generation of security-critical code without review. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Cross-Backend QIEO: Universal Runtime Portability across OpenMP5, CUDA, HIP, and Multi-Language Interfaces
arXiv:2609.30914v1 Announce Type: cross Abstract: Quantum-inspired algorithms emulate quantum mechanical principles, such as, superposition, interference, and probabilistic amplitude evolution, on classical hardware by representing candidate solutions as qubit vectors and evolving them through rotat
XLOG: A CUDA-Native Engine for Neurosymbolic Integration
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GPU-CFR: 80x Faster Counterfactual Regret Minimization by Compiling the Game to Static Dataflow and CUDA Graph Replay
arXiv:2609.11923v1 Announce Type: cross Abstract: Counterfactual regret minimization (CFR) is one of the few large numerical workloads that still runs faster on CPUs than on GPUs. Each iteration sweeps a game tree with up to billions of states in millions of small, interdependent gather and scatter
CUDA-Harness: Harnessing Agentic CUDA Kernel Generation and Optimization from Natural Language
arXiv:2609.00058v1 Announce Type: cross Abstract: Developing high-performance CUDA kernels demands specialized knowledge in algorithm implementation, correctness validation, and hardware-aware parallel optimization, creating a substantial expertise barrier and making generating CUDA kernels directly
M2K: Making the Model-Kernel Interface Explicit for Reliable CUDA Kernel Verification
arXiv:2603.24595v2 Announce Type: replace-cross Abstract: Large language model (LLM) inference systems rely on CUDA kernels for core GPU computations, yet the interface between models and kernels is implicit and poorly specified. Models and kernels evolve independently and often make incompatible as
Kernel Forge: An Agent Harness for LLM-based Generation and Optimization of CUDA Kernels
arXiv:2607.24762v1 Announce Type: new Abstract: Machine learning models are increasingly embedded in everyday software, and most of their runtime is spent in a small set of compute kernels such as matrix multiplication, convolution, and normalization. Optimizing these kernels is one of the most dire
