{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-node-qnn-llm","slug":"node-qnn-llm","name":"node-qnn-llm","type":"repo","url":"https://github.com/mybigday/node-qnn-llm#readme","page_url":"https://unfragile.ai/node-qnn-llm","categories":["frameworks-sdks"],"tags":["qualcomm","npu","llm","llama","ai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-node-qnn-llm__cap_0","uri":"capability://code.generation.editing.qualcomm.npu.accelerated.llm.inference.via.qnn.runtime","name":"qualcomm npu-accelerated llm inference via qnn runtime","description":"Provides native Node.js bindings to Qualcomm's QNN (Qualcomm Neural Network) SDK, enabling LLM inference execution directly on Snapdragon NPUs (Neural Processing Units) rather than CPU or GPU. The binding wraps QNN's C++ runtime APIs, allowing developers to load quantized LLM models (particularly Llama variants) and execute forward passes with hardware acceleration on compatible Snapdragon processors. This approach offloads computation to specialized silicon, reducing power consumption and latency compared to CPU-only inference.","intents":["Run LLMs locally on mobile/edge devices with Snapdragon processors without cloud dependency","Reduce inference latency and power consumption for on-device AI applications","Deploy quantized language models on IoT and embedded systems with NPU support","Build privacy-preserving chatbots that execute entirely on user hardware"],"best_for":["Mobile app developers targeting Snapdragon-equipped Android devices","Edge AI engineers building on-device inference pipelines","IoT teams deploying LLMs on resource-constrained hardware","Privacy-focused teams requiring local-only model execution"],"limitations":["Requires Snapdragon processor with QNN-compatible NPU (not available on all devices)","Limited to quantized models optimized for QNN (typically INT8 or lower precision)","No GPU fallback — inference fails gracefully on unsupported hardware without automatic CPU delegation","QNN SDK licensing and availability restrictions may apply depending on Qualcomm partnership tier","Model conversion/optimization pipeline not included — requires external tools to prepare models for QNN"],"requires":["Node.js 14+ (native module compilation support)","Qualcomm QNN SDK installed and configured on build system","Target device with Snapdragon processor featuring QNN-capable NPU","Pre-quantized LLM model compatible with QNN format (e.g., Llama 2 quantized variants)","Android NDK for cross-compilation to ARM64 architecture"],"input_types":["quantized model files (QNN-optimized format)","text prompts (string input for LLM inference)","model configuration metadata"],"output_types":["text tokens (generated LLM output)","token logits (raw model predictions)","inference timing metrics"],"categories":["code-generation-editing","edge-ai","hardware-acceleration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-node-qnn-llm__cap_1","uri":"capability://code.generation.editing.llama.model.loading.and.tokenization.with.qnn.backend","name":"llama model loading and tokenization with qnn backend","description":"Implements Llama-specific model loading logic that parses Llama weights, initializes the QNN computation graph, and provides tokenization via integrated or external tokenizer bindings. The capability handles model state initialization, weight quantization validation, and token encoding/decoding for Llama architectures specifically, bridging the gap between Llama model artifacts and QNN's generic tensor execution layer. Supports streaming token generation with proper context management.","intents":["Load pre-trained Llama 2/3 models and prepare them for inference on Snapdragon NPU","Tokenize user input text using Llama's vocabulary and BPE tokenizer","Generate text token-by-token with streaming output for interactive applications","Manage model state and context windows across multiple inference calls"],"best_for":["Developers building Llama-based chatbots for mobile devices","Teams migrating from cloud-based Llama inference to on-device execution","Applications requiring streaming text generation with low latency"],"limitations":["Llama-specific implementation — does not support other model architectures (Mistral, Qwen, etc.)","Tokenizer must be provided separately or bundled (no built-in SentencePiece integration documented)","Context window limited by NPU memory constraints (typically 2K-4K tokens on mobile Snapdragon)","No automatic model quantization — assumes models are pre-quantized to QNN-compatible format"],"requires":["Llama model weights in QNN-compatible quantized format","Llama tokenizer (SentencePiece .model file or equivalent)","QNN SDK with Llama operator support","Node.js native module build environment"],"input_types":["model weight files (binary quantized format)","text prompts (UTF-8 strings)","tokenizer vocabulary files"],"output_types":["token IDs (integer sequences)","generated text (string output)","token probabilities (optional logits)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-node-qnn-llm__cap_2","uri":"capability://text.generation.language.streaming.token.generation.with.configurable.sampling.strategies","name":"streaming token generation with configurable sampling strategies","description":"Provides token-by-token generation with support for multiple sampling methods (temperature, top-k, top-p) to control output diversity and coherence. The implementation iteratively calls the QNN inference engine, applies sampling logic to the output logits, and yields tokens as they are generated, enabling real-time streaming responses. Supports early stopping conditions (EOS token detection, max length) and allows fine-grained control over generation parameters.","intents":["Generate natural language responses with real-time streaming for chat interfaces","Control output randomness and diversity via temperature and nucleus sampling","Implement early stopping to prevent runaway generation or respect token budgets","Build interactive applications where users see text appearing token-by-token"],"best_for":["Chat application developers building responsive user interfaces","Teams building streaming APIs that need per-request sampling configuration","Applications with strict latency budgets requiring token-level control"],"limitations":["Sampling logic runs on CPU (not NPU-accelerated), adding ~5-10ms per token overhead","No beam search or other advanced decoding strategies — only single-path greedy/sampling","Temperature and top-k/top-p parameters are global per generation call, not per-token adaptive","No built-in repetition penalty or other advanced generation constraints"],"requires":["Loaded Llama model with QNN backend","Tokenizer for encoding prompts and decoding output tokens","Node.js async/await or callback-based event handling for streaming"],"input_types":["prompt text (string)","sampling parameters (temperature: 0-2, top_k: integer, top_p: 0-1)","generation constraints (max_tokens, stop_sequences)"],"output_types":["token stream (async iterable or callback-based)","generated text (accumulated string)","generation metadata (token count, timing)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-node-qnn-llm__cap_3","uri":"capability://automation.workflow.npu.memory.management.and.model.quantization.validation","name":"npu memory management and model quantization validation","description":"Handles allocation and lifecycle management of NPU memory buffers for model weights and inference activations, including validation that loaded models match QNN's quantization requirements (typically INT8 or lower precision). The binding tracks memory usage, prevents buffer overflows, and provides diagnostics for out-of-memory conditions. Includes utilities to verify model compatibility before attempting inference and to estimate memory footprint based on model size and quantization level.","intents":["Validate that a model is properly quantized before loading it onto the NPU","Monitor NPU memory usage to prevent out-of-memory crashes during inference","Estimate whether a model will fit on a target device before deployment","Debug memory-related issues and optimize model size for constrained devices"],"best_for":["Mobile app developers deploying to devices with limited NPU SRAM (typically 512MB-2GB)","Teams building model selection logic based on device capabilities","Embedded systems engineers optimizing for power and memory constraints"],"limitations":["No automatic model quantization — only validates pre-quantized models","Memory estimates are approximate and may not account for runtime fragmentation","No memory pooling or optimization strategies — uses simple linear allocation","Limited visibility into QNN's internal memory management — diagnostics are indirect"],"requires":["Model metadata indicating quantization level and size","QNN SDK with memory profiling support","Device with known NPU memory capacity"],"input_types":["model files (binary format with quantization metadata)","device specifications (NPU memory capacity)"],"output_types":["validation results (boolean + error messages)","memory usage estimates (bytes)","compatibility reports (device-model pairs)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-node-qnn-llm__cap_4","uri":"capability://automation.workflow.batch.inference.with.multi.prompt.processing","name":"batch inference with multi-prompt processing","description":"Supports processing multiple prompts in a single inference batch to improve throughput and hardware utilization. The implementation groups prompts, pads sequences to uniform length, executes a single QNN forward pass over the batch, and unpacks results back to individual prompts. Enables efficient processing of multiple requests without sequential per-prompt overhead, though with latency-throughput tradeoffs depending on batch size and sequence length variance.","intents":["Process multiple user queries simultaneously to improve overall throughput","Reduce per-request latency overhead by amortizing model loading and initialization costs","Build server-side inference endpoints that handle concurrent requests efficiently","Maximize NPU utilization by keeping the accelerator busy with multiple workloads"],"best_for":["Server-side inference services handling multiple concurrent requests","Batch processing pipelines (e.g., analyzing multiple documents)","Applications with bursty traffic patterns where batching reduces tail latency"],"limitations":["Batch size is limited by NPU memory — typically 2-8 on mobile Snapdragon devices","Sequence length padding adds overhead if prompts vary significantly in length","No dynamic batching — batch size must be fixed at initialization","Latency increases with batch size (linear scaling with number of prompts)","Requires all prompts to complete before returning any results (no streaming per-prompt)"],"requires":["QNN SDK with batch processing support","Sufficient NPU memory for batch_size × max_sequence_length × model_size","Batch size configuration at model initialization time"],"input_types":["list of text prompts (string array)","batch size (integer, fixed at init)","sampling parameters (applied uniformly to all prompts)"],"output_types":["list of generated texts (string array, same order as input)","per-prompt generation metadata (tokens, timing)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-node-qnn-llm__cap_5","uri":"capability://automation.workflow.model.caching.and.hot.reload.with.zero.downtime.updates","name":"model caching and hot-reload with zero-downtime updates","description":"Implements in-memory model caching to avoid reloading weights from disk on every inference call, and provides hot-reload capability to swap model versions without stopping the inference service. The binding maintains a model registry, tracks reference counts, and coordinates transitions between model versions to ensure in-flight requests complete before unloading old models. Enables A/B testing different model versions and rapid iteration without service interruption.","intents":["Avoid repeated disk I/O and model initialization overhead for high-throughput inference","Deploy model updates without restarting the Node.js process or dropping requests","Run A/B tests comparing different model versions on live traffic","Manage multiple model versions in memory simultaneously for fallback/routing"],"best_for":["Production inference services requiring high availability","Teams iterating on model versions and needing rapid deployment cycles","Applications comparing multiple model variants (e.g., different quantization levels)"],"limitations":["Requires sufficient memory to hold multiple model versions simultaneously","Hot-reload coordination adds complexity — race conditions possible if not carefully managed","No automatic versioning or rollback — manual coordination required","Cache invalidation strategy not specified — unclear when old models are evicted","Caching is process-local — does not work across multiple Node.js instances without external coordination"],"requires":["Sufficient NPU and system memory for multiple model versions","Model versioning/tagging system to identify which version to load","Graceful shutdown logic to drain in-flight requests before model swap"],"input_types":["model identifier (name + version)","model file path or URL","cache eviction policy (LRU, TTL, etc.)"],"output_types":["cache hit/miss status","model reference (for inference)","cache statistics (size, hit rate)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["Node.js 14+ (native module compilation support)","Qualcomm QNN SDK installed and configured on build system","Target device with Snapdragon processor featuring QNN-capable NPU","Pre-quantized LLM model compatible with QNN format (e.g., Llama 2 quantized variants)","Android NDK for cross-compilation to ARM64 architecture","Llama model weights in QNN-compatible quantized format","Llama tokenizer (SentencePiece .model file or equivalent)","QNN SDK with Llama operator support","Node.js native module build environment","Loaded Llama model with QNN backend"],"failure_modes":["Requires Snapdragon processor with QNN-compatible NPU (not available on all devices)","Limited to quantized models optimized for QNN (typically INT8 or lower precision)","No GPU fallback — inference fails gracefully on unsupported hardware without automatic CPU delegation","QNN SDK licensing and availability restrictions may apply depending on Qualcomm partnership tier","Model conversion/optimization pipeline not included — requires external tools to prepare models for QNN","Llama-specific implementation — does not support other model architectures (Mistral, Qwen, etc.)","Tokenizer must be provided separately or bundled (no built-in SentencePiece integration documented)","Context window limited by NPU memory constraints (typically 2K-4K tokens on mobile Snapdragon)","No automatic model quantization — assumes models are pre-quantized to QNN-compatible format","Sampling logic runs on CPU (not NPU-accelerated), adding ~5-10ms per token overhead","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.06646419755561254,"quality":0.22,"ecosystem":0.55,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-04-22T08:08:13.652Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":231,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=node-qnn-llm","compare_url":"https://unfragile.ai/compare?artifact=node-qnn-llm"}},"signature":"VQR2J6GrkBHmsdAPKf+nWwzPdmpQv7TxG067SSnBHGW86E6iPe72KHNJnhTQ/4sZ/RIfYHufftftoCwUnSt+AQ==","signedAt":"2026-06-21T23:42:30.431Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/node-qnn-llm","artifact":"https://unfragile.ai/node-qnn-llm","verify":"https://unfragile.ai/api/v1/verify?slug=node-qnn-llm","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}