Llama Nemotron Embed VL 1B v2 di NVIDIA è un modello di intelligenza artificiale con finestra di contesto da 32K token e tariffazione API di $0.02/1M token in input e $0.02/1M in output, ideale per coding, ragionamento e automazione aziendale.
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
- Architettura Dense Multimodal Embedding Transformer avanzata di NVIDIA con supporto fino a 32K token di contesto
- Ottimo rapporto qualità-prezzo ($0.02/1M input, $0.02/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Limitations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Editorial Assessment
Llama Nemotron Embed VL 1B v2 di NVIDIA offre una combinazione eccellente di precisione, velocità ed efficienza dei costi API.
Best for: Automazione enterprise, sviluppo software, agenti IA autonomi e pipeline di produzione NVIDIA
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: Automazione enterprise, sviluppo software, agenti IA autonomi e pipeline di produzione NVIDIA. Assessment: Llama Nemotron Embed VL 1B v2 di NVIDIA offre una combinazione eccellente di precisione, velocità ed efficienza dei costi API.
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:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
A cost and latency optimization where inference providers store pre-computed attention states for repeated prompt prefixes. Reusing cached tokens typically slashes API input costs by 50% to 90% and cuts TTFT substantially.
A GPU memory optimization that stores previously calculated Key and Value attention matrices so the model does not recompute attention for existing tokens at each generation step. KV cache is vital for fast streaming generation and prompt caching.
A multimodal neural network engineered to jointly process, reason over, and synthesize both visual content (images, diagrams, UI screenshots, video) and natural language text within a single framework.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Fonti ufficiali: NVIDIA NeMo Retriever Documentation (Feb 2026)↗