{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tensorrt-llm","slug":"tensorrt-llm","name":"TensorRT-LLM","type":"framework","url":"https://github.com/NVIDIA/TensorRT-LLM","page_url":"https://unfragile.ai/tensorrt-llm","categories":["deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tensorrt-llm__cap_0","uri":"capability://code.generation.editing.multi.precision.quantization.with.fp8.int4.awq.and.gptq.support","name":"multi-precision quantization with fp8, int4, awq, and gptq support","description":"Implements a pluggable quantization system that converts model weights to lower precision formats (FP8, INT4, AWQ, GPTQ) with per-layer scale management and weight loading pipelines. The quantization configuration system allows fine-grained control over which layers use which quantization methods, with automatic scale computation during model compilation. Supports mixed-precision strategies where different layers can use different quantization schemes optimized for their numerical characteristics.","intents":["Reduce model memory footprint from 40GB to 10GB while maintaining inference quality","Deploy large models on consumer GPUs with limited VRAM","Achieve 2-4x throughput improvement through reduced memory bandwidth requirements","Benchmark quantization accuracy tradeoffs for production deployment decisions"],"best_for":["ML engineers optimizing inference cost on NVIDIA GPUs","Teams deploying 7B-70B parameter models on single or dual GPU systems","Production systems requiring sub-100ms latency with memory constraints"],"limitations":["Quantization requires offline calibration on representative data — cannot be applied post-hoc to arbitrary checkpoints","FP8 quantization may lose 1-3% accuracy on reasoning tasks; INT4 can lose 5-10% without careful calibration","AWQ and GPTQ require access to training data or representative samples for scale computation","No dynamic quantization — all quantization decisions are baked into compiled engine at build time"],"requires":["NVIDIA GPU with Ampere or newer architecture (A100, H100, RTX 4090, etc.)","CUDA 12.0+","TensorRT 9.0+","Python 3.10+"],"input_types":["PyTorch model checkpoints (.pt, .pth)","Hugging Face model identifiers","Calibration datasets (numpy arrays or PyTorch DataLoaders)"],"output_types":["Compiled TensorRT engine (.engine)","Quantization configuration metadata (JSON)","Quantized weight tensors (binary format)"],"categories":["code-generation-editing","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tensorrt-llm__cap_1","uri":"capability://memory.knowledge.paged.kv.cache.management.with.disaggregated.serving.support","name":"paged kv cache management with disaggregated serving support","description":"Implements a memory-efficient KV cache system that pages attention key-value tensors into fixed-size blocks, enabling dynamic allocation and reuse across requests without fragmentation. The cache is managed by the PyExecutor runtime which tracks block allocation, deallocation, and reuse across the request queue. Supports disaggregated serving architectures where KV cache can be transferred between encoder and decoder workers via IPC, enabling horizontal scaling of inference workloads.","intents":["Serve 100+ concurrent requests on a single GPU without OOM errors","Reduce KV cache memory overhead from 30% to 5% of total GPU memory through block reuse","Implement disaggregated prefill-decode separation for better hardware utilization","Enable long-context inference (8K-100K tokens) on consumer GPUs"],"best_for":["High-throughput inference services handling variable-length requests","Multi-tenant deployments requiring isolation and fair resource allocation","Long-context applications (RAG, document analysis) with memory constraints","Teams building disaggregated inference clusters with separate prefill and decode workers"],"limitations":["Paging overhead adds ~5-10ms per request due to block allocation and tracking","Disaggregated serving requires low-latency network (sub-1ms) between prefill and decode workers; not suitable for WAN deployments","KV cache transfer via IPC is GPU-to-GPU only; CPU-GPU transfers incur significant latency","Block size is fixed at compile time; cannot dynamically adjust to request patterns"],"requires":["NVIDIA GPU with compute capability 8.0+ (A100, H100, RTX 3090, etc.)","CUDA 12.0+","TensorRT 9.0+","For disaggregated serving: NVLink or high-speed interconnect between GPUs"],"input_types":["Sequence lengths (integers)","Batch sizes (integers)","KV cache block size configuration (integers)"],"output_types":["Allocated block indices (integer arrays)","KV cache tensors (float16/bfloat16 tensors)","Block reuse statistics (JSON metrics)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tensorrt-llm__cap_10","uri":"capability://automation.workflow.automatic.model.compilation.and.engine.generation","name":"automatic model compilation and engine generation","description":"Implements an AutoDeploy system that automatically converts Hugging Face models to optimized TensorRT engines through a transformation pipeline. The pipeline applies sharding transformations, pattern-matching fusion, quantization, and kernel optimization in sequence. Supports model discovery from Hugging Face Hub and automatic configuration of optimal settings based on model architecture and target hardware.","intents":["Convert any Hugging Face model to optimized TensorRT engine in one command","Automatically select optimal quantization, parallelism, and kernel settings","Reduce deployment time from hours to minutes","Enable non-experts to deploy optimized inference without manual tuning"],"best_for":["Teams deploying diverse models without deep optimization expertise","Rapid prototyping and experimentation workflows","Production systems requiring automated model updates","Organizations with limited ML infrastructure expertise"],"limitations":["Automatic configuration may not be optimal for all workloads; manual tuning often yields 10-20% better performance","Compilation time ranges from 5 minutes (7B models) to 30+ minutes (70B models) depending on model size","Requires sufficient GPU memory for compilation; may fail on memory-constrained systems","Automatic settings don't account for workload-specific characteristics (batch size distribution, sequence length patterns)"],"requires":["NVIDIA GPU with Ampere or newer architecture","CUDA 12.0+","TensorRT 9.0+","Python 3.10+","Sufficient GPU memory (24GB+ for 70B models)"],"input_types":["Hugging Face model identifier (string)","Target hardware specification (GPU type, count)","Optional: custom configuration (quantization, parallelism, batch size)"],"output_types":["Compiled TensorRT engine (.engine file)","Engine configuration (JSON)","Performance benchmarks (latency, throughput, memory usage)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tensorrt-llm__cap_11","uri":"capability://image.visual.multimodal.input.processing.with.vision.encoders","name":"multimodal input processing with vision encoders","description":"Implements multimodal inference where images are encoded using vision encoders (CLIP, SigLIP) and their embeddings are injected into the token sequence for processing by the LLM. Supports multiple image formats (JPEG, PNG, WebP) and automatic image resizing/normalization. Vision encoder outputs are cached to avoid redundant computation when the same image is processed multiple times.","intents":["Process image+text prompts in a single inference pass","Build vision-language applications (image captioning, visual QA, document analysis)","Cache vision encoder outputs to reduce latency for repeated images","Support variable-resolution images without recompilation"],"best_for":["Vision-language applications (image captioning, visual QA)","Document analysis and OCR systems","Multimodal chatbots","Teams building applications that combine text and image understanding"],"limitations":["Vision encoder inference adds 50-200ms latency depending on image resolution and encoder size","Vision encoder output caching requires additional GPU memory (10-20% overhead)","Image resolution is limited by vision encoder training; very high-res images must be downsampled","No support for video input; only static images"],"requires":["NVIDIA GPU with Ampere or newer architecture","CUDA 12.0+","TensorRT 9.0+","Vision encoder model (CLIP, SigLIP, or compatible)","Image processing libraries (PIL, OpenCV)"],"input_types":["Images (JPEG, PNG, WebP, numpy arrays)","Text prompts (strings)","Image metadata (resolution, format)"],"output_types":["Vision embeddings (float32 tensors)","Generated text (strings)","Multimodal outputs (text + image descriptions)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tensorrt-llm__cap_12","uri":"capability://data.processing.analysis.performance.benchmarking.and.regression.detection","name":"performance benchmarking and regression detection","description":"Implements a comprehensive benchmarking framework that measures inference latency, throughput, memory usage, and accuracy across different configurations. Includes regression detection that compares performance against baseline metrics and flags significant degradations. Supports both synthetic benchmarks (fixed batch sizes, sequence lengths) and realistic workload simulation (variable request patterns, arrival rates).","intents":["Measure inference performance (latency, throughput) across different hardware and configurations","Detect performance regressions before deployment","Compare optimization techniques (quantization, fusion, parallelism) quantitatively","Generate performance reports for capacity planning and cost analysis"],"best_for":["Teams optimizing inference performance","CI/CD pipelines requiring automated performance testing","Production systems monitoring performance over time","Research comparing optimization techniques"],"limitations":["Benchmarking adds significant overhead (5-30 minutes per configuration)","Synthetic benchmarks may not reflect real workload characteristics","Regression detection requires baseline metrics; cannot detect regressions on new configurations","Performance is highly dependent on hardware state (temperature, clock speeds); results may vary"],"requires":["NVIDIA GPU with Ampere or newer architecture","CUDA 12.0+","TensorRT 9.0+","Python 3.10+","Benchmark dataset (prompts, images, etc.)"],"input_types":["Model and engine configuration","Benchmark parameters (batch sizes, sequence lengths, num_requests)","Baseline metrics (for regression detection)"],"output_types":["Performance metrics (latency, throughput, memory, accuracy)","Performance reports (JSON, CSV, HTML)","Regression alerts (if performance degrades)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tensorrt-llm__cap_13","uri":"capability://planning.reasoning.sampling.parameter.control.with.temperature.top.k.top.p.and.beam.search","name":"sampling parameter control with temperature, top-k, top-p, and beam search","description":"Implements a flexible sampling system through the SamplingParams configuration that controls token generation behavior. Supports multiple sampling strategies: temperature-based softmax scaling, top-k filtering, nucleus (top-p) sampling, and beam search. Parameters can be set per-request, enabling fine-grained control over generation diversity and quality. Integrates with the Sampler component in PyExecutor to apply sampling decisions at token generation time.","intents":["Control generation diversity through temperature and top-k/top-p parameters","Implement deterministic generation (temperature=0) for reproducibility","Use beam search for higher-quality outputs at the cost of latency","Tune sampling parameters per-request based on application requirements"],"best_for":["Interactive applications requiring tunable generation behavior","Research exploring sampling strategies","Production systems with diverse generation requirements","Applications where generation quality is critical (summarization, translation)"],"limitations":["Beam search adds significant latency (2-5x slower than greedy decoding)","Top-k and top-p filtering add minimal overhead but may reduce generation quality if too aggressive","Sampling parameters are global; cannot vary within a single sequence","No support for constrained decoding (e.g., forcing specific tokens or patterns)"],"requires":["NVIDIA GPU with Ampere or newer architecture","CUDA 12.0+","TensorRT 9.0+","Python 3.10+"],"input_types":["SamplingParams object with temperature, top_k, top_p, beam_width, etc.","Logits from model forward pass"],"output_types":["Sampled token indices","Token probabilities (optional)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tensorrt-llm__cap_14","uri":"capability://tool.use.integration.triton.inference.server.backend.integration.with.model.configuration","name":"triton inference server backend integration with model configuration","description":"Provides a Triton Inference Server backend that wraps TensorRT-LLM models, enabling deployment via Triton's standardized model serving interface. Includes automatic model configuration generation from TensorRT engine metadata and support for Triton's ensemble models for complex inference pipelines. The backend handles request batching, response formatting, and metrics collection compatible with Triton's monitoring infrastructure.","intents":["Deploy TensorRT-LLM models via Triton Inference Server","Integrate with existing Triton deployments and monitoring","Support ensemble models combining multiple TensorRT-LLM models","Enable A/B testing and model versioning via Triton"],"best_for":["Teams already using Triton Inference Server","Production deployments requiring standardized model serving","Multi-model serving scenarios with ensemble pipelines","Organizations with existing Triton monitoring and infrastructure"],"limitations":["Triton backend adds abstraction layer; ~5-10% latency overhead vs. direct TensorRT-LLM API","Model configuration generation is automatic but may require manual tuning for optimal performance","Ensemble models add complexity; debugging multi-model pipelines is challenging","Triton's batching may not be optimal for all workloads; requires careful configuration"],"requires":["Triton Inference Server 2.40+","NVIDIA GPU with compute capability 8.0+","CUDA 12.0+","TensorRT 10.0+","TensorRT-LLM Triton backend (included in TensorRT-LLM)"],"input_types":["TensorRT engine","Triton model configuration (auto-generated or manual)","Ensemble model definition (for multi-model pipelines)"],"output_types":["Triton-compatible model artifacts","Model configuration files (config.pbtxt)","Metrics compatible with Triton monitoring"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tensorrt-llm__cap_2","uri":"capability://automation.workflow.in.flight.batching.with.dynamic.request.scheduling","name":"in-flight batching with dynamic request scheduling","description":"Implements a request scheduler in the PyExecutor runtime that dynamically batches requests at the token level, allowing new requests to join ongoing batches mid-inference without waiting for current batches to complete. The scheduler uses an event loop that processes requests in priority order, allocates KV cache blocks, and schedules forward passes through the ModelEngine. Supports heterogeneous batch composition where requests with different sequence lengths, batch sizes, and sampling parameters execute in the same batch.","intents":["Reduce time-to-first-token (TTFT) from 500ms to 50ms by scheduling new requests immediately","Achieve 3-5x higher throughput by overlapping prefill and decode phases across requests","Implement fair scheduling policies (round-robin, priority queues) for multi-tenant inference","Minimize GPU idle time by continuously feeding new requests into the batch pipeline"],"best_for":["High-concurrency inference services (100+ QPS)","Interactive applications requiring low latency (chatbots, code completion)","Multi-tenant systems with SLA requirements","Workloads with variable request arrival patterns"],"limitations":["Scheduling overhead adds ~2-5ms per batch due to request queue management and KV cache allocation","Heterogeneous batching requires padding sequences to max length in batch, wasting compute on padding tokens","No support for request preemption — long-running requests block shorter requests from completing","Scheduling decisions are greedy; no global optimization across multiple batches"],"requires":["NVIDIA GPU with Ampere or newer architecture","CUDA 12.0+","TensorRT 9.0+","Python 3.10+ with asyncio support"],"input_types":["Request queue (list of LLM requests with prompts, sampling params)","Batch configuration (max batch size, max tokens per batch)","Scheduling policy (round-robin, priority, FCFS)"],"output_types":["Scheduled batches (grouped requests)","Scheduling metrics (latency, throughput, queue depth)","Token generation results (text, logits)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tensorrt-llm__cap_3","uri":"capability://code.generation.editing.kernel.fusion.and.custom.cuda.kernel.integration","name":"kernel fusion and custom cuda kernel integration","description":"Implements a pattern-matching and fusion transformation pipeline that identifies subgraphs of operations (e.g., linear layer + activation + layer norm) and replaces them with fused custom CUDA kernels. The AutoTuner system profiles different kernel implementations and selects the fastest variant for each operation based on input shapes and hardware. Supports vendor-specific kernels (Triton, CUTLASS) and allows registration of custom ops through a tunable runner interface.","intents":["Reduce inference latency by 20-40% through operation fusion and kernel optimization","Eliminate memory bandwidth bottlenecks by fusing element-wise operations","Automatically select optimal kernel implementations for different input shapes and batch sizes","Integrate custom CUDA kernels (e.g., FlashAttention, MoE kernels) into the inference pipeline"],"best_for":["Teams optimizing inference latency on specific GPU architectures (H100, A100, L40S)","Developers implementing novel attention mechanisms or custom layers","Production systems where 10-20ms latency improvements translate to significant cost savings","Research teams exploring new kernel implementations"],"limitations":["AutoTuner profiling adds 5-30 minutes to model compilation time depending on model size and number of ops","Fused kernels are architecture-specific; engines compiled for H100 won't run optimally on A100","Custom kernel registration requires CUDA expertise; no Python-only path for kernel development","Fusion patterns are hardcoded; cannot dynamically fuse arbitrary operation sequences"],"requires":["NVIDIA GPU with Ampere or newer architecture","CUDA 12.0+ with development tools (nvcc, cuBLAS, cuDNN)","TensorRT 9.0+","C++ compiler (GCC 9.0+ or Clang 12.0+)"],"input_types":["Computation graph (TensorRT network definition)","Operation metadata (input shapes, dtypes, batch sizes)","Kernel implementations (CUDA source or compiled binaries)"],"output_types":["Fused computation graph","Kernel selection metadata (JSON)","Performance profiles (latency per op, memory bandwidth)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tensorrt-llm__cap_4","uri":"capability://automation.workflow.tensor.parallelism.with.multi.gpu.synchronization","name":"tensor parallelism with multi-gpu synchronization","description":"Implements distributed tensor parallelism where model weights are sharded across multiple GPUs and each forward pass requires all-reduce communication to synchronize partial results. The sharding transformation pipeline automatically partitions linear layers, attention operations, and MoE layers across GPUs based on a sharding strategy. Uses NCCL for efficient GPU-to-GPU communication and supports multiple communication backends (NCCL, GLOO, MPI).","intents":["Fit 70B+ parameter models on multi-GPU systems by sharding weights across GPUs","Reduce per-GPU memory footprint from 40GB to 10GB per GPU on 4-GPU setup","Maintain near-linear scaling of throughput with number of GPUs (80-90% efficiency)","Implement custom sharding strategies for novel model architectures"],"best_for":["Teams deploying 70B+ parameter models on multi-GPU clusters","Production systems requiring high throughput (1000+ tokens/sec)","Research teams exploring distributed inference architectures","Data centers with high-speed GPU interconnects (NVLink, InfiniBand)"],"limitations":["All-reduce communication adds 10-50ms per forward pass depending on GPU count and interconnect bandwidth","Scaling efficiency drops below 70% with >8 GPUs due to communication overhead","Requires low-latency GPU interconnect; WAN deployments incur unacceptable latency","Sharding strategy must be defined at compile time; cannot dynamically adjust to different GPU counts"],"requires":["Multiple NVIDIA GPUs (2-8 recommended, up to 128 supported)","CUDA 12.0+","NCCL 2.18+","MPI runtime (OpenMPI or MPICH) for multi-node setups","High-speed GPU interconnect (NVLink recommended for >4 GPUs)"],"input_types":["Model architecture definition","Sharding strategy (tensor parallelism degree, layer-wise sharding)","GPU topology (number of GPUs, interconnect type)"],"output_types":["Sharded model weights (distributed across GPUs)","Communication schedules (all-reduce operations)","Performance metrics (scaling efficiency, communication overhead)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tensorrt-llm__cap_5","uri":"capability://automation.workflow.pipeline.parallelism.with.inter.stage.communication","name":"pipeline parallelism with inter-stage communication","description":"Implements pipeline parallelism where model layers are partitioned across multiple GPUs and each GPU processes a different stage of the pipeline. Uses a bubble-minimization scheduling algorithm (similar to GPipe) to overlap computation and communication across stages. Supports both synchronous and asynchronous pipeline execution with configurable pipeline depth and micro-batch sizes.","intents":["Distribute 70B+ models across 8-16 GPUs with better scaling than tensor parallelism","Reduce per-GPU memory footprint by storing only a subset of layers per GPU","Achieve 70-80% GPU utilization through pipeline bubble minimization","Implement hybrid parallelism combining tensor and pipeline parallelism"],"best_for":["Teams deploying very large models (100B+ parameters) on multi-GPU clusters","Systems with moderate GPU interconnect bandwidth where tensor parallelism is inefficient","Workloads where latency is less critical than throughput","Research exploring distributed inference architectures"],"limitations":["Pipeline bubbles (idle GPU time) reduce efficiency to 70-80% even with optimization","Asynchronous execution complicates debugging and error handling","Requires careful tuning of micro-batch size and pipeline depth for optimal performance","Inter-stage communication latency can dominate on slow interconnects"],"requires":["Multiple NVIDIA GPUs (8+ recommended for meaningful speedup)","CUDA 12.0+","NCCL 2.18+","MPI runtime for multi-node setups","High-speed GPU interconnect (NVLink or InfiniBand)"],"input_types":["Model architecture definition","Pipeline stage assignment (which layers on which GPU)","Micro-batch size and pipeline depth configuration"],"output_types":["Pipeline schedule (forward/backward pass ordering)","Inter-stage communication operations","Performance metrics (pipeline efficiency, bubble percentage)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tensorrt-llm__cap_6","uri":"capability://planning.reasoning.speculative.decoding.with.eagle3.and.mtp.strategies","name":"speculative decoding with eagle3 and mtp strategies","description":"Implements speculative decoding where a smaller draft model generates candidate tokens and a larger verifier model validates them in parallel, reducing the number of forward passes required. Supports multiple speculation strategies: EAGLE3 (learned draft model), MTP (multi-token prediction), and custom strategies. The verification process uses batch processing to validate multiple candidate sequences in a single forward pass, amortizing compute cost.","intents":["Reduce inference latency by 30-50% through speculative decoding without accuracy loss","Achieve 2-3x speedup on latency-sensitive workloads (chatbots, code completion)","Implement custom draft models for domain-specific acceleration","Trade compute for latency in bandwidth-limited scenarios"],"best_for":["Interactive applications requiring sub-100ms latency","Chatbots and code completion systems","Teams with compute budget to spare but latency constraints","Workloads where draft model accuracy is high (>80% acceptance rate)"],"limitations":["Requires training or fine-tuning a draft model; no pre-trained EAGLE3 models for all architectures","Speculative decoding adds compute overhead (draft model forward pass) that may not pay off if acceptance rate is low (<50%)","Verification batch size is limited by GPU memory; cannot verify too many candidates in parallel","Latency improvement is workload-dependent; some distributions have low draft model accuracy"],"requires":["NVIDIA GPU with Ampere or newer architecture","CUDA 12.0+","TensorRT 9.0+","Pre-trained draft model (EAGLE3 or custom) or training data to fine-tune one"],"input_types":["Verifier model (main LLM)","Draft model (smaller LLM or EAGLE3 module)","Speculation strategy configuration (num_tokens, acceptance_threshold)"],"output_types":["Generated tokens (same as standard decoding)","Speculation metrics (acceptance rate, latency reduction, compute overhead)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tensorrt-llm__cap_7","uri":"capability://automation.workflow.mixture.of.experts.moe.with.expert.parallelism.and.load.balancing","name":"mixture of experts (moe) with expert parallelism and load balancing","description":"Implements efficient MoE inference with expert parallelism where experts are distributed across GPUs and routing decisions are made per token. Supports multiple MoE backends (TRTLLM native, custom implementations) and communication strategies (all-to-all, hierarchical). Includes expert load balancing to minimize GPU idle time and communication overhead. Supports quantization of expert weights independently from dense layers.","intents":["Efficiently serve MoE models (Mixtral, Grok) on multi-GPU systems","Achieve near-linear scaling with number of experts through expert parallelism","Minimize communication overhead through hierarchical routing and batching","Balance load across experts to prevent GPU stalls"],"best_for":["Teams deploying MoE models (Mixtral 8x7B, Grok-1) in production","Multi-GPU clusters with high-speed interconnects","Workloads where expert load is unbalanced (e.g., domain-specific queries)","Research exploring MoE architectures and routing strategies"],"limitations":["Expert parallelism requires all-to-all communication which is expensive on slow interconnects","Load balancing overhead adds 5-10ms per forward pass","Routing decisions are made per-token; cannot batch across tokens for better load balancing","Expert quantization may reduce accuracy if experts have different numerical ranges"],"requires":["Multiple NVIDIA GPUs (4+ recommended)","CUDA 12.0+","NCCL 2.18+ for efficient all-to-all communication","MPI runtime for multi-node setups","High-speed GPU interconnect (NVLink or InfiniBand)"],"input_types":["MoE model architecture (num_experts, expert_dim, routing_strategy)","Expert assignment (which experts on which GPU)","Load balancing strategy (round-robin, least-loaded, custom)"],"output_types":["Routed token batches (tokens assigned to experts)","Expert outputs (token embeddings)","Load balancing metrics (expert utilization, communication overhead)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tensorrt-llm__cap_8","uri":"capability://tool.use.integration.openai.compatible.api.server.with.function.calling.and.tool.integration","name":"openai-compatible api server with function calling and tool integration","description":"Implements a Triton Inference Server backend that exposes TensorRT-LLM models via OpenAI-compatible REST API endpoints. Supports function calling through a schema-based function registry where tools are defined as JSON schemas and the model generates function calls that are executed and fed back into the context. Includes response post-processing to extract structured outputs (JSON, function calls) from model generations.","intents":["Deploy TensorRT-LLM models as drop-in replacements for OpenAI API","Enable function calling and tool use without modifying client code","Build agentic systems where models can call external tools and APIs","Implement structured output extraction for downstream processing"],"best_for":["Teams migrating from OpenAI API to self-hosted inference","Building agentic systems with tool calling capabilities","Production deployments requiring OpenAI API compatibility","Multi-tenant systems where function registry needs to be dynamic"],"limitations":["Function calling requires model fine-tuning or in-context learning; base models may have low accuracy","Response post-processing adds 10-50ms latency depending on output complexity","Function registry is static at server startup; cannot dynamically add/remove tools","No built-in function execution sandbox; requires external tool implementations"],"requires":["Triton Inference Server 2.40+","NVIDIA GPU with Ampere or newer architecture","CUDA 12.0+","TensorRT 9.0+","Python 3.10+ with FastAPI or similar web framework"],"input_types":["OpenAI API requests (messages, model, temperature, tools)","Function schemas (JSON Schema format)","Tool implementations (Python callables)"],"output_types":["OpenAI API responses (choices, usage, function_call)","Structured outputs (JSON, function calls)","Streaming responses (SSE format)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tensorrt-llm__cap_9","uri":"capability://automation.workflow.disaggregated.prefill.decode.serving.with.service.discovery","name":"disaggregated prefill-decode serving with service discovery","description":"Implements a disaggregated serving architecture where prefill (prompt processing) and decode (token generation) are separated into independent worker pools that communicate via gRPC. Prefill workers process incoming requests and generate initial KV cache, which is transferred to decode workers for token generation. Includes service discovery and load balancing to route requests to available workers and handle worker failures.","intents":["Achieve 2-3x higher throughput by separating prefill and decode workloads","Optimize hardware utilization by running prefill on compute-optimized GPUs and decode on memory-optimized GPUs","Scale prefill and decode independently based on workload characteristics","Implement fault tolerance through worker redundancy and request replay"],"best_for":["High-throughput inference services (1000+ QPS)","Multi-tenant systems with heterogeneous workloads","Teams with diverse GPU inventory (mix of H100, A100, L40S)","Production systems requiring fault tolerance and auto-scaling"],"limitations":["KV cache transfer between workers adds 5-20ms latency depending on network bandwidth","Requires low-latency network (sub-5ms) between prefill and decode workers; WAN deployments are impractical","Service discovery and load balancing add operational complexity","Disaggregated architecture complicates debugging and monitoring"],"requires":["Multiple NVIDIA GPUs (4+ recommended)","CUDA 12.0+","TensorRT 9.0+","gRPC 1.50+","Service discovery system (Consul, etcd, Kubernetes)","High-speed network (10Gbps+ recommended)"],"input_types":["Prefill requests (prompts, sampling params)","Worker configuration (prefill/decode worker counts, GPU assignment)","Load balancing strategy (round-robin, least-loaded, custom)"],"output_types":["Generated tokens (same as standard serving)","Service discovery metadata (worker endpoints, health status)","Performance metrics (prefill latency, decode latency, throughput)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tensorrt-llm__headline","uri":"capability://deployment.infra.nvidia.gpu.optimized.llm.inference.framework","name":"nvidia gpu-optimized llm inference framework","description":"TensorRT-LLM is a framework designed to optimize large language model inference on NVIDIA GPUs, utilizing advanced techniques like quantization and kernel fusion for maximum performance.","intents":["best LLM inference framework","LLM optimization for NVIDIA GPUs","high-performance LLM deployment","quantization techniques for LLMs","GPU-accelerated inference for transformers"],"best_for":["NVIDIA hardware users","developers optimizing LLMs"],"limitations":[],"requires":["NVIDIA GPU"],"input_types":["large language models"],"output_types":["optimized inference performance"],"categories":["deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"low","permissions":["NVIDIA GPU with Ampere or newer architecture (A100, H100, RTX 4090, etc.)","CUDA 12.0+","TensorRT 9.0+","Python 3.10+","NVIDIA GPU with compute capability 8.0+ (A100, H100, RTX 3090, etc.)","For disaggregated serving: NVLink or high-speed interconnect between GPUs","NVIDIA GPU with Ampere or newer architecture","Sufficient GPU memory (24GB+ for 70B models)","Vision encoder model (CLIP, SigLIP, or compatible)","Image processing libraries (PIL, OpenCV)"],"failure_modes":["Quantization requires offline calibration on representative data — cannot be applied post-hoc to arbitrary checkpoints","FP8 quantization may lose 1-3% accuracy on reasoning tasks; 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