{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-vllm","slug":"pypi-vllm","name":"vllm","type":"framework","url":"https://pypi.org/project/vllm/","page_url":"https://unfragile.ai/pypi-vllm","categories":["deployment-infra"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-vllm__cap_0","uri":"capability://data.processing.analysis.pagedattention.based.kv.cache.management.with.memory.pooling","name":"pagedattention-based kv cache management with memory pooling","description":"Implements a paging-based key-value cache system that treats attention cache like virtual memory, allowing non-contiguous memory allocation and reuse across sequences. Uses a block manager that allocates fixed-size cache blocks (typically 16 tokens per block) and implements a least-recently-used eviction policy, reducing memory fragmentation by ~75% compared to contiguous allocation. Supports both GPU and CPU cache with automatic spillover.","intents":["Maximize batch size and sequence length within fixed GPU memory constraints","Reduce memory waste from padding and sequence length variance in batches","Enable serving longer sequences without OOM errors on consumer GPUs","Improve throughput by fitting more concurrent requests in memory"],"best_for":["Teams deploying LLMs on resource-constrained hardware (8GB-40GB GPUs)","Production serving systems requiring high throughput with variable sequence lengths","Researchers optimizing inference efficiency for long-context models"],"limitations":["Block-based allocation introduces ~2-5% latency overhead from block lookup and management","Requires CUDA compute capability 7.0+ for optimal performance; older GPUs fall back to slower implementations","Memory pooling effectiveness depends on batch composition; highly variable sequence lengths reduce reuse efficiency","CPU cache spillover significantly slower than GPU cache; only recommended for emergency overflow"],"requires":["Python 3.8+","CUDA 11.8+ or ROCm 5.7+ for GPU acceleration","PyTorch 2.0+","GPU with minimum 8GB VRAM for practical use"],"input_types":["token_ids (int32/int64 tensors)","attention_mask (boolean or float tensors)","sequence_length metadata"],"output_types":["logits (float32 tensors)","cache_blocks (internal block references)","memory_usage_stats (dict with peak/current allocation)"],"categories":["data-processing-analysis","memory-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-vllm__cap_1","uri":"capability://automation.workflow.continuous.batching.with.dynamic.request.scheduling","name":"continuous batching with dynamic request scheduling","description":"Implements an iteration-level scheduler that decouples request arrival from GPU iteration cycles, allowing new requests to join mid-batch and completed sequences to exit without blocking others. Uses a priority queue with configurable scheduling policies (FCFS, priority-based, SJF) and tracks per-request state (tokens generated, cache blocks allocated, position in sequence). Overlaps I/O and computation by prefetching next batch while current batch executes.","intents":["Reduce time-to-first-token latency for new requests arriving during batch processing","Maximize GPU utilization by eliminating idle time waiting for slow requests to complete","Support variable-length sequences without padding all sequences to max length","Enable fair scheduling policies (priority, deadline-aware) for multi-tenant deployments"],"best_for":["Real-time inference services with unpredictable request arrival patterns","Multi-tenant SaaS platforms requiring fairness and latency SLAs","High-throughput batch serving where latency variance is critical"],"limitations":["Scheduler overhead adds ~5-10ms per iteration for large batches (>100 requests); scales linearly with batch size","Requires careful tuning of batch size and iteration frequency to balance latency vs. throughput; no auto-tuning","Scheduling policies are greedy; no global optimization across multiple iterations","Cache block fragmentation increases with frequent request arrivals/departures; requires periodic defragmentation"],"requires":["Python 3.8+","CUDA 11.8+ or ROCm 5.7+","Async I/O support (asyncio or similar)","Request queue with timestamp metadata"],"input_types":["request_queue (list of Request objects with prompt_tokens, max_tokens, priority)","batch_size (int)","scheduling_policy (str: 'fcfs', 'priority', 'sjf')"],"output_types":["scheduled_batch (list of request IDs and token positions)","scheduling_metrics (dict with latency, throughput, fairness scores)"],"categories":["automation-workflow","scheduling-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-vllm__cap_10","uri":"capability://automation.workflow.model.serving.with.automatic.gpu.memory.management.and.eviction","name":"model serving with automatic gpu memory management and eviction","description":"Implements a model manager that tracks GPU memory allocation per model, automatically evicts least-recently-used models when memory is exhausted, and preloads frequently-accessed models. Uses a weighted LRU cache considering both access frequency and model size. Supports model swapping between GPU and CPU with automatic migration. Implements memory pressure monitoring and proactive eviction before OOM.","intents":["Serve multiple large models on a single GPU by swapping between them","Maximize GPU utilization by keeping hot models in VRAM and cold models on CPU","Prevent out-of-memory errors through proactive model eviction","Support dynamic model loading without manual memory management"],"best_for":["Multi-model serving systems with limited GPU memory","Applications with bursty model access patterns (some models used frequently, others rarely)","Research systems experimenting with many models","Cost-optimized deployments using smaller GPUs"],"limitations":["Model swapping introduces 500ms-2s latency for GPU↔CPU transfers; not suitable for latency-critical applications","LRU eviction is greedy; no global optimization across multiple models","CPU memory must be sufficient for evicted models; no disk-based spillover","Eviction overhead scales with model size; very large models (>100GB) cause noticeable latency spikes"],"requires":["Python 3.8+","CUDA 11.8+ for GPU memory management","Sufficient CPU memory for model swapping (typically 2-4x GPU memory)","Model metadata including size and access patterns"],"input_types":["model_id (str: model identifier)","models_to_load (list of str: model IDs)","gpu_memory_limit (int: bytes available for models)","eviction_policy (str: 'lru', 'lfu', 'weighted')"],"output_types":["loaded_model (PreTrainedModel or similar)","memory_stats (dict with gpu_used, gpu_available, cpu_used)","eviction_log (list of dicts with model_id, eviction_reason, swap_time)"],"categories":["automation-workflow","memory-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-vllm__cap_11","uri":"capability://automation.workflow.distributed.tracing.and.performance.profiling.with.detailed.metrics","name":"distributed tracing and performance profiling with detailed metrics","description":"Instruments inference pipeline with distributed tracing (OpenTelemetry compatible) capturing request flow across multiple components (scheduler, attention, quantization, communication). Collects per-layer latency, memory allocation, and throughput metrics. Exports metrics to Prometheus and traces to Jaeger/Zipkin. Implements automatic bottleneck detection and performance regression alerts.","intents":["Identify performance bottlenecks in inference pipeline (compute vs. communication vs. I/O)","Monitor production inference systems for latency regressions and anomalies","Optimize model serving by analyzing per-layer execution time and memory usage","Debug distributed inference issues across multiple GPUs/nodes"],"best_for":["Production inference systems requiring observability and debugging","Performance optimization teams analyzing inference bottlenecks","Multi-GPU/multi-node deployments requiring distributed tracing","SRE teams monitoring inference SLAs and detecting regressions"],"limitations":["Tracing overhead adds ~5-10% latency; should be disabled in ultra-low-latency scenarios","Metric collection requires external infrastructure (Prometheus, Jaeger); adds operational complexity","Detailed per-layer tracing generates high cardinality metrics; can overwhelm monitoring systems at scale","Bottleneck detection is heuristic-based; may miss subtle performance issues"],"requires":["Python 3.8+","OpenTelemetry Python SDK 1.0+","Prometheus server for metrics collection (optional)","Jaeger or Zipkin for distributed tracing (optional)"],"input_types":["enable_tracing (bool)","trace_level (str: 'request', 'layer', 'kernel')","metrics_export_interval (int: seconds)"],"output_types":["traces (OpenTelemetry format with span hierarchy)","metrics (Prometheus format with latency, memory, throughput)","bottleneck_report (dict with identified bottlenecks and recommendations)"],"categories":["automation-workflow","monitoring-observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-vllm__cap_2","uri":"capability://automation.workflow.multi.gpu.distributed.inference.with.tensor.parallelism.and.pipeline.parallelism","name":"multi-gpu distributed inference with tensor parallelism and pipeline parallelism","description":"Partitions model weights and computation across multiple GPUs using tensor parallelism (splitting weight matrices row/column-wise) and pipeline parallelism (splitting layers across devices). Implements AllReduce and AllGather collectives via NCCL for synchronization, with automatic communication scheduling to overlap computation and communication. Supports both intra-node (NVLink) and inter-node (Ethernet) topologies with topology-aware optimization.","intents":["Serve models larger than single GPU memory (e.g., 70B+ parameter models on 8x A100s)","Reduce latency for large models by parallelizing computation across multiple GPUs","Scale throughput linearly with number of GPUs for batch inference workloads","Support heterogeneous GPU clusters with automatic load balancing"],"best_for":["Teams deploying 13B+ parameter models requiring sub-second latency","Multi-GPU clusters (2-128 GPUs) with high-bandwidth interconnects","Production systems requiring fault tolerance and dynamic scaling"],"limitations":["Communication overhead dominates for small batches; tensor parallelism only efficient with batch_size >= 8-16","Requires homogeneous GPU types and NCCL-compatible interconnects; heterogeneous clusters need custom communication kernels","Pipeline parallelism introduces bubble overhead (idle GPUs waiting for previous stage); typically 10-20% efficiency loss","Synchronous communication blocks all GPUs; asynchronous variants add complexity and require careful deadlock prevention"],"requires":["Python 3.8+","CUDA 11.8+ with NCCL 2.14+","PyTorch 2.0+ with distributed training support","Multi-GPU setup with NVIDIA GPUs (A100, H100, L40S) or AMD MI250X","High-bandwidth interconnect (NVLink for intra-node, 100Gbps+ Ethernet for inter-node)"],"input_types":["model_config (dict with num_layers, hidden_size, num_heads)","tensor_parallel_size (int: 1-8 typical)","pipeline_parallel_size (int: 1-4 typical)","rank (int: GPU index in distributed group)"],"output_types":["logits (float32 tensors, gathered on rank 0)","distributed_state (dict with shard assignments and communication graph)","performance_metrics (dict with compute/communication time breakdown)"],"categories":["automation-workflow","distributed-computing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-vllm__cap_3","uri":"capability://code.generation.editing.speculative.decoding.with.draft.model.acceleration","name":"speculative decoding with draft model acceleration","description":"Implements speculative execution where a smaller draft model generates candidate tokens in parallel, and the main model validates them in a single forward pass using a modified attention mechanism. Accepts valid tokens and rejects invalid ones, then continues with main model's output. Uses a rejection sampling strategy to maintain output distribution equivalence. Supports both on-device draft models and external draft model servers.","intents":["Reduce latency for autoregressive generation by 1.5-3x using smaller draft models","Maintain output quality (same distribution) while accelerating inference","Amortize draft model cost across multiple validation steps","Support variable draft model sizes and architectures (e.g., distilled versions)"],"best_for":["Latency-sensitive applications (chat, real-time translation) where 2-3x speedup justifies draft model overhead","Deployments with sufficient GPU memory for both main and draft models","Scenarios where draft model can be much smaller (e.g., 7B draft for 70B main)"],"limitations":["Requires draft model with compatible tokenizer and vocabulary; mismatches cause rejection cascades","Speedup highly dependent on draft model quality; poor draft models cause >50% rejection rate, negating benefits","Adds ~10-15% memory overhead for draft model weights and intermediate activations","Validation overhead (attention computation) can exceed draft generation time for very small drafts; typically requires draft_size >= 1B parameters"],"requires":["Python 3.8+","Two compatible models (main and draft) with same tokenizer","CUDA 11.8+ for efficient attention kernels","GPU with sufficient memory for both models (typically 2x main model memory)"],"input_types":["prompt_tokens (int32 tensor)","draft_model (PreTrainedModel or model_id string)","num_speculative_tokens (int: 4-16 typical)","temperature (float: 0.0-1.0 for sampling)"],"output_types":["generated_tokens (int32 tensor)","acceptance_rate (float: 0.0-1.0)","latency_breakdown (dict with draft_time, validation_time, rejection_time)"],"categories":["code-generation-editing","latency-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-vllm__cap_4","uri":"capability://data.processing.analysis.quantization.aware.inference.with.mixed.precision.execution","name":"quantization-aware inference with mixed-precision execution","description":"Supports multiple quantization schemes (INT8, INT4, GPTQ, AWQ, GGUF) with automatic precision selection per layer based on sensitivity analysis. Implements custom CUDA kernels for quantized matrix multiplication (e.g., INT8 GEMM via cuBLAS) and dequantization-on-the-fly to maintain accuracy. Tracks per-layer quantization statistics and allows dynamic precision adjustment based on runtime performance.","intents":["Reduce model memory footprint by 4-8x using INT4/INT8 quantization without significant accuracy loss","Accelerate inference by 2-4x using quantized compute kernels vs. FP32","Deploy large models on consumer GPUs (e.g., 70B model on 24GB GPU)","Support mixed-precision execution where sensitive layers remain FP32 and others are INT4"],"best_for":["Edge deployments and consumer GPU inference where memory is constrained","Cost-sensitive cloud deployments where model size directly impacts hardware costs","Applications tolerating 1-3% accuracy loss for 4-8x memory savings"],"limitations":["Quantization requires calibration on representative data; poor calibration causes >5% accuracy degradation","INT4 kernels slower than INT8 on some hardware; speedup varies by GPU architecture (A100 vs. RTX 4090)","Mixed-precision requires manual layer-wise configuration; no automatic sensitivity analysis in current version","Quantized models incompatible with some fine-tuning and LoRA techniques; requires quantization-aware training for best results"],"requires":["Python 3.8+","CUDA 11.8+ with cuBLAS support for quantized kernels","Pre-quantized model weights or quantization library (AutoGPTQ, AWQ)","Calibration dataset for post-training quantization (optional but recommended)"],"input_types":["model_weights (FP32 or pre-quantized INT8/INT4)","quantization_config (dict with scheme, bits, group_size)","calibration_data (optional, for dynamic quantization)"],"output_types":["quantized_logits (float32 tensor, dequantized for output)","quantization_stats (dict with per-layer scales, zero_points, min/max values)","memory_usage (dict with original vs. quantized size)"],"categories":["data-processing-analysis","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-vllm__cap_5","uri":"capability://memory.knowledge.prefix.caching.and.prompt.reuse.optimization","name":"prefix caching and prompt reuse optimization","description":"Caches KV cache blocks for common prompt prefixes (e.g., system prompts, few-shot examples) and reuses them across requests with matching prefixes. Uses a trie-based prefix tree to identify shareable prefixes and implements copy-on-write semantics for cache blocks to avoid duplication. Automatically detects prefix overlaps and merges cache blocks when beneficial.","intents":["Reduce redundant computation for requests sharing common prefixes (e.g., system prompts, RAG context)","Accelerate time-to-first-token by skipping prefix computation entirely","Reduce memory usage by sharing cache blocks across requests with identical prefixes","Support dynamic prefix updates without invalidating entire cache"],"best_for":["Multi-turn conversation systems where system prompt is shared across turns","RAG-augmented systems where retrieved context is reused across similar queries","Few-shot prompting scenarios with fixed examples","High-volume services with repeated user patterns"],"limitations":["Prefix detection overhead adds ~1-2ms per request; only beneficial for batches with >20% prefix overlap","Trie-based lookup scales linearly with prefix length; very long prefixes (>4K tokens) add noticeable overhead","Cache block sharing requires careful synchronization; concurrent modifications to shared blocks can cause data races","Prefix mismatch (e.g., different tokenization) causes cache misses; requires exact token-level matching"],"requires":["Python 3.8+","CUDA 11.8+ for efficient cache block operations","Request metadata including prompt tokens and prefix information","Sufficient GPU memory for prefix cache storage (typically 10-20% of total KV cache)"],"input_types":["prompt_tokens (int32 tensor)","prefix_hash (str or int: hash of prefix for matching)","enable_prefix_caching (bool)"],"output_types":["cache_hit (bool: whether prefix was cached)","tokens_skipped (int: number of tokens reused from cache)","cache_stats (dict with hit_rate, memory_saved)"],"categories":["memory-knowledge","caching-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-vllm__cap_6","uri":"capability://tool.use.integration.openai.compatible.rest.api.with.streaming.and.async.support","name":"openai-compatible rest api with streaming and async support","description":"Exposes a drop-in replacement for OpenAI's Chat Completions and Completions APIs via FastAPI, supporting streaming responses via Server-Sent Events (SSE), async request handling with asyncio, and request queuing with configurable timeout policies. Implements request validation, error handling, and response formatting to match OpenAI's schema exactly. Supports both synchronous and asynchronous client libraries.","intents":["Migrate applications from OpenAI API to local inference without code changes","Support streaming responses for real-time token generation in web UIs","Handle concurrent requests with async/await patterns and connection pooling","Integrate with existing OpenAI client libraries (Python, JavaScript, etc.)"],"best_for":["Teams migrating from OpenAI to self-hosted inference","Web applications requiring streaming responses for real-time UX","Multi-tenant systems with concurrent request handling","Development teams already familiar with OpenAI API"],"limitations":["API compatibility is best-effort; some OpenAI-specific features (function calling, vision) may have limited support","Streaming adds ~50-100ms latency per token due to SSE overhead; not suitable for ultra-low-latency applications","Request queuing can cause unbounded latency growth under sustained high load; requires external load balancing","No built-in authentication or rate limiting; requires reverse proxy (nginx, Envoy) for production security"],"requires":["Python 3.8+","FastAPI 0.95+","Uvicorn or similar ASGI server","OpenAI Python client library 0.27+ (optional, for client-side testing)"],"input_types":["messages (list of dicts with role, content)","model (str: model name)","temperature (float: 0.0-2.0)","max_tokens (int)","stream (bool: enable streaming)"],"output_types":["choices (list of completion objects with finish_reason, message)","usage (dict with prompt_tokens, completion_tokens, total_tokens)","stream (SSE events with delta tokens if streaming=true)"],"categories":["tool-use-integration","api-compatibility"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-vllm__cap_7","uri":"capability://code.generation.editing.lora.adapter.loading.and.dynamic.model.switching","name":"lora adapter loading and dynamic model switching","description":"Supports loading and applying Low-Rank Adaptation (LoRA) adapters on top of base models without modifying weights, using efficient rank-decomposed matrix multiplication. Implements dynamic adapter switching at inference time (swap adapters between requests) with automatic weight merging/unmerging. Supports multiple LoRA formats (HuggingFace, Alpaca, custom) and adapter composition (combining multiple adapters).","intents":["Fine-tune models for specific tasks without storing full model copies","Switch between task-specific adapters at inference time without reloading base model","Reduce storage and memory overhead by sharing base model across multiple adapters","Support multi-task inference with per-request adapter selection"],"best_for":["Multi-tenant systems serving different customers with task-specific models","Applications requiring frequent model updates without full retraining","Resource-constrained deployments where storing multiple full models is infeasible","Research teams experimenting with multiple fine-tuned variants"],"limitations":["LoRA adapter loading adds ~50-100ms per adapter switch; not suitable for per-token adapter changes","Adapter composition (merging multiple adapters) requires careful rank selection; poor composition causes accuracy degradation","LoRA effectiveness depends on rank; very low ranks (<8) may lose task-specific information, high ranks (>64) reduce memory savings","Incompatible with some quantization schemes; INT4 quantization requires quantization-aware LoRA training"],"requires":["Python 3.8+","PyTorch 1.13+","peft library 0.4+ for LoRA support","Base model compatible with LoRA (most HuggingFace models supported)"],"input_types":["base_model (PreTrainedModel or model_id)","lora_adapters (list of dicts with adapter_name, adapter_path, r, lora_alpha)","active_adapter (str: adapter name to use for inference)"],"output_types":["logits (float32 tensor with LoRA applied)","adapter_stats (dict with adapter_size, merge_time, inference_overhead)"],"categories":["code-generation-editing","model-adaptation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-vllm__cap_8","uri":"capability://text.generation.language.structured.output.generation.with.json.schema.validation","name":"structured output generation with json schema validation","description":"Constrains token generation to match a provided JSON schema, using a constrained decoding algorithm that filters invalid tokens at each step based on schema constraints. Implements a finite-state automaton (FSA) derived from the schema to track valid next tokens. Supports nested objects, arrays, enums, and type validation (string, number, boolean). Validates output against schema post-generation.","intents":["Generate structured JSON output guaranteed to match a schema without post-processing","Reduce hallucination by constraining generation to valid schema paths","Extract structured data from unstructured text with guaranteed format compliance","Enable reliable tool calling and function invocation with validated arguments"],"best_for":["Applications requiring guaranteed JSON output format (APIs, data extraction)","Tool-calling systems where function arguments must match signatures","Data extraction pipelines where schema compliance is critical","Structured prediction tasks (entity extraction, relation extraction)"],"limitations":["Constrained decoding adds ~10-30% latency overhead due to FSA state tracking and token filtering","Complex schemas with many branches cause exponential FSA growth; very large schemas (>1000 fields) become impractical","Schema constraints may force suboptimal token choices; can reduce output quality if schema is overly restrictive","Requires schema definition upfront; dynamic schema generation not supported"],"requires":["Python 3.8+","JSON schema definition (JSON Schema Draft 7 or later)","CUDA 11.8+ for efficient token filtering kernels","Model with sufficient vocabulary coverage for schema tokens"],"input_types":["prompt (str)","json_schema (dict: JSON Schema specification)","temperature (float: 0.0-1.0, typically 0.0 for deterministic output)"],"output_types":["generated_json (dict: validated JSON object matching schema)","schema_compliance (bool: whether output matches schema)","validation_errors (list of str: schema violations if any)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-vllm__cap_9","uri":"capability://data.processing.analysis.embedding.model.inference.with.batch.processing.and.similarity.search","name":"embedding model inference with batch processing and similarity search","description":"Optimizes embedding generation for large batches using efficient pooling strategies (mean, max, CLS token) and optional normalization. Implements approximate nearest neighbor (ANN) search via FAISS integration for fast similarity queries over large embedding collections. Supports both dense embeddings and sparse embeddings (for BM25-style retrieval). Batches embedding computation to maximize GPU utilization.","intents":["Generate embeddings for large document collections efficiently","Perform semantic similarity search over millions of embeddings","Build retrieval-augmented generation (RAG) systems with fast document lookup","Implement semantic clustering and deduplication of documents"],"best_for":["RAG systems requiring fast semantic search over large document collections","Semantic similarity applications (duplicate detection, recommendation)","Information retrieval systems combining dense and sparse retrieval","Large-scale document processing pipelines"],"limitations":["FAISS indexing requires upfront computation; index building time scales linearly with collection size (1M docs ≈ 5-10 minutes)","ANN search trades recall for speed; approximate search may miss relevant documents; exact search requires linear scan","Embedding quality depends on model choice; generic embeddings may not capture domain-specific semantics","Index updates require full rebuild; incremental updates not supported in standard FAISS"],"requires":["Python 3.8+","CUDA 11.8+ for GPU embedding computation","FAISS library 1.7+ for similarity search","Embedding model (e.g., sentence-transformers, OpenAI embeddings)"],"input_types":["texts (list of str: documents to embed)","batch_size (int: embedding batch size)","pooling_strategy (str: 'mean', 'max', 'cls')","normalize (bool: L2 normalize embeddings)"],"output_types":["embeddings (float32 tensor, shape [num_texts, embedding_dim])","search_results (list of dicts with doc_id, similarity_score, rank)","index_stats (dict with index_size, search_time, recall)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","CUDA 11.8+ or ROCm 5.7+ for GPU acceleration","PyTorch 2.0+","GPU with minimum 8GB VRAM for practical use","CUDA 11.8+ or ROCm 5.7+","Async I/O support (asyncio or similar)","Request queue with timestamp metadata","CUDA 11.8+ for GPU memory management","Sufficient CPU memory for model swapping (typically 2-4x GPU memory)","Model metadata including size and access patterns"],"failure_modes":["Block-based allocation introduces ~2-5% latency overhead from block lookup and management","Requires CUDA compute capability 7.0+ for optimal performance; older GPUs fall back to slower implementations","Memory pooling effectiveness depends on batch composition; highly variable sequence lengths reduce reuse efficiency","CPU cache spillover significantly slower than GPU cache; only recommended for emergency overflow","Scheduler overhead adds ~5-10ms per iteration for large batches (>100 requests); scales linearly with batch size","Requires careful tuning of batch size and iteration frequency to balance latency vs. throughput; no auto-tuning","Scheduling policies are greedy; no global optimization across multiple iterations","Cache block fragmentation increases with frequent request arrivals/departures; requires periodic defragmentation","Model swapping introduces 500ms-2s latency for GPU↔CPU transfers; not suitable for latency-critical applications","LRU eviction is greedy; no global optimization across multiple models","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.061Z","last_scraped_at":"2026-05-03T15:20:18.280Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-vllm","compare_url":"https://unfragile.ai/compare?artifact=pypi-vllm"}},"signature":"1FBZ+vout6hBzc3TWVehQUdzVf15vM3y7zLgIENF0uu3EZobgyKo29tQmf8h2MRZIeYeU4ggA/fRo4zy1zQiCw==","signedAt":"2026-06-20T05:32:34.974Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-vllm","artifact":"https://unfragile.ai/pypi-vllm","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-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"}}