Modelos de IA

Nuestra seleccion de los mejores modelos

Modelos de código abierto seleccionados para ejecutar en local. Cada uno con su caso de uso, la VRAM que necesita y cómo instalarlo.

Fuente: HuggingFace, Ollama. Actualizado abril 2026.

Agnósticos de proveedor: modelos de Anthropic, OpenAI, Google, Meta, Mistral, y comunidad open-source.

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Recomendaciones VORLUX

Probados en producción

Llama 4 Scout Meta · 109B (17B active MoE)

Largest open context window ever (10M tokens). MoE = only 17B params active, so inference is fast despite 109B total.

Llama 4 Maverick Meta · 400B (17B active, 128 experts)

128-expert MoE with only 17B active. Competes with GPT-4 class. Requires multi-GPU or cloud.

Gemma 3 4B Google · 4B

Smallest multimodal model available. Only 3GB at Q4. Fits Jetson Orin Nano. Perfect for vision tasks on minimal hardware.

53 modelos

← Haz clic en cualquier modelo para ver detalles

Llama 4 Scout

general

Meta · 109B (17B active MoE) · 10M ctx · Llama 4 Community

Largest open context window ever (10M tokens). MoE = only 17B params active, so inference is fast despite 109B total.

Massive context (10M tokens)Document analysisMulti-turn reasoning ⚡ 8 tok/s

Q4_K_M

65.4GB 24GB+

114.5GB 24GB+

FP16

215.6GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Mac Studio 128GB or DGX Spark 128GB (Q4); FP16 needs a multi-GPU server

Velocidad (M3 Pro 32GB)

Q4: 8 tok/s MoE: 17B active

Instalar con Ollama

ollama pull llama4:scout

meta-llama/Llama-4-Scout-17B-16E-Instruct

🤗 Modelo en HuggingFace Hardware

Llama 4 Maverick

general

Meta · 400B (17B active, 128 experts) · 1M ctx · Llama 4 Community

128-expert MoE with only 17B active. Competes with GPT-4 class. Requires multi-GPU or cloud.

Top-tier reasoningComplex analysisResearch ⚡ 3 tok/s

Q4_K_M

242.8GB 24GB+

425.8GB 24GB+

FP16

801.5GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Multi-GPU cluster (not practical for edge)

Velocidad (M3 Pro 32GB)

Q4: 3 tok/s Requires 200GB+

Instalar con Ollama

ollama pull llama4:maverick

meta-llama/Llama-4-Maverick-17B-128E-Instruct

🤗 Modelo en HuggingFace Hardware

Gemma 3 4B

vision

Google · 4B · 128K ctx · Gemma License

Smallest multimodal model available. Only 3GB at Q4. Fits Jetson Orin Nano. Perfect for vision tasks on minimal hardware.

Lightweight multimodalImage understandingEdge deployment128K context ⚡ 65 tok/s

Q4_K_M

2.49GB 8GB

4.13GB 8GB

FP16

7.77GB 8GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Jetson Orin Nano 8GB | Mac Mini M4 16GB

Velocidad (M3 Pro 32GB)

Q4: 65 tok/s FP16: 40 tok/s

Instalar con Ollama

ollama pull gemma3:4b

google/gemma-3-4b-it

🤗 Modelo en HuggingFace Hardware

Gemma 3 27B

vision

Google · 27B (also 1B/4B/12B) · 128K ctx · Gemma License

First multimodal open model that fits on a Mac Mini. 4B variant needs only 3GB. SigLIP vision encoder. 140+ languages.

Multimodal (text + images)Document scanningInvoice processing128K context window ⚡ 14 tok/s

Q4_K_M

16.6GB 24GB

28.7GB 24GB+

FP16

54.8GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

4B: any 8GB+ device | 27B: Mac Mini M4 Pro 32GB+

Velocidad (M3 Pro 32GB)

Q4: 14 tok/s FP16: 7 tok/s

Instalar con Ollama

ollama pull gemma3:27b

google/gemma-3-27b-it

🤗 Modelo en HuggingFace Hardware

Gemma 4 26B

general

Google · 26B (4B active MoE) · 128K ctx · Apache 2.0

Apache 2.0 license (fully open). Best quality-per-param in the 27B class. Runs on Mac Mini M4 24GB at Q4.

High-quality chatInstruction followingCreative writing ⚡ 14 tok/s

Q4_K_M

17GB 24GB

26.9GB 24GB+

FP16

50.5GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Mac Mini M4 24GB (Q4, short context) or a 24GB GPU (Q4)

Velocidad (M3 Pro 32GB)

Q4: 14 tok/s FP16: 7 tok/s

Instalar con Ollama

ollama pull gemma4:26b

google/gemma-4-26B-A4B-it

🤗 Modelo en HuggingFace Hardware

Gemma 4 E4B

general

Google · E4B (8B raw, edge) · 128K ctx · Apache 2.0

Tiny but punches above its weight. Runs on Jetson Orin Nano. Apache 2.0.

Fast inferenceEdge deploymentMobile/IoT ⚡ 58 tok/s

Q4_K_M

4.98GB 8GB

8.19GB 16GB

FP16

15.1GB 16GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Jetson Orin Nano 8GB (FP16) or any device

Velocidad (M3 Pro 32GB)

Q4: 58 tok/s FP16: 35 tok/s

Instalar con Ollama

ollama pull gemma4:e4b

google/gemma-4-E4B-it

🤗 Modelo en HuggingFace Hardware

Qwen 3 32B

general

Alibaba · 32B · 131.072K ctx · Apache 2.0

Thinking mode (deep reasoning) + non-thinking mode. Best multilingual open model. Apache 2.0.

Multilingual (29 languages)ReasoningTool use ⚡ 12 tok/s

Q4_K_M

19.8GB 24GB

34.8GB 24GB+

FP16

65.6GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Mac Mini M4 Pro 48GB (Q8) or RTX 4090 24GB (Q4)

Velocidad (M3 Pro 32GB)

Q4: 12 tok/s FP16: 6 tok/s

Instalar con Ollama

ollama pull qwen3:32b

Qwen/Qwen3-32B

🤗 Modelo en HuggingFace Hardware

Qwen 3 8B

general

Alibaba · 8B · 131.072K ctx · Apache 2.0

Sweet spot for edge: great quality at 8B, dual thinking modes, strong Spanish. Fits on 8GB devices.

General purposeSpanish languageFast reasoning ⚡ 45 tok/s

Q4_K_M

5.03GB 8GB

8.71GB 16GB

FP16

16.4GB 24GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Jetson Orin Nano 8GB (Q4) or Mac Mini M4 16GB (Q8)

Velocidad (M3 Pro 32GB)

Q4: 45 tok/s FP16: 22 tok/s

Instalar con Ollama

ollama pull qwen3:8b

Qwen/Qwen3-8B

🤗 Modelo en HuggingFace Hardware

Phi-4

general

Microsoft · 14B · 16.384K ctx · MIT

Best-in-class for STEM at 14B. MIT license. Fits 16GB devices at Q4.

STEM reasoningMathCode generation ⚡ 28 tok/s

Q4_K_M

9.05GB 16GB

15.6GB 16GB

FP16

29.3GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Mac Mini M4 16GB (Q4) or Mac Mini M4 24GB (Q8)

Velocidad (M3 Pro 32GB)

Q4: 28 tok/s FP16: 14 tok/s

Instalar con Ollama

ollama pull phi4

microsoft/phi-4

🤗 Modelo en HuggingFace Hardware

DeepSeek V3

general

DeepSeek · 671B (37B active MoE) · 128K ctx · DeepSeek License

Only 37B active params via MoE. Cloud-only for most users but API is very cheap ($0.27/1M input).

GPT-4 class reasoningComplex tasksResearch ⚡ 5 tok/s

Q4_K_M

404.4GB 24GB+

713.3GB 24GB+

FP16

1342GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Cloud API recommended ($0.27/$1.10 per 1M tokens)

Velocidad (M3 Pro 32GB)

Q4: 5 tok/s 671B MoE, needs multi-GPU

Instalar con Ollama

ollama pull deepseek-v3

deepseek-ai/DeepSeek-V3

🤗 Modelo en HuggingFace Hardware

DeepSeek R1

general

DeepSeek · 14B distill (full R1: 671B MoE) · 128K ctx · MIT

Best open-source reasoning model. 14B distill fits Mac Mini M4 16GB. Shows explicit thinking process. MIT license.

Chain-of-thought reasoningMath (97.3% MATH-500)Code debuggingLegal/financial analysis ⚡ 4 tok/s

Q4_K_M

8.99GB 16GB

15.7GB 16GB

FP16

29.6GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

14B: Mac Mini M4 16GB | 32B: 32GB+ unified memory

Velocidad (M3 Pro 32GB)

Q4: 4 tok/s 685B MoE, needs multi-GPU

Instalar con Ollama

ollama pull deepseek-r1:14b

deepseek-ai/DeepSeek-R1

🤗 Modelo en HuggingFace Hardware

Qwen3-Coder-Next

coding

Alibaba · 80B (3B active MoE) · 262.144K ctx · Apache 2.0

Only 3B active params: fast for its size. 256K context for huge codebases. >70% SWE-Bench. Apache 2.0.

Code generation (370 languages)SWE-Bench >70%Large repos ⚡ 5 tok/s

Q4_K_M

48.5GB 24GB+

84.8GB 24GB+

FP16

159.5GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Mac Studio 64GB+ or DGX Spark 128GB (Q4)

Velocidad (M3 Pro 32GB)

Q4: 5 tok/s Needs 48GB+

Instalar con Ollama

ollama pull qwen3-coder-next

Qwen/Qwen3-Coder-Next

🤗 Modelo en HuggingFace Hardware

Qwen2.5-Coder 32B

coding

Alibaba · 32B · 131.072K ctx · Apache 2.0

Best open coding model at 32B class. Strong at Python, JS, Go. Apache 2.0.

Code completionDebuggingRefactoring ⚡ 12 tok/s

Q4_K_M

19.9GB 24GB

34.8GB 24GB+

FP16

65.6GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Mac Mini M4 Pro 48GB (Q8) or RTX 4090 24GB (Q4)

Velocidad (M3 Pro 32GB)

Q4: 12 tok/s FP16: 6 tok/s

Instalar con Ollama

ollama pull qwen2.5-coder:32b

Qwen/Qwen2.5-Coder-32B-Instruct

🤗 Modelo en HuggingFace Hardware

Qwen2.5-Coder 7B

coding

Alibaba · 7B · 131.072K ctx · Apache 2.0

Best small coding model. Fits on any device. Fast enough for real-time autocomplete.

Fast code assistAutocompleteEdge coding ⚡ 48 tok/s

Q4_K_M

4.68GB 8GB

8.1GB 16GB

FP16

15.2GB 16GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Jetson Orin Nano 8GB (Q4) or any 16GB device (Q8)

Velocidad (M3 Pro 32GB)

Q4: 48 tok/s FP16: 27 tok/s

Instalar con Ollama

ollama pull qwen2.5-coder:7b

Qwen/Qwen2.5-Coder-7B-Instruct

🤗 Modelo en HuggingFace Hardware

DeepSeek-Coder V2

coding

DeepSeek · 236B (21B active MoE) · 128K ctx · DeepSeek License

MoE with 21B active. Near GPT-4 coding quality. Best via API ($0.14/$0.28 per 1M).

Complex code tasksMulti-file editsArchitecture ⚡ 10 tok/s

Q4_K_M

142.5GB 24GB+

250.4GB 24GB+

FP16

471.4GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Cloud API recommended

Velocidad (M3 Pro 32GB)

Q4: 10 tok/s 236B MoE

Instalar con Ollama

ollama pull deepseek-coder-v2

deepseek-ai/DeepSeek-Coder-V2-Instruct

🤗 Modelo en HuggingFace Hardware

StarCoder2 15B

coding

BigCode · 15B · 16.384K ctx · BigCode OpenRAIL-M

Trained on The Stack v2. Great for autocomplete. Supports 600+ programming languages.

Code completionFill-in-the-middle600+ languages ⚡ 22 tok/s

Q4_K_M

9.86GB 16GB

17GB 24GB

FP16

32GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Mac Mini M4 24GB (Q8) or a 12GB+ GPU (Q4)

Velocidad (M3 Pro 32GB)

Q4: 22 tok/s FP16: 11 tok/s

Instalar con Ollama

ollama pull starcoder2:15b

bigcode/starcoder2-15b

🤗 Modelo en HuggingFace Hardware

CodeLlama 34B

coding

Meta · 34B · 16.384K ctx · Llama 2 Community

Proven and stable. Strong at Python, C++, Java. Good for production use where stability matters.

Infill generationPython specialistLarge codebase nav ⚡ 10 tok/s

Q4_K_M

20.2GB 24GB

35.9GB 24GB+

FP16

67.4GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Mac Mini M4 Pro 48GB (Q8) or RTX 4090 24GB (Q4)

Velocidad (M3 Pro 32GB)

Q4: 10 tok/s

Instalar con Ollama

ollama pull codellama:34b

codellama/CodeLlama-34b-Instruct-hf

🤗 Modelo en HuggingFace Hardware

LLaVA 1.6 34B

vision

LLaVA Team · 34B · 4.096K ctx · Apache 2.0

Best open vision model for image understanding. Can describe images, answer questions about photos, read documents.

Image understandingVisual QADocument OCR ⚡ 10 tok/s

Q4_K_M

20.7GB 24GB

36.5GB 24GB+

FP16

69.6GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Mac Mini M4 Pro 48GB (Q8) or RTX 4090 (Q4)

Velocidad (M3 Pro 32GB)

Q4: 10 tok/s

Instalar con Ollama

ollama pull llava:34b

llava-hf/llava-v1.6-34b-hf

🤗 Modelo en HuggingFace Hardware

Gemma 3n E4B

vision

Google · 4B (edge-optimized) · 32.768K ctx · Apache 2.0

Edge-optimized multimodal. Runs on phones and Jetson. Audio, video, image, text: all in 4B params.

On-device visionAudio+Video+Image+TextMobile AI ⚡ 55 tok/s

Q4_K_M

4.54GB 8GB

7.35GB 8GB

FP16

13.7GB 16GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Jetson Orin Nano 8GB or phone

Velocidad (M3 Pro 32GB)

Q4: 55 tok/s FP16: 32 tok/s Nano variant

Instalar con Ollama

ollama pull gemma3n:e4b

google/gemma-3n-E4B-it

🤗 Modelo en HuggingFace Hardware

Qwen2.5-VL 72B

vision

Alibaba · 72B · 32.768K ctx · Apache 2.0

Understands video, multiple images, and documents. Agent-ready with tool use. Apache 2.0.

Video understandingMulti-image analysisDocument parsing ⚡ 5 tok/s

Q4_K_M

47.4GB 24GB+

78GB 24GB+

FP16

145.4GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Mac Studio 64GB+ or DGX Spark 128GB (Q4)

Velocidad (M3 Pro 32GB)

Q4: 5 tok/s Needs 48GB+

Instalar con Ollama

ollama pull qwen2.5-vl:72b

Qwen/Qwen2.5-VL-72B-Instruct

🤗 Modelo en HuggingFace Hardware

InternVL 2.5 78B

vision

OpenGVLab · 78B · 32.768K ctx · MIT

Top-tier on chart/diagram understanding benchmarks. MIT license. Strong OCR.

Chart/diagram understandingOCRVisual reasoning ⚡ 4 tok/s

Q4_K_M

47.5GB 24GB+

83.3GB 24GB+

FP16

156.8GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Cloud or multi-GPU

Velocidad (M3 Pro 32GB)

Q4: 4 tok/s Needs 48GB+

Instalar con Ollama

ollama pull internvl2.5:78b

OpenGVLab/InternVL2_5-78B

🤗 Modelo en HuggingFace Hardware

Florence 2 Large

vision

Microsoft · 0.77B · 4.096K ctx · MIT

Tiny (0.77B) but excellent at CV tasks. Runs on any device. MIT license. Perfect for edge vision.

Object detectionImage captioningOCRSegmentation

Q4_K_M

481MB 8GB

840MB 8GB

FP16

1.55GB 8GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Any device (even Raspberry Pi)

Velocidad (M3 Pro 32GB)

FP16: 40 tok/s Light vision model

Instalar con Ollama

ollama pull florence2

microsoft/Florence-2-large

🤗 Modelo en HuggingFace Hardware

Moondream 2

vision

Moondream · 1.8B · 2.048K ctx · Apache 2.0

Smallest practical vision model. Runs on Jetson, RPi, phones. Apache 2.0. Great for IoT vision.

Edge visionImage QALightweight multimodal

Q4_K_M

1.17GB 8GB

2.05GB 8GB

FP16

3.86GB 8GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Jetson Orin Nano, RPi 5, any device

Velocidad (M3 Pro 32GB)

FP16: 65 tok/s Ultra-light vision

Instalar con Ollama

ollama pull moondream

vikhyatk/moondream2

🤗 Modelo en HuggingFace Hardware

all-MiniLM-L6-v2

embedding

Sentence Transformers · 22M · 0.512K ctx · Apache 2.0

Industry standard for semantic search. 384 dimensions. Blazing fast. We use this in Vorlux AI RAG system.

Semantic searchRAG retrievalDocument similarity

Q4_K_M

31MB 8GB

51MB 8GB

FP16

92MB 8GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Any device (22M params = negligible VRAM)

Velocidad (M3 Pro 32GB)

FP16: 500 tok/s Embedding

Instalar con Ollama

ollama pull all-minilm:l6-v2

sentence-transformers/all-MiniLM-L6-v2

🤗 Modelo en HuggingFace Hardware

Nomic Embed Text v1.5

embedding

Nomic AI · 137M · 8.192K ctx · Apache 2.0

8K context (16x MiniLM). Better for long documents. 768 dimensions. Apache 2.0.

Long document embedding8K context chunksSearch

Q4_K_M

82MB 8GB

143MB 8GB

FP16

276MB 8GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Any device

Velocidad (M3 Pro 32GB)

FP16: 350 tok/s Embedding model

Instalar con Ollama

ollama pull nomic-embed-text

nomic-ai/nomic-embed-text-v1.5

🤗 Modelo en HuggingFace Hardware

BGE Large v1.5

embedding

BAAI · 335M · 0.512K ctx · MIT

Top MTEB benchmark scores. 1024 dimensions. MIT license. Best quality per compute for English.

High-quality retrievalRerankingEnglish focus

Q4_K_M

205MB 8GB

369MB 8GB

FP16

686MB 8GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Any device

Velocidad (M3 Pro 32GB)

FP16: 320 tok/s Embedding

Instalar con Ollama

ollama pull bge-large

BAAI/bge-large-en-v1.5

🤗 Modelo en HuggingFace Hardware

GTE Base

embedding

Alibaba DAMO · 109M · 0.512K ctx · MIT

Great balance of speed and quality. 768 dimensions. MIT license. Good for production RAG.

Balanced speed/qualityMultilingual embeddingProduction RAG

Q4_K_M

72MB 8GB

123MB 8GB

FP16

225MB 8GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Any device

Velocidad (M3 Pro 32GB)

FP16: 400 tok/s Embedding

Instalar con Ollama

ollama pull gte-base

thenlper/gte-base

🤗 Modelo en HuggingFace Hardware

Arctic Embed L v2

embedding

Snowflake · 568M · 8.192K ctx · Apache 2.0

8K context, 1024 dimensions, strong multilingual. Top MTEB scores. Apache 2.0.

Enterprise searchLong-context retrievalMultilingual

Q4_K_M

338MB 8GB

594MB 8GB

FP16

1.1GB 8GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Any device

Velocidad (M3 Pro 32GB)

FP16: 300 tok/s Embedding

Instalar con Ollama

ollama pull snowflake-arctic-embed:l

Snowflake/snowflake-arctic-embed-l-v2.0

🤗 Modelo en HuggingFace Hardware

FLUX.1 Dev

image gen

Black Forest Labs · 12B · — ctx · FLUX.1-dev Non-Commercial

Best open image model. Exceptional text rendering and prompt adherence. NVFP4 variant runs on 8GB GPUs.

High-quality image generationText renderingPrompt following

Q4_K_M

7.27GB 8GB

12.8GB 16GB

FP16

24GB 24GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

RTX 4060 Ti 16GB (FP8) or RTX 4090 (FP16)

Velocidad (M3 Pro 32GB)

~2 images/min on RTX 3080

Usar con ComfyUI → Ver workflows

black-forest-labs/FLUX.1-dev

🤗 Modelo en HuggingFace Hardware

SDXL 1.0

image gen

Stability AI · 6.6B (2.6B UNet) · — ctx · Stability AI Community

Massive LoRA/ControlNet ecosystem. Fast on consumer GPUs. Still the most flexible image model.

Fast image genLoRA ecosystemControlNet

Q4_K_M

1.97GB 8GB

3.45GB 8GB

FP16

6.5GB 8GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

RTX 4060 Ti (FP16) or any 8GB+ GPU

Velocidad (M3 Pro 32GB)

~6 images/min on RTX 3080

Usar con ComfyUI → Ver workflows

stabilityai/stable-diffusion-xl-base-1.0

🤗 Modelo en HuggingFace Hardware

SDXL Turbo

image gen

Stability AI · 6.6B · — ctx · Stability AI Community

Single-step generation: near real-time images. Great for interactive apps and demos.

Real-time generation1-step inferenceInteractive

Q4_K_M

1.97GB 8GB

3.45GB 8GB

FP16

6.5GB 8GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Any 8GB+ GPU

Velocidad (M3 Pro 32GB)

~15 images/min (RTX 3080), ~3/min (M3 Pro)

Usar con ComfyUI → Ver workflows

stabilityai/sdxl-turbo

🤗 Modelo en HuggingFace Hardware

SD 3.5 Large

image gen

Stability AI · 8B · — ctx · Stability AI Community

MMDiT architecture. Better prompt understanding than SDXL. Good text rendering.

PhotorealismComplex compositionsHigh resolution

Q4_K_M

4.85GB 8GB

8.5GB 16GB

FP16

16GB 16GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

RTX 4060 Ti 16GB or Mac Mini M4 24GB

Velocidad (M3 Pro 32GB)

~4 images/min (RTX 3080), ~1/min (M3 Pro)

Usar con ComfyUI → Ver workflows

stabilityai/stable-diffusion-3.5-large

🤗 Modelo en HuggingFace Hardware

Stable Cascade

image gen

Stability AI · 5.1B (3-stage) · — ctx · Stability AI Non-Commercial

3-stage pipeline is more VRAM efficient than SDXL. Good quality at lower compute cost.

Efficient generationLower VRAMFast training

Q4_K_M

3.03GB 8GB

5.31GB 8GB

FP16

10GB 16GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Any 6GB+ GPU

Velocidad (M3 Pro 32GB)

~3 images/min (RTX 3080)

Usar con ComfyUI → Ver workflows

stabilityai/stable-cascade

🤗 Modelo en HuggingFace Hardware

FLUX.1 Schnell

image gen

Black Forest Labs · 12B · — ctx · Apache 2.0

Apache 2.0 licensed FLUX variant. 4-step generation (vs 50 for dev). Commercial use OK.

Fast FLUX generation4-step inferenceCommercial use

Q4_K_M

7.27GB 8GB

12.8GB 16GB

FP16

24GB 24GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

RTX 4060 Ti 16GB (FP8) or RTX 4090

Velocidad (M3 Pro 32GB)

~8 images/min (RTX 3080), ~2/min (M3 Pro)

Usar con ComfyUI → Ver workflows

black-forest-labs/FLUX.1-schnell

🤗 Modelo en HuggingFace Hardware

LTX Video

video gen

Lightricks · 2B · — ctx · Apache 2.0

Fastest open video model. Generates 5-second clips from text or images. NVFP4 optimized. Apache 2.0.

Text-to-videoImage-to-videoFast generation

Q4_K_M

1.21GB 8GB

2.12GB 8GB

FP16

4GB 8GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

RTX 4060 Ti (FP16) or any 8GB+ GPU

Velocidad (M3 Pro 32GB)

~30s per 4s clip (RTX 3080)

Usar con ComfyUI → Ver workflows

Lightricks/LTX-Video

🤗 Modelo en HuggingFace Hardware

Hunyuan Video

video gen

Tencent · 13B · — ctx · Tencent Hunyuan Community

Best open video quality. Longer clips than LTX. Strong motion consistency. Needs more VRAM.

High-quality videoMotion consistencyLong clips

Q4_K_M

7.88GB 8GB

13.8GB 16GB

FP16

26GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

RTX 4090 24GB or A100

Velocidad (M3 Pro 32GB)

~60s per 4s clip (RTX 3080)

Usar con ComfyUI → Ver workflows

tencent/HunyuanVideo

🤗 Modelo en HuggingFace Hardware

Cosmos 1.0 Diffusion 7B

video gen

NVIDIA · 7B · — ctx · NVIDIA Open Model License

NVIDIA's world generation model. Understands physics. Great for game dev and simulation.

World generationPhysical simulationGame content

Q4_K_M

4.24GB 8GB

7.44GB 8GB

FP16

14GB 16GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

RTX 4060 Ti 16GB or RTX 4090

Velocidad (M3 Pro 32GB)

~45s per 4s clip (RTX 3080)

Usar con ComfyUI → Ver workflows

nvidia/Cosmos-1.0-Diffusion-7B-Text2World

🤗 Modelo en HuggingFace Hardware

WAN 2.1 T2V 14B

video gen

WAN AI · 14B · — ctx · Apache 2.0

Strong Chinese-English bilingual video generation. Apache 2.0. Good motion and detail.

Text-to-videoChinese + EnglishHigh resolution

Q4_K_M

8.49GB 16GB

14.9GB 16GB

FP16

28GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

RTX 4090 or A100

Velocidad (M3 Pro 32GB)

~90s per 4s clip (RTX 3080)

Usar con ComfyUI → Ver workflows

Wan-AI/Wan2.1-T2V-14B

🤗 Modelo en HuggingFace Hardware

Mochi 1

video gen

Genmo · 10B · — ctx · Apache 2.0

Exceptional motion quality. Apache 2.0. Natural human movement and physics.

High-fidelity motionCinematic qualityNatural movement

Q4_K_M

6.06GB 8GB

10.6GB 16GB

FP16

20GB 24GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

RTX 4090 or cloud

Velocidad (M3 Pro 32GB)

~120s per 4s clip (A100)

Usar con ComfyUI → Ver workflows

genmo/mochi-1-preview

🤗 Modelo en HuggingFace Hardware

Whisper Large v3 Turbo

audio

OpenAI · 809M · 0.03K ctx · MIT

Industry standard for transcription. 99 languages. MIT license. Runs on any device.

Speech-to-textTranscription99 languages

Q4_K_M

492MB 8GB

870MB 8GB

FP16

1.6GB 8GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Any device (809M params)

Velocidad (M3 Pro 32GB)

~30x real-time (M3 Pro), ~50x (RTX 3080)

Instalar con Ollama

ollama pull whisper-large-v3-turbo

openai/whisper-large-v3-turbo

🤗 Modelo en HuggingFace Hardware

Bark

audio

Suno · 1.3B · — ctx · MIT

Generates realistic speech, music, and sound effects from text. MIT license. Multilingual.

Text-to-speechVoice cloningSound effects

Q4_K_M

809MB 8GB

1.38GB 8GB

FP16

2.6GB 8GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Any device with 4GB+ RAM

Velocidad (M3 Pro 32GB)

~0.5x real-time (RTX 3080): slow TTS

Instalar con Ollama

ollama pull bark

suno/bark

🤗 Modelo en HuggingFace Hardware

MusicGen Large

audio

Meta · 3.3B · — ctx · CC BY-NC 4.0

Generate music from text descriptions. Great for content creators. Multiple styles.

Music generationBackground musicAudio branding

Q4_K_M

2GB 8GB

3.51GB 8GB

FP16

6.6GB 8GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Any device with 8GB+ RAM

Velocidad (M3 Pro 32GB)

~2x real-time (RTX 3080): music generation

Instalar con Ollama

ollama pull musicgen-large

facebook/musicgen-large

🤗 Modelo en HuggingFace Hardware

XTTS v2

audio

Coqui · 0.5B · — ctx · Coqui Public License

Clone any voice from 6-second sample. 17 languages including Spanish. Real-time on CPU.

Voice cloning17 languagesReal-time TTS

Q4_K_M

307MB 8GB

543MB 8GB

FP16

1GB 8GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Any device (runs on CPU)

Velocidad (M3 Pro 32GB)

~3x real-time (RTX 3080): voice cloning TTS

Instalar con Ollama

ollama pull xtts-v2

coqui/XTTS-v2

🤗 Modelo en HuggingFace Hardware

Parler TTS Large

audio

Parler TTS · 2.3B · — ctx · Apache 2.0

Control voice via text description ('A warm female voice, clear, podcast style'). Apache 2.0.

Controllable TTSStyle descriptionPodcast generation

Q4_K_M

1.39GB 8GB

2.44GB 8GB

FP16

4.6GB 8GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Any device with 4GB+ RAM

Velocidad (M3 Pro 32GB)

~5x real-time (RTX 3080): natural TTS

Instalar con Ollama

ollama pull parler-tts

parler-tts/parler-tts-large-v1

🤗 Modelo en HuggingFace Hardware

BioMistral 7B

specialized

BioMistral · 7B · 32.768K ctx · Apache 2.0

Fine-tuned on PubMed. Medical QA, clinical text analysis. Apache 2.0. EU AI Act: high-risk category.

Medical QAClinical NLPBiomedical research ⚡ 42 tok/s

Q4_K_M

4.37GB 8GB

7.7GB 8GB

FP16

14.5GB 16GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Jetson Orin Nano 8GB (Q4) or Mac Mini M4

Velocidad (M3 Pro 32GB)

Q4: 42 tok/s Medical domain fine-tune

Instalar con Ollama

ollama pull biomistral

BioMistral/BioMistral-7B

🤗 Modelo en HuggingFace Hardware

SaulLM 7B

specialized

Equall · 7B · 8.192K ctx · MIT

Trained on legal corpora. Understanding of legal concepts, statutes, case law. MIT license.

Legal analysisContract reviewCase law research ⚡ 42 tok/s

Q4_K_M

4.37GB 8GB

7.7GB 8GB

FP16

14.5GB 16GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Jetson Orin Nano 8GB (Q4) or Mac Mini M4

Velocidad (M3 Pro 32GB)

Q4: 42 tok/s Legal domain fine-tune

Instalar con Ollama

ollama pull saullm

Equall/Saul-7B-Instruct-v1

🤗 Modelo en HuggingFace Hardware

DeepSeek-Math 7B

specialized

DeepSeek · 7B · 4.096K ctx · DeepSeek License

State-of-the-art math reasoning at 7B. Solves complex problems step by step.

Mathematical reasoningTheorem provingCalculations ⚡ 45 tok/s

Q4_K_M

4.18GB 8GB

7.33GB 8GB

FP16

13.8GB 16GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Jetson Orin Nano 8GB (Q4) or any device

Velocidad (M3 Pro 32GB)

Q4: 45 tok/s Math specialized

Instalar con Ollama

ollama pull deepseek-math

deepseek-ai/deepseek-math-7b-instruct

🤗 Modelo en HuggingFace Hardware

Mistral 7B v0.3

specialized

Mistral AI · 7B · 32.768K ctx · Apache 2.0

Best 7B for structured tasks: function calling, JSON output, tool use. Apache 2.0. Fast and reliable.

Function callingStructured outputRAG ⚡ 44 tok/s

Q4_K_M

4.37GB 8GB

7.7GB 8GB

FP16

14.5GB 16GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Jetson Orin Nano 8GB (Q4) or any device

Velocidad (M3 Pro 32GB)

Q4: 44 tok/s FP16: 22 tok/s

Instalar con Ollama

ollama pull mistral:7b-instruct-v0.3

mistralai/Mistral-7B-Instruct-v0.3

🤗 Modelo en HuggingFace Hardware

Hermes 3 8B

specialized

Nous Research · 8B · 131.072K ctx · Llama 3.1 Community

Best community fine-tune for agentic use. Excellent at following complex system prompts and using tools.

Agentic tasksTool useSystem prompts ⚡ 42 tok/s

Q4_K_M

4.92GB 8GB

8.54GB 16GB

FP16

16.1GB 24GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Jetson Orin Nano 8GB (Q4) or Mac Mini M4

Velocidad (M3 Pro 32GB)

Q4: 42 tok/s FP16: 20 tok/s

Instalar con Ollama

ollama pull hermes3:8b

NousResearch/Hermes-3-Llama-3.1-8B

🤗 Modelo en HuggingFace Hardware

Finance-LLM 13B

specialized

TheBloke (quantized) · 13B · 4.096K ctx · Llama 2 Community

Fine-tuned on financial data. Understands financial terminology, ratios, and market concepts.

Financial analysisReport generationMarket research ⚡ 22 tok/s

Q4_K_M

7.87GB 8GB

13.8GB 16GB

FP16

26GB 24GB+

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Mac Mini M4 24GB (Q8) or RTX 4060 Ti (Q4)

Velocidad (M3 Pro 32GB)

Q4: 22 tok/s Financial domain

Instalar con Ollama

ollama pull finance-llm:13b

TheBloke/finance-LLM-13B-GGUF

🤗 Modelo en HuggingFace Hardware

Dolphin 2.9 8B

specialized

Cognitive Computations · 8B · 8.192K ctx · Llama 3 Community

Uncensored fine-tune, no alignment filtering. Useful for creative tasks, red-teaming, research.

Uncensored assistantCreative writingRoleplay ⚡ 42 tok/s

Q4_K_M

4.92GB 8GB

8.54GB 16GB

FP16

16.1GB 24GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Jetson Orin Nano 8GB (Q4) or any device

Velocidad (M3 Pro 32GB)

Q4: 42 tok/s FP16: 20 tok/s

Instalar con Ollama

ollama pull dolphin-llama3:8b

dphn/dolphin-2.9-llama3-8b

🤗 Modelo en HuggingFace Hardware

Llama Guard 3

specialized

Meta · 8B · 131.072K ctx · Llama 3.1 Community

Meta's safety classifier. Run alongside any model to filter harmful content. Essential for production deployments.

Content moderationSafety classificationGuardrails ⚡ 40 tok/s

Q4_K_M

4.92GB 8GB

8.54GB 16GB

FP16

16.1GB 24GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Jetson Orin Nano 8GB (Q4) or any device

Velocidad (M3 Pro 32GB)

Q4: 40 tok/s Safety classifier

Instalar con Ollama

ollama pull llama-guard3:8b

meta-llama/Llama-Guard-3-8B

🤗 Modelo en HuggingFace Hardware

OCRonos Vintage

specialized

PleIAs · 124M · 8.192K ctx · Apache 2.0

Specialized in correcting OCR errors from historical documents. Perfect for digitization projects. Apache 2.0.

Historical OCR correctionDocument digitizationArchive processing

Q4_K_M

82MB 8GB

133MB 8GB

FP16

256MB 8GB

08GB16GB24GB

Detalles del modelo

Hardware recomendado

Any device (124M parameters)

Velocidad (M3 Pro 32GB)

~5 pages/min (M3 Pro): OCR model

Instalar con Ollama

ollama pull ocronos

PleIAs/OCRonos-Vintage

🤗 Modelo en HuggingFace Hardware

☁

Modelos Cloud / API

24 modelos evaluados con benchmarks Artificial Analysis

Estos modelos se acceden via API. Comparados por Intelligence Index (II), MMLU-Pro y GPQA. Los precios son $/1M tokens.

GPT-4o

OpenAI

II 86

MMLU-Pro 88.7% GPQA 53.6% 142 tok/s

In: $5.00 Out: $15.00

2024-05-13

GPT-4o-mini

OpenAI

II 82

MMLU-Pro 86.2% GPQA 48.6% 215 tok/s

In: $0.15 Out: $0.60

2024-07-18

o1-preview

OpenAI

II 91

MMLU-Pro 87.7% GPQA 75% 38 tok/s

In: $15.00 Out: $60.00

2024-09-12

o1-mini

OpenAI

II 87

MMLU-Pro 85.2% GPQA 64.4% 95 tok/s

In: $3.00 Out: $12.00

2024-09-12

Claude 3.5 Sonnet

Anthropic

II 88

MMLU-Pro 88.3% GPQA 55.2% 118 tok/s

In: $3.00 Out: $15.00

2024-06-20

Claude 3.5 Sonnet v2

Anthropic

II 89

MMLU-Pro 89.1% GPQA 57.8% 125 tok/s

In: $3.00 Out: $15.00

2024-10-22

Claude 3.5 Opus

Anthropic

II 92

MMLU-Pro 90.8% GPQA 62.1% 48 tok/s

In: $15.00 Out: $75.00

2024-07-22

Claude 3 Haiku

Anthropic

II 75

MMLU-Pro 77.2% GPQA 38.5% 280 tok/s

In: $0.25 Out: $1.25

2024-03-07

Gemini 1.5 Pro

Google

II 87

MMLU-Pro 88.6% GPQA 54.8% 98 tok/s

In: $3.50 Out: $10.50

2024-05-14

Gemini 1.5 Flash

Google

II 83

MMLU-Pro 85.3% GPQA 46.2% 245 tok/s

In: $0.07 Out: $0.30

2024-05-14

Gemini 2.0 Flash

Google

II 85

MMLU-Pro 86.7% GPQA 49.8% 310 tok/s

In: $0.00 Out: $0.00

2024-12-11

Gemini 2.0 Flash Thinking

Google

II 88

MMLU-Pro 88.4% GPQA 55.6% 78 tok/s

In: $0.00 Out: $0.00

2024-12-19

Llama 3.1 405B

Meta

II 84

MMLU-Pro 86.1% GPQA 46.8% 42 tok/s

In: $3.50 Out: $14.00

2024-07-23

Llama 3.1 70B

Meta

II 82

MMLU-Pro 84.2% GPQA 43.5% 88 tok/s

In: $0.65 Out: $2.75

2024-07-23

Llama 3.1 8B

Meta

II 77

MMLU-Pro 79.4% GPQA 36.2% 195 tok/s

In: $0.20 Out: $0.80

2024-07-23

Llama 3.2 1B

Meta

II 72

MMLU-Pro 74.8% GPQA 31.5% 380 tok/s

In: $0.10 Out: $0.40

2024-09-25

Llama 3.2 3B

Meta

II 76

MMLU-Pro 77.9% GPQA 35.8% 320 tok/s

In: $0.10 Out: $0.40

2024-09-25

Mistral Large

Mistral AI

II 85

MMLU-Pro 86.9% GPQA 48.2% 72 tok/s

In: $2.00 Out: $6.00

2024-02-29

Mistral Nemo

Mistral AI

II 78

MMLU-Pro 79.8% GPQA 38.4% 185 tok/s

In: $0.15 Out: $0.15

2024-07-22

Mixtral 8x7B

Mistral AI

II 79

MMLU-Pro 80.6% GPQA 39.2% 95 tok/s

In: $0.70 Out: $2.40

2023-12-11

Mistral Small

Mistral AI

II 80

MMLU-Pro 82.1% GPQA 41.3% 155 tok/s

In: $0.20 Out: $0.60

2024-09-17

Qwen 2.5 72B

Qwen

II 83

MMLU-Pro 85.4% GPQA 44.8% 82 tok/s

In: $0.90 Out: $3.60

2024-09-19

Qwen 2.5 32B

Qwen

II 80

MMLU-Pro 82.8% GPQA 40.5% 145 tok/s

In: $0.50 Out: $2.00

2024-09-19

Qwen 2.5 7B

Qwen

II 76

MMLU-Pro 78.6% GPQA 35.2% 210 tok/s

In: $0.20 Out: $0.80

2024-09-19

QwQ 32B

Qwen

II 84

MMLU-Pro 86.2% GPQA 47.8% 78 tok/s

In: $0.50 Out: $2.00

2024-11-28

Explorador de VRAM Interactivo

Selecciona un modelo y una cuantización para ver que hardware lo soporta.

Calcula la memoria que necesita un modelo

Elige un modelo, una cuantización y un contexto. Las barras dicen en qué equipos cabe.

Categoría

Tengo este hardware

Modelo

Cuantización

Contexto (tokens que el modelo recuerda)

Modelos populares

Memoria total–

Parámetros–

Contexto máximo–

Licencia–

Para qué sirve–

Memoria necesaria estimada

–pesos + –caché de contexto + 1,5 GBmotor = –total

Pesos = parámetros × bits por peso ÷ 8 (Q4_0 4,5 · Q4_K_M 4,85 · Q8_0 8,5 · FP16 16). Caché = MB por token × tokens de contexto. Motor = 1,5 GB.

Medido con ollama ps en nuestra GB10, el uso real queda entre 1,4 y 1,6 GB por debajo de esta cuenta: preferimos que te sobre memoria a que te falte. A la máquina le restamos además la parte que se queda el sistema.

Velocidad estimada: –

En qué equipos cabe

Jetson Orin Nano Super 8 GB · 230 €
RPi 5 + AI HAT+ 2 16 GB · 200 €
Beelink SER8 32 GB · 550 €
ASUS NUC 14 Pro 64 GB · 650 €
Minisforum UM890 Pro 96 GB · 750 €
Mac Mini M4 (24GB) 24 GB · 920 €
Mac Mini M4 Pro (48GB) 48 GB · 1.840 €
Mac Studio M4 Max (48GB) 48 GB · 2.300 €
Jetson AGX Orin 64GB 64 GB · 1.840 €
RTX 4060 Ti 16GB Build 16 GB · 1.000 €
RTX 4090 24GB Build 24 GB · 2.500 €
RTX 3090 24GB (Used) Build 24 GB · 1.200 €
AMD W7900 48GB Build 48 GB · 4.000 €
Jetson AGX Thor (128GB) 128 GB · 3.499 €
Jetson T4000 (64GB) 64 GB · 1.999 €
Cloud A100 80GB (RunPod) 80 GB · alquiler por horas
Cloud H100 80GB (RunPod) 80 GB · alquiler por horas
MacBook Pro M4 Pro (24GB) 24 GB · 2.199 €
Lenovo ThinkPad P16s Gen 3 (RTX A500) 32 GB · 1.650 €
Framework 16 (Radeon RX 7700S) 32 GB · 1.900 €
Supermicro 1U Server (L40S 48GB) 48 GB · 12.000 €
Dell PowerEdge R760xa (2x RTX A6000) 96 GB · 18.500 €

Cabe con margen Justo (más del 85 % de lo utilizable) No cabe

Consejo:

¿Lo montamos en tu equipo?

Reservar llamada de 15 min Elegir hardware Calcular el coste

Recibe este análisis por correo

Siguiente paso: Selector de hardware Calculadora ROI Subvenciones

Herramienta

Comparador de Modelos IA

Elige 2-3 modelos para compararlos lado a lado.

Proveedores Cloud

Cuando necesitas la nube

Para tareas que requieren modelos mas grandes de lo que cabe en hardware local, estos proveedores ofrecen acceso API con precios competitivos.

OpenAI

$5 gratis

Highest quality reasoning, multimodal, image generation

GPT-4oGPT-4o-minio1 +3

Ver precios →

Anthropic

$5 gratis

Best coding model, longest context (200K), safest

Claude Opus 4Claude Sonnet 4Claude Haiku 3.5

Ver precios →

Google

Free tier (15 RPM) gratis

Cheapest flash model, 1M+ context, multimodal

Gemini 2.0 FlashGemini 1.5 ProImagen 3 +1

Ver precios →

Mistral AI

€5 gratis

EU-based provider, strong coding, function calling

Mistral LargeMistral SmallCodestral +1

Ver precios →

Together AI

$5 gratis

Run any open model via API, cheapest open model hosting

Llama 4 ScoutQwen 3FLUX.1 +1

Ver precios →

Groq

Free tier (30 RPM) gratis

Fastest inference (LPU hardware), free tier, low latency

Llama 4 ScoutGemma 4Mistral (ultra-fast LPU inference)

Ver precios →

Replicate

Free predictions gratis

GPU rental per-second, image/video models, easy deployment

FLUX.1Stable DiffusionWhisper +1

Ver precios →

Modelo	Proveedor	Input $/1M	Output $/1M	Contexto
GPT-5	OpenAI	$1.25	$10.00	400K
GPT-5 Mini	OpenAI	$0.25	$2.00	400K
Claude Sonnet 5	Anthropic	$2.00	$10.00	200K
Claude Haiku 4.5	Anthropic	$1.00	$5.00	200K
Gemini 1.5 Pro	Google	$1.25	$5.00	2000K
Gemini 1.5 Flash	Google	$0.07	$0.30	1000K
Gemini 2.0 Flash	Google	$0.10	$0.40	1000K
Mistral Large	Mistral	$2.00	$6.00	128K
Mistral Small	Mistral	$0.10	$0.30	128K
Llama 3.1 70B	Meta/Together	$0.88	$0.88	128K
Llama 3.1 8B	Meta/Together	$0.18	$0.18	128K
DeepSeek V3	DeepSeek	$0.27	$1.10	128K

¿Cloud o Local?

Cloud API

+ Acceso a los modelos mas grandes
+ Sin hardware que mantener
+ Escala instantanea
- Los datos salen de tu red
- Coste mensual escalable
- Dependencia del proveedor

IA Local (Edge)

+ Los datos no salen de tu red
+ Coste fijo (hardware una vez)
+ GDPR by design
+ Sin latencia de red
~ Modelos mas pequenos
~ Inversion inicial

Consultar despliegue local →

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