Pod OOMKilled Loading ONNX Sentence-Transformers Model Under 2Gi Limit
A FastAPI service serving a multilingual embedding model via ONNX on CPU is killed for exceeding a 2 GiB Kubernetes memory limit at load time. The image carries large PyTorch and cache footprints, and the author asks for production memory-reduction practices.
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Similar Problems
surfaced semanticallyMultiple Fine-Tuned ML Models Consume Excessive Memory on Budget VPS Infrastructure
Running several specialized fine-tuned models in parallel for ML pipelines creates prohibitive memory overhead on affordable VPS instances, limiting deployment options for cost-conscious developers. Model consolidation techniques reduce memory dramatically but require significant engineering effort to implement.
No Way to Locally Reproduce Kubernetes Memory Limits Before Shipping to Production
Developers who test locally with docker compose never see OOM kills, because compose doesn't enforce memory limits the way a Kubernetes Deployment does, so containers that run fine locally get OOMKilled the moment resource limits are applied in the cluster. There is no straightforward way to reproduce Kubernetes-style memory enforcement locally to size limits deliberately. This forces developers into a trial-and-error loop of bumping memory limits in production until kills stop.
Lack of Tooling to Deploy Neural Networks to Non-Linux Embedded C Targets
A developer needs to run a Python-trained neural network on an embedded controller with no Linux, limited CPU/memory, minimal runtime dependencies, and predictable real-time behavior, but existing frameworks like ONNX Runtime mostly target desktop or embedded-Linux environments. This points to a broader gap in tooling for generating standalone, dependency-light C code from trained models for bare-metal or RTOS embedded targets.
Run MoE models larger than RAM via SSD expert streaming
Mixture-of-Experts models are typically limited by available system RAM because all expert weights must be loaded at once. This request proposes streaming only the active experts from SSD into a small RAM cache on demand, allowing much larger MoE models to run on hardware that could not otherwise hold them.
On-Device RAG Apps Crash or Stall on Low-End Android Phones
Developers building offline RAG Android apps face OOM crashes on low-end devices. Small models like SmolLM 135M cannot follow instructions well, while capable 2.5B models require too much RAM. There is no good middle ground for cross-device LLM inference.
Problem descriptions, scores, analysis, and solution blueprints may be updated as new community data becomes available.