compacto 1B multimodal embedding modelo para joint texto y imagen vector búsqueda, RAG recuperación, y cross-modal clustering.
Technical Specifications
- Provider
- NVIDIA
- Category
- lightweight
- Release Date
- 2026-02
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Fixed
- Structured Outputs
- No
- Output Modalities
- embedding
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- llama-nemotron-embed-vl-1b-v2
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2028+)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($0.02/1M in) across full 32K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.005/1M tokens
- Computer Use & Browser GUI Agent
- Text-only completion (No GUI action loop)
- MCP & Parallel Tool Calling
- Prompt-based JSON extraction only
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Llama Nemotron Embed VL 1B v2 Cost, Context, Hardware & API Integration
Pay-as-you-go API is cheaper for this volume (saves $18.95/mo vs $20 flat plan).
Break-even point: ~9,524 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Llama Nemotron Embed VL 1B v2:
Benchmark Scores & 6-Axis Capability Radar
- Chatbot Arena Elo
- 1210pts
Strengths
- Joint multimodal embedding space uniting texto queries con diagram y documento imágenes
- ultra-rápido vector encoding velocidad exceeding 280 tokens/sec
- alto MTEB benchmark ranking para recuperación y cross-modal búsqueda
Limitations & Trade-offs
- Outputs vector embeddings solo, no conversacional generative texto
- 32K ventana de contexto limitado en comparación con larger documento modelos
Editorial Assessment
un ultra-compacto, alto-velocidad multimodal embedding estándar para next-generación RAG.
Best for: empresarial vector databases, multimodal RAG recuperación, y semantic imagen búsqueda
Compare Llama Nemotron Embed VL 1B v2
Frequently Asked Questions about Llama Nemotron Embed VL 1B v2
How much does the Llama Nemotron Embed VL 1B v2 API cost per 1M tokens?+
Llama Nemotron Embed VL 1B v2 by NVIDIA is priced at $0.02 per 1M input tokens and $0.02 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.005 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Llama Nemotron Embed VL 1B v2 pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Llama Nemotron Embed VL 1B v2's context window?+
Llama Nemotron Embed VL 1B v2 features a 32K tokens context window, which equals approximately 24,000 English words or ~64 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 2,048 tokens.
Can I run Llama Nemotron Embed VL 1B v2 locally on my own GPU (Open Weights)?+
Yes, Llama Nemotron Embed VL 1B v2 (1B) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does NVIDIA train on data sent to the Llama Nemotron Embed VL 1B v2 API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Open-Weights Research & Commercial License. Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does Llama Nemotron Embed VL 1B v2 perform on coding and reasoning benchmarks?+
Llama Nemotron Embed VL 1B v2 achieves a composite Intelligence Index of 32/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, N/A% on MMLU-Pro, and 1210 pts on Chatbot Arena Elo.
What is Llama Nemotron Embed VL 1B v2 best used for?+
Llama Nemotron Embed VL 1B v2 is best suited for: empresarial vector databases, multimodal RAG recuperación, y semantic imagen búsqueda. Assessment: un ultra-compacto, alto-velocidad multimodal embedding estándar para next-generación RAG.
NVIDIA Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to NVIDIA's flagship reasoning tier:
Nemotron 3 Ultra 550B A55B
Nemotron 3.5 Content Safety
Nemotron 3 Nano Omni 30B A3B Reasoning
Nemotron 3 Content Safety
Nemotron 3 Super 120B A12B
Nemotron VoiceChat
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Llama Nemotron Embed VL 1B v2:
La unidad básica de texto que procesa un modelo de lenguaje, que normalmente equivale a 3 o 4 caracteres o aproximadamente 0,75 palabras. Por ejemplo, una frase típica se divide en varios tokens.
Un conjunto estandarizado de pruebas para evaluar y comparar objetivamente las capacidades de distintos modelos de IA en tareas como programación (SWE-bench) o matemáticas avanzadas (AIME).
La cantidad máxima de tokens que un modelo puede considerar a la vez, incluyendo tanto la entrada que lee como la salida que genera. Una ventana de 1.000.000 de tokens puede procesar unas 750.000 palabras en una sola petición.
Una optimización donde los proveedores almacenan estados de atención previos para prefijos de prompts frecuentes, reduciendo el coste de entrada hasta en un 90% y acelerando la respuesta.
Una optimización de memoria GPU que almacena matrices de atención previas (Key y Value) para evitar recalcular tokens ya procesados, acelerando la generación de respuestas en streaming.
Una red neuronal multimodal diseñada para interpretar, razonar y generar contenido combinando simultáneamente imágenes, diagramas, capturas de pantalla y texto en lenguaje natural.
Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.