ML Inference Lacks Generalized Low-Latency GEMM Kernels with Broad Precision Support
Current low-latency GPU GEMM kernels for ML inference only support specific shapes and bf16 precision. Engineers need generalized versions supporting fp8, nvfp4, and arbitrary shapes for flexible model deployment with PDL after auto-regressive decoding.
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Similar Problems
surfaced semanticallyFP8 Quantization Support for Older Nvidia GPUs
Request to support NVFP4 models on Turing and Ampere GPUs by implementing FP8ScaledMMLinearKernel via Marlin FP8.
DeepSeek-V4 Flash inference fails on widely-deployed A100/A800 Ampere GPUs
vLLM's DeepSeek-V4-Flash image fails on sm_80 (A100/A800) due to DeepGEMM/HyperConnection kernel architecture checks. Operators want a slower fallback so existing Ampere clusters remain usable.
No Framework Support for MiniMax Sparse Attention in Long-Context Inference
ML inference frameworks lack support for MiniMax Sparse Attention (MSA), a technique from MiniMax-M3 that reduces attention computation cost while preserving quality. Teams running long-context workloads cannot take advantage of this efficiency gain without manual implementation. The absence of framework-level support creates a performance and cost gap for production deployments.
Latest Deepseek models unsupported in local inference frameworks
Deepseek V4-Flash and other new models lack support outside VLLM, leaving users unable to run them locally through popular frameworks. Delay between model release and framework integration blocks experimentation.
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.
Problem descriptions, scores, analysis, and solution blueprints may be updated as new community data becomes available.