Rust Causal Conv1d for Mamba Model Blocks
Python CUDA ecosystem fails to build causal-conv1d for new GPUs. Need native Rust implementation in Candle for cross-platform support.
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Similar Problems
surfaced semanticallyTriton Causal Conv1d Update Breaks Autoregressive Token Decode
A Triton-based causal convolution kernel works correctly for forward passes but breaks during autoregressive decode, generating only one token before stopping. The monkey-patched update function is incompatible with token-by-token generation.
LLM Inference Frameworks Leave Most GPU Bandwidth Untapped
Conventional LLM inference stacks dispatch one kernel per operation, resulting in hundreds of kernel launches per token, repeated CPU round-trips, and significant memory re-fetching — leaving the majority of available GPU compute and bandwidth unused. This affects developers and researchers running local or self-hosted inference on consumer and prosumer NVIDIA hardware. The gap between theoretical hardware capability and realized throughput is large, but this post is primarily a project announcement rather than a problem statement from users experiencing pain.
VLM Model Wrapper Lacks Piecewise CUDAGraph Support
Piecewise cudagraph is not supported for VLM model wrappers in the auto-deploy pipeline. Users deploying vision-language models like Qwen3.5 cannot leverage cudagraph optimizations for the text model component.
Quadratic Attention Complexity Bottleneck in Small Language Model Inference
A researcher building a small Rust-focused language model from scratch encountered severe inference slowdowns due to the O(n²) complexity of standard full attention mechanisms. To address this, they forked PyTorch and Triton internals to implement a hybrid attention scheme combining local windowed attention with a GRU-style recurrent path, achieving a reported 50x speedup at modest perplexity cost. This is shared as an experimental finding rather than a validated, reproducible problem with broad user evidence.
Candle Framework Needs Qwen3.5-VL Visual Encoder Support
The Candle Rust ML framework lacks native support for Qwen3.5-VL visual encoder blocks. Developers cannot run vision-language models natively without implementing the visual transformer architecture from scratch.
Problem descriptions, scores, analysis, and solution blueprints may be updated as new community data becomes available.