Independent deployment field guide
Compare current models for language, coding, vision, OCR, embeddings, speech, image, video, 3D and safety. See the minimum GPU memory, licence and compatibility across all eleven GPU Servers products.
Start with the decision
A useful choice depends on task quality, licence, runtime maturity, context, concurrency and deployment cost. Parameter count is not a substitute for testing the work your organisation actually performs.
A best open-source AI model for business shortlist may include both open-source and source-available open-weight releases because buyers encounter both. Each page states the applicable licence rather than treating “open” as one legal category.
Browse by task
One model can appear in more than one category when it handles several types of work.
33 current models
Showing 33 models
Moonshot AI
A 2.8-trillion-parameter, 104-billion-active multimodal mixture-of-experts model for long-context reasoning, coding, visual understanding and agentic work.
Qwen
A 36-billion-parameter multimodal mixture-of-experts model with about 3 billion active parameters, a 262,144-token default context and a strong emphasis on agentic coding.
DeepSeek
A 284B-total, 13B-active mixture-of-experts language model with one-million-token context and a mixed FP4/FP8 release format.
Mistral AI
A 119B-total, 6B-active hybrid model that combines instruction following, reasoning, coding-agent and multimodal capabilities.
Meta
Meta's 109B-total, 17B-active multimodal mixture-of-experts checkpoint with text and image input and a very long documented context.
OpenAI
OpenAI's 117B-total, 5.1B-active open-weight reasoning and agentic model, released with native MXFP4 MoE weights.
Google's 27B instruction-tuned multimodal model for text and image input, with a 128K context and broad language coverage.
DeepSeek
A 3B-class image-to-text model for document optical character recognition and layout-aware text extraction.
Qwen
The largest Qwen3 embedding checkpoint, designed for multilingual dense retrieval, classification, clustering and text matching.
BAAI
A multilingual embedding model that supports dense, sparse and multi-vector retrieval with 1,024 dimensions and inputs up to 8,192 tokens.
Qwen
A compact automatic speech recognition checkpoint released in July 2026 for multilingual transcription and audio understanding.
OpenAI
OpenAI's 1.55B-parameter multilingual speech-recognition and translation checkpoint, widely supported across transcription runtimes.
Mistral AI
A 4B-class real-time automatic speech recognition model released by Mistral AI for low-latency streaming transcription.
Black Forest Labs
A compact rectified-flow model for text-to-image, image editing and multi-reference work, released under Apache 2.0.
Qwen
The December 2025 Qwen-Image update for text-to-image generation, with improved realism, natural detail and text rendering.
Wan AI
A 5B text-and-image-to-video model that supports 720p generation and an official single-GPU offload route.
Tencent
An 8.3B text-to-video and image-to-video model with 480p and 720p checkpoints, optional super-resolution and distilled workflows.
Tencent
Tencent's image-to-3D generation pipeline for shape and texture creation, with open model weights and a model-specific community licence.
Meta
A 12B multimodal safeguard model for classifying text and image prompts and responses against Meta's hazard taxonomy.
Stability AI
An 8B-class text-to-image model in the Stable Diffusion 3.5 family, with a large ecosystem of Diffusers and ComfyUI workflows.
OpenAI
A 21-billion-parameter sparse reasoning model with 3.6 billion active parameters, native tool use and an MXFP4 release intended for local or specialised work.
Qwen
An 80-billion-total, 3-billion-active sparse model designed for coding agents, local development and long repository context.
Qwen
A compact 4-billion-parameter multimodal model with native vision, reasoning, coding and broad multilingual support.
Qwen
A 27-billion-parameter dense multimodal model for reasoning, coding, agents and visual understanding across 201 languages and dialects.
Google DeepMind
Google DeepMind's 12-billion-parameter instruction-tuned Gemma 4 model, supporting text, images, video and audio input with text output.
Google DeepMind
Google DeepMind's 30.7-billion-parameter instruction-tuned multimodal model for text and image understanding with a 256K context window.
StepFun
A 198-billion-parameter sparse vision-language model with about 11 billion active parameters, designed for reasoning, coding, agents and visual intelligence.
Z.ai
A 744-billion-total, 40-billion-active sparse model aimed at complex systems engineering and long-horizon agentic tasks.
MiniMax
A sparse agentic model trained for coding, tool use, search and office work across more than ten programming languages.
Qwen
A multilingual text-to-speech model with streaming and non-streaming generation, instruction-controlled delivery and nine supplied voice timbres.
Qwen
An 8-billion-parameter multilingual reranker for scoring retrieved passages across more than 100 natural and programming languages.
Black Forest Labs
A 9-billion-parameter rectified-flow image model for text-to-image generation and multi-reference image editing.
StepFun
An end-to-end audio-language model for spoken conversation, audio understanding, paralinguistic cues and audio tool use.
No model matches that search and workload combination.
Understanding compatibility
GPU memory provides the first useful sizing check. The final result also depends on model precision, serving software, context, batch, concurrent users and the way multiple GPUs are connected.
Memory, runtime, data boundaries and service access affect whether a model can be deployed reliably. Test the exact model version with your real workload before choosing hardware.
Need another model?
Send us the Hugging Face, GitHub or official model link. We will compare the exact version, licence and memory requirement with the complete server range.
A similar name is not enough to establish compatibility. Quantisation, context length, image resolution and runtime can materially change the hardware requirement.
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