Mesh LLM
Official@mesh-llm
Democratize and Decentralized Models and Agents.
Agent Skills by Mesh LLM
Showing 28 vetted skills indexed across 1 GitHub repositories.
Mesh-LLMbenchmark-tune
Runs mesh-llm benchmark tune sweeps to optimize model-serving throughput settings.
Mesh-LLMplugin-web-ui-extension
Maintains the plugin web UI projection contract, exemplar coverage, and recovery flow for mesh-llm.
Mesh-LLMrelease-validation
Validate MeshLLM release candidates against the previous GitHub release with evidence-backed reports.
Mesh-LLMmanage-ci
Governs inspection, editing, and validation of MeshLLM GitHub Actions CI workflows.
Mesh-LLMhf-quant-and-layer-package-jobs
Runs GGUF quantization and Skippy layer-package creation as one local or Hugging Face Jobs workflow.
Mesh-LLMhf-gguf-quant-jobs
Quantize split BF16/FP16 GGUF model repos into custom quant GGUF repos with skippy-quantize.
Mesh-LLMllama-patch-changes
Manage llama.cpp patch queues and upstream pins for Mesh-LLM builds.
Mesh-LLMmetrics-server
Ingest OTLP telemetry into DuckDB and export benchmark reports via HTTP API.
Mesh-LLMskippy-family-certification
Certifies GGUF model families for staged splits via parity and topology checks.
Mesh-LLMskippy-server
Run the skippy-server backend runtime for binary stage transport and OpenAI integration.
Mesh-LLMtelemetry-privacy-review
Audits OTLP metrics and telemetry attributes for privacy risks before changes.
Mesh-LLMskippy-metrics
Standardize telemetry attributes and benchmark metrics across Skippy components.
Mesh-LLMskippy-correctness
Validate staged Skippy execution parity against full-model results across deployments.
Mesh-LLMskippy-spec-bench
Benchmark target and draft GGUF pairs to verify decoding compatibility.
Mesh-LLMhf-layer-package-jobs
Automate planning, validation, and submission of Hugging Face GGUF layer-package jobs.
Mesh-LLMllama-stage-patch-changes
Regenerate the llama-stage.cpp patch queue and update the upstream SHA pin.
Mesh-LLMskippy-prompt
Orchestrates setup and lifecycle of prompt-owned staged workflows across multiple hosts.
Mesh-LLMkv-tool-loop-stability
Certify KV/cache stability of mesh-llm OpenAI-compatible endpoints under repeated tool-loop activity.
Mesh-LLMskippy-bench
Coordinate Skippy stage performance benchmarks and generate consistent reports.
Mesh-LLMskippy-model-package
Inspect GGUF model packages and plan layer ranges for staged deployment workflows.
Mesh-LLMskippy-cache-family-bench
Benchmark Skippy cache by model family and generate README-ready tables.
Mesh-LLMremote-observable-process
Start, supervise, debug, and stop remote processes over SSH with TTY sessions.
Mesh-LLMconfig-settings-management
Validate mesh-llm TOML configuration changes against runtime schemas.
Mesh-LLMdeploy-windows
Automate Windows deployment of Mesh LLM via PowerShell install.ps1.
Frequently Asked Questions About Mesh LLM
FAQPage SchemaWhat specific tasks are enabled by Mesh LLM?▼
Mesh LLM enables the partitioning of GGUF model layers, distributed node synchronization, and rigorous performance benchmarking. It facilitates OTLP telemetry ingestion into DuckDB, KV-cache stability verification, and the management of upstream patch queues for custom inference builds.
Which personas benefit from these capabilities?▼
Infrastructure engineers, machine learning researchers, and systems architects focused on distributed inference benefit from these capabilities. It is designed for teams managing multi-node GPU clusters who require granular control over model layer distribution, performance metrics, and runtime stability across heterogeneous hardware environments.
What are the prerequisites for deploying Mesh LLM?▼
Deployment requires Linux GPU nodes with CUDA support, macOS, or Windows environments. Users must manage TOML configuration files validated against runtime schemas and ensure SSH connectivity for remote process supervision and node-to-node mesh coordination.