{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"llama-3-1-405b","slug":"llama-3-1-405b","name":"Llama 3.1 405B","type":"model","url":"https://ai.meta.com/blog/meta-llama-3-1/","page_url":"https://unfragile.ai/llama-3-1-405b","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"llama-3-1-405b__cap_0","uri":"capability://text.generation.language.long.context.text.generation.with.128k.token.window","name":"long-context text generation with 128k token window","description":"Generates coherent multi-turn conversations and long-form content up to 128K tokens using a transformer architecture trained on 15+ trillion tokens. Implements standard causal language modeling with attention mechanisms optimized for extended context, enabling document-length reasoning and synthesis without context truncation. The 128K window allows processing of entire codebases, research papers, or conversation histories in a single inference pass.","intents":["Generate comprehensive technical documentation from entire codebase context","Summarize multi-page research papers or legal documents without splitting","Maintain coherent multi-turn conversations with full dialogue history","Process and reason over large knowledge bases in a single prompt"],"best_for":["Developers building document analysis systems requiring full-context reasoning","Teams processing long-form content without chunking overhead","Researchers needing end-to-end document understanding"],"limitations":["Requires multi-GPU inference — single-GPU deployment not supported, necessitating distributed inference infrastructure","Latency scales with context length; 128K token inputs will have significantly higher per-token latency than shorter contexts","Memory footprint for 405B parameters with 128K context exceeds typical single-machine VRAM budgets"],"requires":["Multi-GPU cluster (specific VRAM requirements unknown from documentation)","Inference framework supporting long-context attention (vLLM, TensorRT-LLM, or similar)","API access via Meta's llama.meta.com, Hugging Face, or ecosystem partner (AWS, Azure, Google Cloud)"],"input_types":["text","code","markdown","structured prompts"],"output_types":["text","code","structured responses"],"categories":["text-generation-language","long-context-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-1-405b__cap_1","uri":"capability://text.generation.language.multilingual.text.generation.across.8.languages","name":"multilingual text generation across 8 languages","description":"Generates fluent text in 8 supported languages using a unified transformer trained on multilingual corpora. The model learns language-agnostic representations during training, allowing it to switch between languages and handle code-switching within single responses. Supports conversational agents, translation-adjacent tasks, and localized content generation without language-specific fine-tuning.","intents":["Build conversational agents serving users in multiple languages from single model","Generate localized content (marketing copy, documentation) in target languages","Handle code-switching in multilingual user bases without separate model deployments","Support international teams with language-agnostic reasoning"],"best_for":["International SaaS platforms requiring multi-language support without model multiplication","Teams building global conversational AI without language-specific infrastructure","Content platforms needing localization at scale"],"limitations":["Only 8 languages supported — specific languages not enumerated in documentation, implying gaps for less-represented languages","Multilingual performance may degrade for low-resource languages if training data was imbalanced","No documented language-specific fine-tuning capability; performance varies by language"],"requires":["API access via Meta, Hugging Face, or ecosystem partner","Multi-GPU inference infrastructure","Language specification in prompt or system message"],"input_types":["text in any of 8 supported languages","code-switched prompts"],"output_types":["text in target language","code-switched responses"],"categories":["text-generation-language","multilingual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-1-405b__cap_10","uri":"capability://safety.moderation.prompt.injection.detection.with.prompt.guard","name":"prompt injection detection with prompt guard","description":"Detects and flags prompt injection attacks using Prompt Guard, a security tool released alongside 405B. Prompt Guard classifies prompts to identify attempts to manipulate model behavior through adversarial inputs, enabling security-aware applications to reject or handle suspicious prompts. The tool operates as a separate classification model that scores prompt safety before inference.","intents":["Detect jailbreak attempts and adversarial prompts before they reach 405B","Implement security controls for user-facing applications","Monitor for prompt injection attacks in production systems","Prevent unauthorized behavior modification through adversarial prompts"],"best_for":["Security-critical applications requiring defense against prompt injection","Multi-tenant systems where user prompts may be adversarial","Applications with strict access controls and behavior constraints","Teams implementing defense-in-depth security strategies"],"limitations":["Prompt Guard is separate model requiring additional inference pass — adds latency","Detection is probabilistic; sophisticated adversarial prompts may evade detection","No documented accuracy metrics or false positive/negative rates","Prompt injection landscape evolves; detection rules may become outdated","Cannot prevent all forms of prompt manipulation — only detects known attack patterns"],"requires":["Multi-GPU inference infrastructure for Prompt Guard","API access via Meta, Hugging Face, or ecosystem partner","Integration layer to check prompts before sending to 405B","Policies for handling detected injections (reject, log, alert)"],"input_types":["user prompts","system messages"],"output_types":["injection risk score","classification (safe/unsafe)","threat indicators"],"categories":["safety-moderation","security"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-1-405b__cap_11","uri":"capability://text.generation.language.consumer.facing.deployment.via.whatsapp.and.meta.ai","name":"consumer-facing deployment via whatsapp and meta.ai","description":"Llama 3.1 405B is accessible to end users through WhatsApp (US only) and meta.ai web interface, enabling non-technical users to interact with the model without API integration or infrastructure setup. These consumer deployments abstract away inference complexity and provide familiar interfaces for conversational AI. The model powers Meta's consumer AI products, demonstrating production-grade reliability and safety.","intents":["Provide end users with direct access to 405B capabilities without technical setup","Validate model quality and safety in production consumer environments","Gather user feedback and usage patterns for model improvement","Demonstrate 405B capabilities to non-technical audiences"],"best_for":["End users wanting to experiment with 405B without technical knowledge","Teams evaluating model quality through consumer-grade interfaces","Organizations benchmarking against consumer AI products","Researchers studying user interactions with large language models"],"limitations":["WhatsApp access limited to US only — geographic restrictions apply","Consumer interfaces may have rate limiting or usage quotas","No API access through consumer interfaces — cannot integrate into applications","Consumer deployments may use quantized or optimized versions of 405B — performance may differ from full model","No control over system prompts or model behavior in consumer interfaces"],"requires":["WhatsApp account (for WhatsApp access, US only)","Web browser (for meta.ai access)","No technical setup required"],"input_types":["natural language text"],"output_types":["natural language responses"],"categories":["text-generation-language","consumer-product"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-1-405b__cap_12","uri":"capability://automation.workflow.open.weight.model.distribution.via.hugging.face.and.meta.repositories","name":"open-weight model distribution via hugging face and meta repositories","description":"Llama 3.1 405B is distributed as open-weight model files through Hugging Face Model Hub and llama.meta.com, enabling developers to download and deploy the model locally or on custom infrastructure. The model is released under an open license (specific license terms not enumerated in documentation) that allows commercial use and modification. Distribution includes model weights in standard formats compatible with popular inference frameworks.","intents":["Download and deploy 405B on custom infrastructure without vendor lock-in","Fine-tune 405B on proprietary data for domain-specific applications","Integrate 405B into existing ML pipelines and workflows","Build custom inference optimizations for specific hardware"],"best_for":["Organizations requiring on-premises deployment for data privacy or compliance","Teams with custom hardware or inference optimization requirements","Researchers fine-tuning or modifying the base model","Companies avoiding vendor lock-in with proprietary APIs"],"limitations":["Model size (405B parameters) requires significant storage and bandwidth for download","Multi-GPU inference infrastructure required — cannot run on single GPU or CPU","Specific quantization formats and model file formats not documented — requires inference framework compatibility research","No official fine-tuning guidance or examples provided","Commercial use terms not explicitly stated in documentation — requires reviewing license text"],"requires":["Hugging Face account or direct access to llama.meta.com","Sufficient storage for 405B model weights (estimated 800GB+ for full precision)","Multi-GPU infrastructure for inference","Inference framework (vLLM, TensorRT-LLM, Ollama, etc.)","Bandwidth for downloading model files"],"input_types":["model weight files","configuration files"],"output_types":["deployed model instance","inference API"],"categories":["automation-workflow","infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-1-405b__cap_13","uri":"capability://planning.reasoning.reference.system.for.building.custom.agents.and.applications","name":"reference system for building custom agents and applications","description":"Meta provides reference implementations and system prompts for building custom agents, conversational systems, and applications using Llama 3.1 405B. The reference system includes best practices for prompt engineering, tool integration, safety filtering, and multi-turn conversation management. Developers can use these references as starting points for building domain-specific applications without starting from scratch.","intents":["Accelerate development of custom agents by using reference implementations","Learn best practices for prompt engineering and system design with 405B","Implement safety and moderation patterns recommended by Meta","Build domain-specific applications using proven architectural patterns"],"best_for":["Teams building custom agents and conversational AI applications","Developers new to large language models seeking guidance","Organizations implementing safety and moderation best practices","Startups accelerating time-to-market for AI applications"],"limitations":["Reference system details not documented in announcement — specific implementations and patterns unknown","Reference implementations may not cover all use cases or domains","Best practices may evolve as model is used in production — reference system may become outdated","No guarantee that reference patterns will work for all applications — customization required"],"requires":["Access to reference documentation (location and format unknown)","Understanding of prompt engineering and agent design","Integration with 405B API or local deployment"],"input_types":["reference implementations","system prompts","architectural patterns"],"output_types":["custom agent implementations","application code","system prompts"],"categories":["planning-reasoning","agent-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-1-405b__cap_14","uri":"capability://data.processing.analysis.model.distillation.and.knowledge.transfer.to.smaller.models","name":"model distillation and knowledge transfer to smaller models","description":"Enables distillation of 405B knowledge into smaller, faster models through synthetic data generation and fine-tuning. The model can generate training data for smaller models, and its outputs can be used as targets for knowledge distillation. This capability is explicitly called out as 'never achieved at this scale in open source,' enabling organizations to create specialized, efficient models that inherit 405B's capabilities.","intents":["Create smaller, faster models that inherit 405B's reasoning and knowledge","Reduce inference latency and cost by deploying distilled models instead of 405B","Build domain-specific models by distilling 405B on specialized data","Enable edge deployment of 405B-derived models on resource-constrained devices"],"best_for":["Teams needing faster inference than 405B but higher quality than smaller open-source models","Organizations building specialized models for specific domains","Companies deploying to edge devices or resource-constrained environments","Researchers exploring model distillation techniques at scale"],"limitations":["Distillation effectiveness not quantified — no benchmarks showing performance of distilled models","Inference cost for generating synthetic data is high due to 405B model size","Distilled model quality depends on distillation technique and training data — requires experimentation","No official distillation guidance or tools provided — requires custom implementation","Distilled models may not achieve 405B performance across all tasks — trade-offs required"],"requires":["Multi-GPU infrastructure for 405B inference (for synthetic data generation)","Training infrastructure for distilled models","Distillation framework (custom or third-party)","Evaluation methodology for assessing distilled model quality"],"input_types":["405B model outputs","training data","distillation targets"],"output_types":["distilled model weights","smaller model instances"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-1-405b__cap_2","uri":"capability://code.generation.editing.code.generation.and.completion.with.89.humaneval.performance","name":"code generation and completion with 89% humaneval performance","description":"Generates syntactically correct and functionally sound code across multiple programming languages using transformer-based code understanding trained on code-heavy portions of the 15+ trillion token dataset. Achieves 89% pass rate on HumanEval benchmark, indicating strong capability for function-level code generation, completion, and bug fixing. Works through standard next-token prediction with learned patterns from diverse codebases.","intents":["Auto-complete code functions from docstrings or partial implementations","Generate boilerplate and scaffolding code for common patterns","Refactor or optimize existing code snippets","Debug code by generating fixes for identified issues"],"best_for":["Developers using IDE integrations or code editors for real-time completion","Teams automating code generation in CI/CD pipelines","Developers prototyping solutions quickly with generated scaffolding"],"limitations":["HumanEval benchmark measures function-level generation; multi-file refactoring or architectural-level code generation not explicitly documented","No codebase-aware indexing mentioned — cannot leverage project-specific patterns or internal libraries without in-context examples","Inference latency for 405B model makes real-time IDE completion slower than smaller specialized code models","No built-in code execution or validation; generated code requires manual testing"],"requires":["Multi-GPU inference infrastructure","API access via Meta, Hugging Face, or ecosystem partner","Integration layer (IDE plugin, API wrapper, or framework)"],"input_types":["code snippets","docstrings","natural language descriptions","partial function signatures"],"output_types":["code","function implementations","refactored code"],"categories":["code-generation-editing","programming"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-1-405b__cap_3","uri":"capability://planning.reasoning.mathematical.reasoning.with.96.8.gsm8k.accuracy","name":"mathematical reasoning with 96.8% gsm8k accuracy","description":"Solves grade-school math word problems and multi-step mathematical reasoning tasks with 96.8% accuracy on the GSM8K benchmark. Implements chain-of-thought reasoning patterns learned during training on mathematical problem-solving data within the 15+ trillion token corpus. The model breaks down problems into intermediate steps and performs arithmetic reasoning without external calculators.","intents":["Solve math word problems in educational or tutoring applications","Perform multi-step mathematical reasoning for quantitative analysis","Validate mathematical correctness of student work or generated solutions","Generate step-by-step explanations for mathematical problem-solving"],"best_for":["EdTech platforms building AI tutoring systems","Quantitative analysis tools requiring mathematical reasoning","Educational content generation systems"],"limitations":["GSM8K benchmark covers grade-school math; performance on advanced mathematics (calculus, linear algebra, abstract algebra) not documented","No symbolic math engine — relies on learned patterns rather than formal verification, risking arithmetic errors in complex calculations","Chain-of-thought reasoning adds latency; real-time tutoring applications may experience delays","Cannot perform symbolic manipulation or formal proofs"],"requires":["Multi-GPU inference infrastructure","API access via Meta, Hugging Face, or ecosystem partner","Prompt engineering to elicit chain-of-thought reasoning"],"input_types":["natural language math word problems","mathematical expressions","step-by-step problem descriptions"],"output_types":["numerical answers","step-by-step solutions","explanations"],"categories":["planning-reasoning","mathematics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-1-405b__cap_4","uri":"capability://tool.use.integration.native.tool.use.and.function.calling.with.state.of.the.art.performance","name":"native tool use and function calling with state-of-the-art performance","description":"Executes tool calls and function invocations through learned patterns in the transformer, enabling the model to decide when to invoke external APIs, databases, or code execution environments. Implements tool use as a learned behavior during training rather than through constrained decoding, allowing flexible tool composition and multi-step tool orchestration. The model generates structured tool calls that downstream systems parse and execute.","intents":["Build AI agents that autonomously call APIs (weather, search, payment systems) to fulfill user requests","Create multi-step workflows where the model decides which tools to invoke and in what order","Integrate LLM reasoning with external data sources (databases, knowledge bases, real-time APIs)","Implement retrieval-augmented generation (RAG) where the model decides when to search for information"],"best_for":["Teams building autonomous agents requiring flexible tool orchestration","Developers implementing RAG systems where the model controls retrieval decisions","Applications requiring multi-step workflows with dynamic tool selection"],"limitations":["Tool use is learned behavior, not constrained — model may hallucinate tool calls or use tools incorrectly without proper guardrails","No built-in tool registry or schema validation — requires external system to parse and validate tool calls before execution","Inference latency for 405B model adds overhead to tool-calling loops; multi-step workflows may be slow","Tool use performance not quantified in documentation — only 'state-of-the-art' claim without specific benchmarks"],"requires":["Multi-GPU inference infrastructure","API access via Meta, Hugging Face, or ecosystem partner","Tool execution framework (custom or third-party) to parse and invoke tool calls","Tool definitions or schema provided in system prompt or context"],"input_types":["natural language requests","tool definitions or schemas","context about available tools"],"output_types":["tool calls (structured format)","reasoning about tool selection","final responses after tool execution"],"categories":["tool-use-integration","agent-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-1-405b__cap_5","uri":"capability://data.processing.analysis.synthetic.data.generation.for.model.training.and.distillation","name":"synthetic data generation for model training and distillation","description":"Generates high-quality synthetic training data that can be used to train smaller models through distillation, leveraging the 405B model's reasoning and knowledge to create diverse, labeled datasets. The model produces varied outputs across different prompts and temperature settings, enabling creation of large synthetic datasets without manual annotation. This capability enables open-source model distillation at scale, previously unavailable in the open-source ecosystem.","intents":["Create synthetic training datasets for fine-tuning smaller models without manual annotation","Distill 405B knowledge into smaller, faster models for production deployment","Generate diverse examples for few-shot learning or in-context learning","Build domain-specific datasets by prompting the model with domain examples"],"best_for":["Teams building specialized models for specific domains or use cases","Organizations wanting to distill 405B capabilities into smaller, faster models","Researchers exploring model distillation techniques at scale","Companies needing large labeled datasets without annotation costs"],"limitations":["Synthetic data quality depends on prompt engineering — poorly designed prompts produce low-quality training data","No documented evaluation metrics for synthetic data quality — requires manual validation or downstream task evaluation","Inference cost for generating large synthetic datasets is high due to 405B model size","Distillation effectiveness not quantified — no benchmarks showing performance of distilled models vs. original","License modification allows using outputs to improve other models, but commercial restrictions on synthetic data usage unclear"],"requires":["Multi-GPU inference infrastructure for 405B model","API access via Meta, Hugging Face, or ecosystem partner","Downstream training infrastructure for distilled models","Prompt templates for generating domain-specific data"],"input_types":["prompt templates","domain examples","task descriptions"],"output_types":["synthetic text examples","labeled datasets","training corpora"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-1-405b__cap_6","uri":"capability://text.generation.language.general.knowledge.reasoning.with.88.6.mmlu.performance","name":"general knowledge reasoning with 88.6% mmlu performance","description":"Answers factual questions and performs reasoning across diverse knowledge domains (science, history, law, medicine, etc.) with 88.6% accuracy on the MMLU benchmark. Implements knowledge retrieval through learned patterns in the 405B transformer trained on 15+ trillion tokens, enabling broad-domain question-answering without external knowledge bases. The model reasons through multiple-choice questions and open-ended queries using learned world knowledge.","intents":["Build question-answering systems for educational or informational applications","Implement knowledge-based chatbots that answer factual queries","Validate factual correctness of generated content","Support research and analysis by retrieving relevant knowledge"],"best_for":["Educational platforms building AI tutoring or homework help systems","Knowledge-based chatbots and virtual assistants","Content validation and fact-checking systems","Research support tools requiring broad domain knowledge"],"limitations":["MMLU benchmark tests knowledge up to training cutoff date — no real-time information or recent events without external retrieval","Knowledge is learned from training data; no mechanism to update knowledge without retraining","No cited sources or evidence for answers — model cannot explain where knowledge comes from","Hallucination risk for obscure or edge-case questions not well-represented in training data","Performance varies significantly across domains; some specialized knowledge areas may have lower accuracy"],"requires":["Multi-GPU inference infrastructure","API access via Meta, Hugging Face, or ecosystem partner","Optional: external knowledge base or search system for fact-checking or real-time information"],"input_types":["natural language questions","multiple-choice prompts","open-ended queries"],"output_types":["factual answers","explanations","reasoning"],"categories":["text-generation-language","knowledge-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-1-405b__cap_7","uri":"capability://text.generation.language.steerability.and.instruction.following.with.fine.grained.control","name":"steerability and instruction-following with fine-grained control","description":"Follows complex, multi-part instructions and adapts behavior based on system prompts, in-context examples, and user directives through learned instruction-following patterns in the transformer. The model interprets nuanced requests, respects tone and style preferences, and maintains consistency with specified constraints throughout long conversations. Steerability is achieved through training on diverse instruction-following examples within the 15+ trillion token dataset.","intents":["Build customizable AI assistants that adapt to user preferences and organizational guidelines","Implement role-playing agents with consistent personas and behavioral constraints","Create content generation systems that respect style, tone, and format requirements","Develop safety-aligned systems by steering model behavior through system prompts"],"best_for":["Teams building customizable AI assistants for enterprise or consumer applications","Developers implementing role-based or persona-driven conversational AI","Content platforms requiring consistent brand voice and style","Safety-critical applications using prompt-based alignment"],"limitations":["Steerability is learned behavior, not guaranteed — complex or conflicting instructions may be misinterpreted","No formal verification that model respects constraints — requires testing and validation","Adversarial prompts or jailbreak attempts may override system instructions","Steerability performance not quantified in documentation — no benchmarks for instruction-following accuracy","Long conversations may drift from initial instructions due to context length and attention dynamics"],"requires":["Multi-GPU inference infrastructure","API access via Meta, Hugging Face, or ecosystem partner","Careful prompt engineering and system message design","Testing and validation of instruction-following behavior"],"input_types":["system prompts","user instructions","in-context examples","conversational context"],"output_types":["instruction-following responses","persona-consistent outputs","style-adapted content"],"categories":["text-generation-language","instruction-following"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-1-405b__cap_8","uri":"capability://automation.workflow.multi.gpu.distributed.inference.with.ecosystem.partner.integrations","name":"multi-gpu distributed inference with ecosystem partner integrations","description":"Executes inference across multiple GPUs using distributed tensor parallelism and pipeline parallelism, coordinated through inference frameworks and cloud platforms. The 405B model is available through 25+ ecosystem partners (AWS, Azure, Google Cloud, NVIDIA, Groq, Databricks, etc.) on day one, each providing optimized inference infrastructure and APIs. Inference is not available as single-GPU deployment; all inference requires multi-GPU coordination.","intents":["Deploy 405B model in production without building custom inference infrastructure","Leverage cloud provider optimizations for cost-effective inference","Scale inference across multiple requests using managed infrastructure","Access model through familiar cloud provider APIs and SDKs"],"best_for":["Teams deploying to AWS, Azure, or Google Cloud without custom infrastructure","Organizations wanting managed inference without operational overhead","Startups and smaller teams lacking GPU infrastructure expertise","Applications requiring auto-scaling and high availability"],"limitations":["Multi-GPU requirement increases infrastructure cost and complexity — single-GPU inference not supported","Specific VRAM requirements per GPU not documented — requires consulting partner documentation","Inference latency for 405B model is higher than smaller models due to parameter count","Vendor lock-in risk if using proprietary APIs rather than standard interfaces","Inference cost is high due to model size; may not be economical for latency-sensitive or high-volume applications"],"requires":["Cloud account with AWS, Azure, Google Cloud, or other ecosystem partner","API credentials and authentication","Budget for inference compute (pricing varies by partner)","Integration with partner SDKs or REST APIs"],"input_types":["text prompts","API requests"],"output_types":["text responses","structured API responses"],"categories":["automation-workflow","infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-1-405b__cap_9","uri":"capability://safety.moderation.safety.filtering.and.content.moderation.with.llama.guard.3","name":"safety filtering and content moderation with llama guard 3","description":"Filters unsafe content and detects policy violations using Llama Guard 3, a companion safety model released alongside 405B. Llama Guard 3 classifies inputs and outputs against safety categories (violence, sexual content, illegal activity, etc.), enabling content moderation in both user inputs and model outputs. The safety model is integrated into the ecosystem but operates as a separate inference pass, not built into 405B itself.","intents":["Filter user inputs before sending to 405B to prevent jailbreak attempts","Moderate model outputs to prevent unsafe content generation","Implement content policies for enterprise or regulated applications","Detect and block prompt injection attacks"],"best_for":["Teams building consumer-facing applications requiring content moderation","Regulated industries (finance, healthcare, education) with compliance requirements","Applications serving minors or sensitive user populations","Enterprise deployments with strict content policies"],"limitations":["Llama Guard 3 is separate model requiring additional inference pass — adds latency and cost","Safety classification is probabilistic; false positives and false negatives are possible","Safety categories may not align with all organizational policies — requires customization","No documented accuracy metrics for Llama Guard 3 — effectiveness unclear","Adversarial attacks may evade safety filters; no guarantee of robustness"],"requires":["Multi-GPU inference infrastructure for both 405B and Llama Guard 3","API access via Meta, Hugging Face, or ecosystem partner","Integration layer to orchestrate safety filtering before/after 405B inference","Policy definitions for safety categories"],"input_types":["user prompts","model outputs"],"output_types":["safety classification","policy violation flags","filtered content"],"categories":["safety-moderation","content-filtering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-1-405b__headline","uri":"capability://text.generation.language.large.open.weight.language.model","name":"large open-weight language model","description":"Llama 3.1 405B is the largest open-weight language model with 405 billion parameters, designed for advanced text generation, code generation, and multilingual tasks, setting a new standard in open-source AI.","intents":["best large language model","open-source model for text generation","top model for code generation","language model for multilingual tasks","best model for synthetic data generation"],"best_for":["developers seeking powerful language models","researchers in AI"],"limitations":["requires multi-GPU for inference"],"requires":["multi-GPU setup"],"input_types":["text"],"output_types":["text","code"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Multi-GPU cluster (specific VRAM requirements unknown from documentation)","Inference framework supporting long-context attention (vLLM, TensorRT-LLM, or similar)","API access via Meta's llama.meta.com, Hugging Face, or ecosystem partner (AWS, Azure, Google Cloud)","API access via Meta, Hugging Face, or ecosystem partner","Multi-GPU inference infrastructure","Language specification in prompt or system message","Multi-GPU inference infrastructure for Prompt Guard","Integration layer to check prompts before sending to 405B","Policies for handling detected injections (reject, log, alert)","WhatsApp account (for WhatsApp access, US only)"],"failure_modes":["Requires multi-GPU inference — single-GPU deployment not supported, necessitating distributed inference infrastructure","Latency scales with context length; 128K token inputs will have significantly higher per-token latency than shorter contexts","Memory footprint for 405B parameters with 128K context exceeds typical single-machine VRAM budgets","Only 8 languages supported — specific languages not enumerated in documentation, implying gaps for less-represented languages","Multilingual performance may degrade for low-resource languages if training data was imbalanced","No documented language-specific fine-tuning capability; performance varies by language","Prompt Guard is separate model requiring additional inference pass — adds latency","Detection is probabilistic; sophisticated adversarial prompts may evade detection","No documented accuracy metrics or false positive/negative rates","Prompt injection landscape evolves; detection rules may become outdated","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.327Z","last_scraped_at":null,"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=llama-3-1-405b","compare_url":"https://unfragile.ai/compare?artifact=llama-3-1-405b"}},"signature":"aQ9IJGZdulz3bjxHFjM911hUFvfDjFk7DiVvPH3VRD39P39fXzUtyI9p8GALOr2i4NSh5kzyfjnBo90hwMWhDw==","signedAt":"2026-06-22T04:24:21.540Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llama-3-1-405b","artifact":"https://unfragile.ai/llama-3-1-405b","verify":"https://unfragile.ai/api/v1/verify?slug=llama-3-1-405b","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"}}