{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"sambanova","slug":"sambanova","name":"SambaNova","type":"platform","url":"https://sambanova.ai","page_url":"https://unfragile.ai/sambanova","categories":["deployment-infra"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"sambanova__cap_0","uri":"capability://text.generation.language.rdu.accelerated.text.generation.inference","name":"rdu-accelerated text generation inference","description":"Executes large language model inference on custom SN50 Reconfigurable Dataflow Unit (RDU) chips optimized for token generation workloads. Uses a three-tier memory architecture and custom dataflow technology to parallelize computation across prefill and decode phases, enabling high-throughput inference for Llama and open-source models without requiring cloud API calls to external providers.","intents":["Run LLM inference at scale with lower latency than GPU-based alternatives","Deploy proprietary or open-source models with custom silicon optimization","Reduce inference costs per token through hardware-specific efficiency gains","Execute agentic AI workflows with multiple model switches on a single compute node"],"best_for":["Enterprise teams requiring sovereign AI deployments with data residency guarantees","Builders optimizing for cost-per-inference in high-volume production workloads","Organizations deploying complex agentic AI systems with multi-model orchestration"],"limitations":["Model availability limited to Llama and unspecified open-source models — no access to proprietary frontier models like GPT-4 or Claude","No documented latency metrics (p50, p95, p99) or time-to-first-token (TTFT) specifications available","Maximum context window and token limits not publicly specified","RDU hardware availability constrained to SambaNova-managed infrastructure; no local deployment option for custom silicon"],"requires":["API key for SambaNova platform (format and authentication mechanism not documented)","Network connectivity to SambaNova inference endpoints or sovereign data center partners","Compliance with SambaNova acceptable use policy (not publicly available in provided documentation)"],"input_types":["text prompts","conversation history (assumed based on agentic AI claims)","structured tool/function definitions (implied by model bundling capability)"],"output_types":["text completions","token probability distributions (assumed)","structured function call responses (implied by agentic AI positioning)"],"categories":["text-generation-language","inference-acceleration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sambanova__cap_1","uri":"capability://planning.reasoning.multi.model.bundling.and.dynamic.switching","name":"multi-model bundling and dynamic switching","description":"Enables loading and switching between multiple frontier-scale language models within a single inference session on SambaNova hardware, allowing agentic systems to route requests to different models based on task requirements without incurring inter-node communication overhead. The SambaStack infrastructure layer manages model lifecycle and context preservation across model switches.","intents":["Route different task types to specialized models (e.g., reasoning tasks to one model, code generation to another) within a single agent","Implement cost-optimized inference by using smaller models for simple tasks and larger models for complex reasoning","Execute multi-step agentic workflows that require different model capabilities without network round-trips"],"best_for":["Agentic AI system builders implementing task-specific model routing","Teams optimizing inference cost by dynamically selecting model size based on task complexity","Enterprises deploying complex multi-model orchestration pipelines on-premise or in sovereign data centers"],"limitations":["Specific models available for bundling not documented — unclear which Llama versions and open-source models support this capability","No documented performance overhead for model switching or context preservation guarantees","Maximum number of bundled models per session not specified","Requires all bundled models to fit within SN50 memory architecture — no guidance on model size constraints"],"requires":["SambaNova API access with model bundling feature enabled (feature availability not documented)","Pre-configured model bundle definition (format and schema unknown)","Agentic orchestration layer to implement routing logic (not provided by SambaNova)"],"input_types":["task classification or routing signals","model selection parameters","conversation context to preserve across model switches"],"output_types":["model-specific completions","routing decisions and model selection metadata"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sambanova__cap_2","uri":"capability://automation.workflow.sovereign.ai.data.center.deployment","name":"sovereign ai data center deployment","description":"Provides managed inference infrastructure deployed in sovereign data centers operated by SambaNova partners in Australia, Europe, and the United Kingdom, ensuring data residency compliance and national border constraints. Models and inference computations execute entirely within specified geographic boundaries without cross-border data transfer, addressing regulatory requirements for sensitive workloads.","intents":["Deploy AI inference for regulated industries (healthcare, finance, government) with strict data residency requirements","Ensure compliance with GDPR, national AI regulations, and data sovereignty mandates","Execute open-source models within national borders as required by some jurisdictions"],"best_for":["European enterprises subject to GDPR and data residency mandates","Australian government and regulated sector organizations","UK-based organizations requiring data to remain within UK jurisdiction","Multinational teams with distributed data residency requirements across regions"],"limitations":["Specific data center locations and latency profiles by region not documented","No published SLA guarantees, uptime commitments, or disaster recovery specifications","Compliance certifications (SOC2, ISO 27001, GDPR adequacy) not listed in provided documentation","Regional availability limited to three geographic zones — no coverage for Asia-Pacific (except Australia), Americas, or Middle East","Data residency guarantees scope not defined — unclear if logs, backups, and metadata also remain in-region"],"requires":["Enterprise contract with SambaNova (minimum commitment and terms not documented)","Verification of data residency requirements and regulatory compliance obligations","Network connectivity to specified sovereign data center region","Acceptance of SambaNova's data processing terms and DPA (Data Processing Agreement)"],"input_types":["inference requests with geographic routing hints","compliance metadata indicating required data residency region"],"output_types":["inference results with residency attestation","compliance audit logs (format and retention period unknown)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sambanova__cap_3","uri":"capability://planning.reasoning.heterogeneous.inference.orchestration.with.cpu.gpu.rdu.pipeline","name":"heterogeneous inference orchestration with cpu-gpu-rdu pipeline","description":"Coordinates inference execution across heterogeneous hardware (Intel Xeon CPUs for agentic tool execution, GPUs for prefill phase, RDUs for decode phase) within a single inference blueprint, optimizing each computation stage for its hardware strengths. The SambaStack infrastructure layer manages data movement, synchronization, and scheduling across the heterogeneous pipeline.","intents":["Optimize inference throughput by parallelizing prefill (GPU) and decode (RDU) phases on specialized hardware","Execute agentic tool calls (function invocations, API requests) on CPUs while maintaining LLM inference on RDUs","Reduce overall inference latency by eliminating bottlenecks in any single hardware component"],"best_for":["Teams building agentic AI systems that require frequent tool invocation alongside LLM reasoning","Builders optimizing for latency-sensitive applications where prefill-decode separation matters","Enterprises with existing Intel infrastructure seeking to integrate custom AI silicon"],"limitations":["No documented performance breakdown showing GPU prefill vs RDU decode latency contributions","CPU tool execution overhead and scheduling latency not quantified","Data movement and synchronization overhead between heterogeneous components not specified","No guidance on when heterogeneous pipeline is beneficial versus single-device inference","Requires SambaNova-managed infrastructure — no option to deploy heterogeneous blueprint on customer hardware"],"requires":["SambaNova inference endpoint with heterogeneous blueprint enabled","Agentic system design that separates tool execution from LLM reasoning","Tool definitions compatible with CPU execution environment (format and constraints unknown)"],"input_types":["LLM prompts for GPU prefill and RDU decode","tool/function definitions for CPU execution","agentic orchestration signals indicating when to invoke tools"],"output_types":["LLM completions from RDU decode phase","tool execution results from CPU phase","merged agentic responses combining LLM and tool outputs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sambanova__cap_4","uri":"capability://automation.workflow.energy.efficient.token.generation.with.tokens.per.watt.optimization","name":"energy-efficient token generation with tokens-per-watt optimization","description":"Optimizes inference compute and memory access patterns on SN50 RDU hardware to maximize tokens generated per unit of energy consumed, reducing operational costs and carbon footprint for large-scale inference workloads. The custom dataflow architecture and three-tier memory hierarchy are tuned for energy efficiency rather than raw peak throughput.","intents":["Reduce operational electricity costs for high-volume inference deployments","Meet sustainability and carbon reduction targets for AI infrastructure","Achieve cost parity or advantage versus GPU-based inference at scale"],"best_for":["Large-scale inference operations (millions of tokens/day) where energy costs dominate OpEx","Organizations with sustainability commitments or carbon accounting requirements","Enterprises evaluating total cost of ownership (TCO) including power and cooling"],"limitations":["Actual tokens-per-watt metrics not published — only relative claim of '3X savings compared to competitive chips' without baseline specification","No breakdown of energy consumption by inference phase (prefill vs decode)","Power consumption under various load profiles (batch size, sequence length, model size) not documented","Energy efficiency gains may not translate to wall-clock latency improvements — trade-off not clarified","Requires SambaNova-managed infrastructure — cannot measure or optimize energy on customer hardware"],"requires":["High-volume inference workload (minimum throughput threshold not specified)","SambaNova inference contract with energy monitoring and reporting enabled","Baseline energy consumption metrics from alternative platforms for comparison"],"input_types":["inference requests with varying batch sizes and sequence lengths","workload profiles indicating peak vs sustained load patterns"],"output_types":["inference results with energy consumption telemetry","tokens-per-watt metrics and energy efficiency reports"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sambanova__cap_5","uri":"capability://text.generation.language.llama.model.inference.with.open.source.model.support","name":"llama model inference with open-source model support","description":"Provides optimized inference execution for Meta's Llama model family and unspecified open-source language models on SambaNova hardware, with model weights and inference kernels tuned for RDU architecture. Supports model loading, context management, and generation parameters specific to Llama and compatible open-source models.","intents":["Run Llama models (2, 3, or newer versions) without vendor lock-in to proprietary model APIs","Deploy open-source models in regulated environments requiring model transparency and auditability","Avoid licensing costs and usage-based pricing of proprietary LLM APIs"],"best_for":["Teams committed to open-source AI and avoiding proprietary model dependencies","Regulated industries requiring model transparency and ability to audit model behavior","Cost-sensitive deployments where per-token pricing of commercial APIs becomes prohibitive"],"limitations":["Specific Llama versions supported not documented — unclear if Llama 2, 3, 3.1, or future versions are available","Open-source model list not provided — no clarity on which models beyond Llama are supported","No fine-tuning or custom model training capability documented — inference-only platform","Model update cadence not specified — unclear how quickly new Llama versions are supported","No documented model performance benchmarks (latency, throughput) for specific Llama versions"],"requires":["SambaNova API access with Llama model availability in target region","Model weights and tokenizer compatible with SambaNova inference runtime (format not specified)","Familiarity with Llama-specific generation parameters and prompt formatting"],"input_types":["text prompts in Llama chat or base model format","generation parameters (temperature, top_p, max_tokens, etc.)","system prompts and conversation history"],"output_types":["text completions from Llama models","token probabilities and logits (if supported)","generation metadata (tokens used, stop reason)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sambanova__cap_6","uri":"capability://planning.reasoning.agentic.ai.workflow.execution.with.tool.integration","name":"agentic ai workflow execution with tool integration","description":"Executes complex agentic AI workflows that combine LLM reasoning with external tool invocation (function calls, API requests, database queries) on a single SambaNova inference node. The heterogeneous CPU-GPU-RDU pipeline routes tool execution to CPUs while maintaining LLM reasoning on RDUs, enabling tight integration between reasoning and action without inter-node communication.","intents":["Build AI agents that reason about tasks and invoke tools (APIs, functions, databases) in a single coordinated execution","Implement ReAct (Reasoning + Acting) patterns with low latency between reasoning and tool invocation","Deploy multi-step agentic workflows with dynamic tool selection based on LLM reasoning"],"best_for":["Teams building production agentic AI systems requiring sub-second latency between reasoning and tool invocation","Enterprises deploying autonomous agents for customer service, data analysis, or business process automation","Builders implementing complex multi-step workflows that require tight LLM-tool coupling"],"limitations":["Tool definition schema and integration mechanism not documented — unclear how tools are registered and called","No guidance on tool execution timeout, error handling, or retry logic","Maximum number of tool invocations per workflow step not specified","Tool execution environment constraints (memory, CPU time, network access) not documented","No built-in tool library — requires custom tool implementation for each use case","Agentic orchestration logic (planning, tool selection) must be implemented by user — SambaNova provides execution layer only"],"requires":["SambaNova API with agentic workflow support enabled","Tool definitions compatible with SambaNova CPU execution environment","Agentic orchestration framework (e.g., LangChain, AutoGPT, custom implementation)","External services/APIs that tools invoke (not managed by SambaNova)"],"input_types":["high-level task descriptions or goals","tool definitions with signatures and descriptions","context and state from previous workflow steps"],"output_types":["final task completion results","tool invocation logs and execution traces","intermediate reasoning steps and decisions"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sambanova__cap_7","uri":"capability://automation.workflow.enterprise.deployment.with.managed.infrastructure","name":"enterprise deployment with managed infrastructure","description":"Provides managed inference infrastructure for enterprise customers with deployment options including SaaS, managed cloud, and on-premise configurations. SambaNova handles infrastructure provisioning, scaling, monitoring, and maintenance while customers focus on application logic. Deployment options support sovereign AI requirements and custom hardware configurations.","intents":["Deploy production AI inference without managing underlying infrastructure or RDU hardware","Scale inference capacity dynamically based on workload demand","Maintain compliance with data residency and sovereignty requirements through managed deployment"],"best_for":["Enterprise teams lacking infrastructure expertise or resources to manage custom silicon","Organizations requiring managed SLAs, support, and operational guarantees","Teams deploying in sovereign data centers with regulatory compliance requirements"],"limitations":["Deployment models (SaaS vs managed cloud vs on-premise) not clearly differentiated — unclear which options are available","No published SLA guarantees, uptime commitments, or availability targets","Support tiers and response times not documented","Minimum commitment, contract terms, and pricing structure not specified","Scaling policies and auto-scaling configuration options not documented","Monitoring, logging, and observability capabilities not described"],"requires":["Enterprise contract with SambaNova (minimum spend and commitment unknown)","Infrastructure requirements assessment and capacity planning","Compliance and security review process (timeline and requirements unknown)","Dedicated account management and support engagement"],"input_types":["infrastructure requirements and capacity specifications","compliance and regulatory requirements","workload profiles and scaling requirements"],"output_types":["managed inference infrastructure with SLA guarantees","monitoring dashboards and operational metrics","compliance attestations and audit reports"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sambanova__cap_8","uri":"capability://automation.workflow.sambastack.inference.stack.with.model.lifecycle.management","name":"sambastack inference stack with model lifecycle management","description":"Provides an inference stack (SambaStack) that manages model loading, context preservation, memory allocation, and execution scheduling across SambaNova hardware. The stack abstracts RDU-specific details and provides a unified interface for model bundling, switching, and agentic workflow execution while optimizing resource utilization across the heterogeneous CPU-GPU-RDU pipeline.","intents":["Abstract away RDU hardware complexity and provide a unified inference interface","Manage model lifecycle (loading, unloading, switching) without manual memory management","Optimize resource allocation and scheduling across heterogeneous hardware components"],"best_for":["Teams building inference applications without deep hardware expertise","Builders requiring abstraction layers to focus on application logic rather than infrastructure","Organizations deploying multiple models with dynamic switching requirements"],"limitations":["SambaStack architecture, APIs, and interfaces not documented — no SDK or API reference available","No information on abstraction overhead or performance impact of stack layers","Model lifecycle management policies (preloading, caching, eviction) not specified","Memory allocation and scheduling algorithms not described","No documentation on debugging, profiling, or performance optimization within SambaStack","Integration with external orchestration frameworks (Kubernetes, Ray, etc.) not mentioned"],"requires":["SambaNova API access with SambaStack runtime","Model definitions compatible with SambaStack format (schema unknown)","Understanding of SambaStack APIs and lifecycle management (documentation not provided)"],"input_types":["model definitions and configurations","inference requests with routing hints","resource allocation policies and constraints"],"output_types":["inference results with execution metadata","resource utilization metrics and performance telemetry"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sambanova__headline","uri":"capability://deployment.infra.ai.inference.platform.with.custom.silicon","name":"ai inference platform with custom silicon","description":"SambaNova is an AI inference platform that leverages custom RDU chips to deliver high throughput and fast inference for open-source models like Llama, making it ideal for enterprise deployments.","intents":["best AI inference platform","AI inference platform for enterprise","high throughput AI model deployment","custom silicon for AI inference","fast inference solutions for AI models"],"best_for":["enterprise AI deployments","high-performance inference tasks"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["API key for SambaNova platform (format and authentication mechanism not documented)","Network connectivity to SambaNova inference endpoints or sovereign data center partners","Compliance with SambaNova acceptable use policy (not publicly available in provided documentation)","SambaNova API access with model bundling feature enabled (feature availability not documented)","Pre-configured model bundle definition (format and schema unknown)","Agentic orchestration layer to implement routing logic (not provided by SambaNova)","Enterprise contract with SambaNova (minimum commitment and terms not documented)","Verification of data residency requirements and regulatory compliance obligations","Network connectivity to specified sovereign data center region","Acceptance of SambaNova's data processing terms and DPA (Data Processing Agreement)"],"failure_modes":["Model availability limited to Llama and unspecified open-source models — no access to proprietary frontier models like GPT-4 or Claude","No documented latency metrics (p50, p95, p99) or time-to-first-token (TTFT) specifications available","Maximum context window and token limits not publicly specified","RDU hardware availability constrained to SambaNova-managed infrastructure; 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