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.
NPU (Ascend) kernel support missing in flash-linear-attention
Flash-linear-attention lacks native NPU/Ascend hardware support, requiring complex adaptation work. This RFC outlines CI setup and kernel porting order. Very niche audience of ML engineers targeting Ascend hardware.
Problem descriptions, scores, analysis, and solution blueprints may be updated as new community data becomes available.