{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-vllm-project--vllm","slug":"vllm-project--vllm","name":"vllm","type":"platform","url":"https://vllm.ai","page_url":"https://unfragile.ai/vllm-project--vllm","categories":["deployment-infra"],"tags":["amd","blackwell","cuda","deepseek","deepseek-v3","gpt","gpt-oss","inference","kimi","llama","llm","llm-serving","model-serving","moe","openai","pytorch","qwen","qwen3","tpu","transformer"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-vllm-project--vllm__cap_0","uri":"capability://automation.workflow.batched.token.generation.with.continuous.batching.scheduler","name":"batched token generation with continuous batching scheduler","description":"Implements a continuous batching scheduler that dynamically groups inference requests into GPU batches without waiting for all requests to complete, using the Scheduler and InputBatch state management system. Requests are added/removed mid-batch as they finish, maximizing GPU utilization by eliminating idle cycles between request completion and new request arrival. The scheduler tracks request state through the RequestLifecycle and allocates KV cache slots dynamically.","intents":["Maximize GPU throughput by keeping the GPU busy with multiple concurrent requests","Reduce per-token latency by batching heterogeneous request lengths","Handle variable-length sequences without padding waste"],"best_for":["Production LLM serving infrastructure teams","High-throughput inference deployments with many concurrent users","Cost-conscious organizations optimizing GPU utilization per dollar"],"limitations":["Continuous batching adds ~5-15ms scheduling overhead per batch iteration","Memory fragmentation can occur with highly variable sequence lengths","Requires careful tuning of max_batch_size and max_num_seqs for optimal performance"],"requires":["CUDA 11.8+ or ROCm 5.7+ for GPU acceleration","GPU with sufficient VRAM for KV cache (typically 16GB+ for production models)","Python 3.9+"],"input_types":["text prompts","token IDs","sampling parameters (temperature, top_p, top_k)"],"output_types":["token sequences","logits","completion metadata"],"categories":["automation-workflow","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vllm-project--vllm__cap_1","uri":"capability://memory.knowledge.multi.level.kv.cache.management.with.prefix.caching","name":"multi-level kv cache management with prefix caching","description":"Manages GPU KV cache allocation across concurrent requests using a hierarchical slot-based allocator with support for prefix caching, which reuses KV cache blocks for repeated prompt prefixes across requests. The system tracks cache block ownership, eviction policies, and supports disaggregated serving where KV cache can be transferred between workers. Implements block-level granularity to minimize memory fragmentation and enable cache sharing across requests with common prefixes (e.g., system prompts, RAG context).","intents":["Reduce memory footprint when serving requests with shared context/prompts","Enable KV cache reuse across requests to lower latency for repeated prefixes","Support disaggregated inference where compute and cache storage are separated"],"best_for":["Multi-tenant SaaS platforms with shared system prompts or RAG contexts","Batch inference on similar documents or conversations","Large-scale deployments where cache efficiency directly impacts cost"],"limitations":["Prefix caching requires exact token-level matching; minor prompt variations bypass cache","Block-level allocation adds ~2-5% memory overhead for metadata tracking","Cache invalidation on model weight updates requires full cache flush","Disaggregated serving introduces network latency for cache transfers (typically 10-50ms)"],"requires":["GPU with unified memory or explicit cache management (NVIDIA A100+, H100 recommended)","Sufficient GPU VRAM to hold KV cache for target batch size","Python 3.9+","CUDA 11.8+ or ROCm 5.7+"],"input_types":["prompt tokens","sequence lengths","request metadata"],"output_types":["cache block allocations","eviction decisions","cache hit/miss statistics"],"categories":["memory-knowledge","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vllm-project--vllm__cap_10","uri":"capability://memory.knowledge.model.registry.with.automatic.architecture.detection","name":"model registry with automatic architecture detection","description":"Provides a Model Registry that automatically detects model architectures from HuggingFace model IDs and loads appropriate model implementations. The system uses configuration parsing to identify model type (LLaMA, Qwen, Mixtral, etc.), then selects the corresponding modeling backend from the Transformers Modeling Backend. Supports custom model registration for non-standard architectures, enabling extensibility without modifying core code.","intents":["Load models from HuggingFace Hub without manual architecture specification","Automatically select optimized implementations for known model architectures","Support custom models through extensible registration system"],"best_for":["Teams wanting to serve multiple model architectures without configuration","Rapid prototyping with different models from HuggingFace Hub","Production deployments requiring automatic model detection"],"limitations":["Architecture detection relies on model config.json; non-standard configs may fail","Custom architectures require manual registration; no automatic detection for unknown models","Model loading time includes architecture detection overhead (~100-500ms)","Some model variants (e.g., quantized versions) may not be detected correctly"],"requires":["Model available on HuggingFace Hub or local filesystem","Model config.json with standard architecture specification","Python 3.9+","transformers library 4.30+"],"input_types":["model ID (e.g., 'meta-llama/Llama-2-7b')","model configuration","custom architecture definitions"],"output_types":["loaded model instance","architecture metadata","model capabilities"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vllm-project--vllm__cap_11","uri":"capability://automation.workflow.attention.backend.selection.with.flashattention.and.flashinfer.optimization","name":"attention backend selection with flashattention and flashinfer optimization","description":"Implements an Attention Backend Selection system that automatically chooses the optimal attention implementation based on hardware capabilities and model requirements. Supports multiple attention backends including FlashAttention (fast approximate attention), FlashInfer (optimized for inference), and platform-specific implementations (ROCm, TPU). The system benchmarks available backends at startup and selects the fastest option, with fallback to standard attention if specialized backends are unavailable.","intents":["Maximize attention computation speed by selecting hardware-optimized implementations","Reduce memory bandwidth requirements through approximate attention methods","Automatically adapt to different hardware (NVIDIA, AMD, TPU) without manual configuration"],"best_for":["Production deployments where attention is a bottleneck (typically 30-50% of compute)","Teams with heterogeneous hardware (mix of GPU types) requiring automatic optimization","High-throughput inference where memory bandwidth is limited"],"limitations":["FlashAttention introduces ~0.1-0.5% accuracy loss due to approximation","Backend selection adds ~1-2 second startup overhead for benchmarking","Some backends (FlashInfer) are NVIDIA-specific; AMD/TPU support is limited","Attention backend switching requires model reload; cannot change at runtime","Custom attention patterns (e.g., sparse attention) may not be supported by all backends"],"requires":["GPU with attention backend support (NVIDIA A100+ for FlashAttention, H100 for FlashInfer)","CUDA 11.8+ or ROCm 5.7+","Python 3.9+","flash-attn library (optional, for FlashAttention)"],"input_types":["model architecture","hardware specifications","attention configuration"],"output_types":["selected attention backend","performance benchmarks","attention outputs"],"categories":["automation-workflow","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vllm-project--vllm__cap_12","uri":"capability://automation.workflow.metrics.collection.and.observability.with.performance.tracking","name":"metrics collection and observability with performance tracking","description":"Provides comprehensive metrics collection through a Metrics and Observability system that tracks request latency, throughput, GPU utilization, cache hit rates, and other performance indicators. Metrics are collected at multiple levels: request-level (time-to-first-token, inter-token latency), batch-level (batch size, batch composition), and system-level (GPU memory, compute utilization). Integrates with monitoring systems through Prometheus-compatible metrics export.","intents":["Monitor inference performance and identify bottlenecks","Track system health and resource utilization in production","Debug performance issues through detailed request-level metrics"],"best_for":["Production deployments requiring performance monitoring","Teams optimizing inference performance and resource utilization","SaaS platforms tracking per-customer performance metrics"],"limitations":["Metrics collection adds 1-3% overhead to inference latency","Detailed per-request metrics can consume significant memory in high-throughput scenarios","Metrics export to external systems (Prometheus, CloudWatch) requires network I/O","Historical metrics retention requires external storage; vLLM doesn't persist metrics"],"requires":["Python 3.9+","Prometheus client library (optional, for metrics export)","Monitoring infrastructure (Prometheus, Grafana, CloudWatch, etc.)"],"input_types":["request metadata","batch information","GPU statistics"],"output_types":["performance metrics","prometheus-compatible metrics","performance reports"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vllm-project--vllm__cap_13","uri":"capability://automation.workflow.offline.inference.with.batch.processing.and.file.based.i.o","name":"offline inference with batch processing and file-based i/o","description":"Supports offline inference mode for batch processing where requests are read from files or data structures, processed in optimized batches, and results written to output files. The offline mode bypasses the HTTP server and request queue, enabling higher throughput for non-interactive workloads. Supports various input formats (JSONL, CSV, Parquet) and output serialization formats, with automatic batch composition for maximum GPU utilization.","intents":["Process large datasets efficiently through batch inference without HTTP overhead","Generate embeddings or completions for entire datasets in a single run","Integrate inference into data pipelines with file-based I/O"],"best_for":["Batch processing jobs (e.g., nightly inference on large datasets)","Data pipeline integration where HTTP API is overkill","Cost-sensitive workloads where throughput is more important than latency"],"limitations":["Offline mode requires all data to be available upfront; cannot stream new requests","No request prioritization or dynamic batching; batch size is fixed","Error handling is limited; failed requests may not be retried automatically","Output ordering may not match input ordering if requests complete out-of-order"],"requires":["Input data in supported format (JSONL, CSV, Parquet, etc.)","Python 3.9+","Sufficient disk space for output files"],"input_types":["JSONL files","CSV files","Parquet files","Python lists/dicts"],"output_types":["JSONL output","CSV output","Parquet output","Python objects"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vllm-project--vllm__cap_2","uri":"capability://automation.workflow.speculative.decoding.with.draft.model.acceleration","name":"speculative decoding with draft model acceleration","description":"Implements speculative decoding by running a smaller draft model to generate candidate tokens, then verifying them against the target model in parallel. The system uses a two-stage pipeline: draft model generates k tokens speculatively, then the target model validates all k tokens in a single forward pass. If verification succeeds, all k tokens are accepted; otherwise, the system falls back to the last verified token and continues. This reduces effective latency by amortizing target model inference across multiple tokens.","intents":["Reduce end-to-end latency for token generation by 1.5-3x using smaller draft models","Maintain output quality while accelerating inference through verification","Enable cost-effective inference by using cheaper draft models for speculation"],"best_for":["Interactive applications requiring low latency (chatbots, real-time assistants)","Cost-sensitive deployments where draft model inference is cheaper than target model","Scenarios with high variance in token generation (where speculation helps most)"],"limitations":["Requires a compatible draft model (typically 0.5-1B parameters for 7B+ target models)","Speculative tokens may be rejected, wasting draft model compute (~10-30% rejection rate typical)","Adds complexity to request scheduling and output handling","Not beneficial for batch inference where latency is less critical than throughput"],"requires":["Two models loaded simultaneously (target + draft), requiring 1.5-2x VRAM vs single model","Draft model must be compatible with target model tokenizer","GPU with sufficient memory for parallel execution (A100 80GB+ recommended)","Python 3.9+"],"input_types":["prompt tokens","draft model outputs","verification parameters"],"output_types":["verified token sequences","acceptance/rejection decisions","latency metrics"],"categories":["automation-workflow","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vllm-project--vllm__cap_3","uri":"capability://automation.workflow.multi.gpu.distributed.inference.with.tensor.pipeline.parallelism","name":"multi-gpu distributed inference with tensor/pipeline parallelism","description":"Supports distributed execution across multiple GPUs using tensor parallelism (splitting model layers across GPUs) and pipeline parallelism (splitting model stages across GPUs), coordinated through a multi-process engine architecture. The system uses NCCL for inter-GPU communication and implements a Communication Infrastructure layer that handles collective operations (all-reduce, all-gather) for gradient/activation synchronization. Workers are managed through the Worker and Executor Architecture, with each worker running on a separate GPU and coordinating through the EngineCore.","intents":["Serve models larger than single GPU VRAM by splitting across multiple GPUs","Reduce per-token latency by parallelizing computation across GPUs","Scale inference throughput by distributing batch processing across multiple GPUs"],"best_for":["Large model serving (70B+ parameters) requiring multi-GPU setups","High-throughput production deployments with multiple GPUs available","Teams with access to GPU clusters (8+ GPUs) for distributed inference"],"limitations":["Inter-GPU communication overhead (typically 5-15% of compute time) reduces scaling efficiency","Tensor parallelism requires careful load balancing; uneven layer sizes cause GPU idle time","Pipeline parallelism introduces pipeline bubbles (10-20% compute waste) due to sequential stages","Requires all GPUs to be on same machine or high-bandwidth interconnect (NVLink/InfiniBand)","Debugging distributed inference is significantly more complex than single-GPU"],"requires":["Multiple GPUs (2+ for tensor parallelism, 4+ for pipeline parallelism recommended)","NCCL 2.14+ for inter-GPU communication","High-bandwidth GPU interconnect (NVLink preferred, PCIe acceptable)","CUDA 11.8+ or ROCm 5.7+","Python 3.9+"],"input_types":["prompt tokens","model configuration","parallelism strategy specification"],"output_types":["token sequences","distributed execution logs","performance metrics per GPU"],"categories":["automation-workflow","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vllm-project--vllm__cap_4","uri":"capability://data.processing.analysis.quantization.with.fp8.and.low.precision.inference","name":"quantization with fp8 and low-precision inference","description":"Supports multiple quantization methods including FP8 (8-bit floating point), INT8, and INT4 to reduce model size and memory footprint while maintaining inference quality. The system implements quantization through a modular backend that applies quantization to weights and activations, with support for per-channel and per-token quantization. FP8 quantization is particularly optimized for NVIDIA GPUs with native FP8 support (H100, L40S), using hardware-accelerated matrix operations to minimize performance overhead.","intents":["Reduce model size by 4-8x to fit larger models on limited GPU VRAM","Lower memory bandwidth requirements to improve throughput on bandwidth-limited GPUs","Deploy cost-effectively on cheaper GPUs with lower memory capacity"],"best_for":["Edge deployment and resource-constrained environments","Cost-sensitive inference where model size directly impacts hardware costs","High-throughput serving where memory bandwidth is the bottleneck"],"limitations":["FP8 quantization introduces 0.5-2% accuracy loss on most models (task-dependent)","INT4 quantization can cause 2-5% accuracy degradation on complex reasoning tasks","Quantization requires calibration on representative data; poor calibration causes quality loss","Not all model architectures support quantization equally; some layers are difficult to quantize","FP8 hardware support limited to recent GPUs (H100, L40S, A100 with limited support)"],"requires":["GPU with quantization support (NVIDIA A100+ for FP8, any GPU for INT8/INT4)","CUDA 11.8+ or ROCm 5.7+","Quantized model weights (pre-quantized or quantization script)","Python 3.9+"],"input_types":["full-precision model weights","calibration data","quantization configuration"],"output_types":["quantized weights","quantization scales/zero-points","accuracy metrics"],"categories":["data-processing-analysis","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vllm-project--vllm__cap_5","uri":"capability://automation.workflow.mixture.of.experts.moe.optimization.with.fused.kernels","name":"mixture-of-experts (moe) optimization with fused kernels","description":"Optimizes inference for Mixture-of-Experts models through a FusedMoE layer architecture that combines expert selection, routing, and computation into fused CUDA kernels. The system implements efficient expert parallelism where experts are distributed across GPUs, with optimized all-to-all communication for token-to-expert routing. Supports both dense and sparse MoE patterns, with automatic kernel selection based on sparsity and hardware capabilities.","intents":["Efficiently serve MoE models (e.g., Mixtral, DeepSeek-MoE) with minimal routing overhead","Reduce latency for MoE inference by fusing routing and expert computation","Scale MoE models across multiple GPUs with optimized expert parallelism"],"best_for":["Teams deploying Mixture-of-Experts models (Mixtral 8x7B, DeepSeek-MoE, etc.)","High-throughput inference where expert routing overhead is significant","Multi-GPU setups where expert parallelism can be leveraged"],"limitations":["MoE routing adds 10-20% compute overhead vs. dense models even with fusion optimization","Load balancing across experts is difficult; some experts may be underutilized","Fused kernels are model-specific; custom MoE architectures may not benefit from fusion","Expert parallelism requires careful GPU allocation to balance load across experts","Quantization of MoE models is more complex due to per-expert scaling"],"requires":["MoE model architecture (Mixtral, DeepSeek-MoE, or compatible)","CUDA 11.8+ with support for custom kernels","Multiple GPUs for expert parallelism (2+ recommended)","Python 3.9+"],"input_types":["token sequences","expert routing decisions","model configuration"],"output_types":["expert outputs","routing statistics","load balancing metrics"],"categories":["automation-workflow","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vllm-project--vllm__cap_6","uri":"capability://tool.use.integration.openai.compatible.rest.api.server.with.streaming.support","name":"openai-compatible rest api server with streaming support","description":"Provides an OpenAI-compatible HTTP API server that implements the OpenAI Chat Completions and Completions endpoints, enabling drop-in replacement for OpenAI's API. The server uses FastAPI for request handling, implements streaming responses via Server-Sent Events (SSE) for real-time token delivery, and includes request validation, error handling, and rate limiting. Supports both synchronous and asynchronous request processing through the async_llm interface.","intents":["Serve LLMs via a standard HTTP API compatible with OpenAI client libraries","Enable real-time streaming of generated tokens to clients","Provide a production-ready inference endpoint without custom integration code"],"best_for":["Teams wanting to replace OpenAI API with self-hosted inference","Applications already using OpenAI client libraries (Python, JavaScript, etc.)","Production deployments requiring standard REST API interface"],"limitations":["API compatibility is partial; some OpenAI-specific features (e.g., function calling with exact schema matching) may differ","Streaming adds ~50-100ms latency per token due to HTTP overhead vs. direct library calls","Rate limiting and authentication must be implemented externally (e.g., nginx, API gateway)","No built-in request logging or audit trail; requires external monitoring","Batch processing API not fully compatible with OpenAI's batch endpoint"],"requires":["Python 3.9+","FastAPI 0.100+","HTTP client library (requests, httpx, or OpenAI SDK)","Network connectivity for HTTP requests"],"input_types":["JSON request bodies with messages, model, temperature, etc.","HTTP headers for authentication"],"output_types":["JSON response objects","Server-Sent Events (SSE) for streaming","HTTP status codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vllm-project--vllm__cap_7","uri":"capability://tool.use.integration.tool.calling.and.structured.output.with.json.schema.validation","name":"tool calling and structured output with json schema validation","description":"Supports tool calling and structured output generation by constraining model outputs to match JSON schemas, using a constraint-based decoding approach that guides token generation to produce valid JSON. The system integrates with the sampling layer to enforce schema constraints at token generation time, preventing invalid JSON and ensuring outputs conform to specified tool signatures. Supports both OpenAI-style tool calling and arbitrary JSON schema constraints.","intents":["Enable reliable tool calling by constraining outputs to match tool signatures","Generate structured data (JSON) that conforms to application schemas","Reduce post-processing overhead by ensuring outputs are valid JSON without parsing errors"],"best_for":["Agentic applications requiring reliable tool calling","Data extraction pipelines needing structured output","Applications where output parsing errors are unacceptable"],"limitations":["Schema constraints add 5-15% latency overhead due to constraint checking per token","Complex schemas with many fields can significantly slow generation","Model must be capable of following schema constraints; weaker models may struggle","Constraint enforcement is approximate; edge cases may still produce invalid JSON","Schema changes require model reloading in some cases"],"requires":["JSON schema definition for output format","Model with reasonable instruction-following capability","Python 3.9+","jsonschema library for schema validation"],"input_types":["JSON schema definitions","tool signatures","prompt text"],"output_types":["valid JSON objects","tool calls with arguments","structured data"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vllm-project--vllm__cap_8","uri":"capability://tool.use.integration.lora.adapter.management.and.dynamic.loading","name":"lora adapter management and dynamic loading","description":"Supports Low-Rank Adaptation (LoRA) adapters that enable efficient fine-tuning and task-specific customization without modifying base model weights. The system manages multiple LoRA adapters in memory, allowing dynamic switching between adapters per-request through request metadata. Adapters are loaded on-demand and cached in GPU memory, with support for adapter composition (combining multiple adapters) and adapter-specific scaling.","intents":["Serve multiple task-specific model variants from a single base model","Enable per-request adapter selection for multi-tenant inference","Reduce memory overhead of fine-tuning by using low-rank adapters instead of full model copies"],"best_for":["Multi-tenant SaaS platforms with customer-specific model customization","Applications requiring task-specific model variants (e.g., different domains, languages)","Cost-sensitive deployments where adapter overhead is lower than full model copies"],"limitations":["LoRA adapters add 3-8% latency overhead per request due to adapter computation","Adapter loading/switching adds 10-50ms overhead if adapter is not cached","Limited to low-rank updates; cannot change model architecture or add new capabilities","Adapter composition (combining multiple adapters) is not well-supported; requires manual merging","Adapter compatibility is model-specific; adapters from one model cannot be used on another"],"requires":["LoRA adapter weights (trained via peft or similar library)","Base model compatible with LoRA (most transformer models supported)","GPU with sufficient VRAM for base model + adapter weights","Python 3.9+"],"input_types":["LoRA adapter paths","adapter selection metadata per request","adapter scaling parameters"],"output_types":["adapter-modified outputs","adapter loading status","performance metrics per adapter"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vllm-project--vllm__cap_9","uri":"capability://image.visual.multimodal.input.processing.with.vision.and.audio.support","name":"multimodal input processing with vision and audio support","description":"Extends inference to multimodal models by implementing Multimodal Data Processing that handles images, audio, and text inputs. The system includes vision encoders (e.g., CLIP) that convert images to embeddings, audio processors that extract audio features, and integration with the input processing pipeline to merge multimodal embeddings with text tokens. Supports both image-to-text and audio-to-text tasks through a unified multimodal interface.","intents":["Process images alongside text for vision-language models (e.g., LLaVA, GPT-4V)","Handle audio inputs for speech-to-text and audio understanding tasks","Enable multimodal reasoning by combining visual, audio, and textual context"],"best_for":["Vision-language applications (image captioning, visual QA, document analysis)","Multimodal AI systems combining text, image, and audio","Applications requiring fine-grained visual understanding"],"limitations":["Vision encoding adds 50-200ms latency per image depending on encoder size","Audio processing requires additional models (speech recognition, feature extraction)","Multimodal models are typically larger than text-only models, requiring more VRAM","Image resolution affects latency; high-resolution images can add 500ms+ overhead","Audio processing requires careful handling of variable-length audio sequences"],"requires":["Multimodal model (LLaVA, GPT-4V compatible, or similar)","Vision encoder (CLIP or model-specific encoder)","GPU with sufficient VRAM for multimodal model + encoders (24GB+ recommended)","Python 3.9+","Image/audio processing libraries (PIL, librosa, etc.)"],"input_types":["text prompts","image files (PNG, JPEG, WebP)","audio files (WAV, MP3, etc.)","image URLs"],"output_types":["text responses","multimodal embeddings","analysis results"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["CUDA 11.8+ or ROCm 5.7+ for GPU acceleration","GPU with sufficient VRAM for KV cache (typically 16GB+ for production models)","Python 3.9+","GPU with unified memory or explicit cache management (NVIDIA A100+, H100 recommended)","Sufficient GPU VRAM to hold KV cache for target batch size","CUDA 11.8+ or ROCm 5.7+","Model available on HuggingFace Hub or local filesystem","Model config.json with standard architecture specification","transformers library 4.30+","GPU with attention backend support (NVIDIA A100+ for FlashAttention, H100 for FlashInfer)"],"failure_modes":["Continuous batching adds ~5-15ms scheduling overhead per batch iteration","Memory fragmentation can occur with highly variable sequence lengths","Requires careful tuning of max_batch_size and max_num_seqs for optimal performance","Prefix caching requires exact token-level matching; minor prompt variations bypass cache","Block-level allocation adds ~2-5% memory overhead for metadata tracking","Cache invalidation on model weight updates requires full cache flush","Disaggregated serving introduces network latency for cache transfers (typically 10-50ms)","Architecture detection relies on model config.json; non-standard configs may fail","Custom architectures require manual registration; no automatic detection for unknown models","Model loading time includes architecture detection overhead (~100-500ms)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4530489279808593,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:57:19.180Z","last_commit":"2026-05-03T12:47:56Z"},"community":{"stars":78914,"forks":16371,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=vllm-project--vllm","compare_url":"https://unfragile.ai/compare?artifact=vllm-project--vllm"}},"signature":"t1wbQxJ+hCHUyAvn6P2zA6qC07ZPyEC9ucLf59VJrHCilpJqTonBxxeAXBcnxW7J2RLF+/8VJqDbAMQQv4B3CQ==","signedAt":"2026-06-20T02:29:26.599Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/vllm-project--vllm","artifact":"https://unfragile.ai/vllm-project--vllm","verify":"https://unfragile.ai/api/v1/verify?slug=vllm-project--vllm","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"}}