BAGEL-7B-MoT

BAGEL-7B-MoT is an open-source multimodal foundation model from ByteDance’s Seed team that collapses understanding and generation of text, images, and video into a single, unified architecture. With 14B total parameters and 7B active under a Mixture-of-Transformer-Experts (MoT) design, it directly competes with models like Qwen2.5-VL and InternVL-2.5 on visual reasoning, while matching dedicated generators like SD3 on text-to-image quality. What sets BAGEL apart is its ability to handle tasks that go beyond standard multimodal work—free-form visual manipulation, multiview synthesis, and 3D world navigation—all in one weight set. Licensed Apache 2.0, it’s built for practitioners who want a single local model that can both describe an image and generate a new one from a prompt.

BAGEL’s MoT architecture is a sparse mixture of transformer experts—only 7B of the 14B parameters are active per forward pass. This means inference is roughly as compute- and memory-intensive as a 7B dense model, but the model retains the representational capacity of a 14B network. The key is expert routing: during each token computation, the model activates a subset of expert parameters, keeping latency and VRAM usage in line with the active count.

The model uses dual encoders for vision input:

• SigLIP-L for semantic-level features
• FLUX.1 VAE for pixel-level details

These feed into separate language and vision decoder experts, enabling the model to both understand and generate images without a separate diffusion backend. For text, it inherits the Qwen2.5-7B-Instruct backbone, giving it strong instruction-following and reasoning abilities.

Context length is not officially specified, but the underlying Qwen2.5 architecture supports up to 32k tokens in its base form—expect similar or slightly reduced effective length due to vision token overhead. For local deployment, MoE’s active-parameter efficiency is critical: you get a 14B model’s performance at 7B-level memory and compute requirements.

BAGEL-7B-MoT is best described as a unified multimodal engine. It can:

• Visual understanding: answer questions about images, describe scenes, perform OCR on natural images, reason about spatial relationships. Benchmarks show it outperforms Qwen2.5-VL-7B on standard VQA and reasoning tasks.
• Text-to-image generation: produce high-quality images from complex prompts, competitive with SD3 medium. The quality is consistent across styles—photorealistic, illustrations, and abstract compositions.
• Image editing: modify existing images via natural language instructions—swap objects, change backgrounds, adjust lighting. The model supports free-form manipulation (e.g., “add a golden retriever sitting on the couch”), not just constrained editing.
• World-modeling tasks: generate multiple consistent views of a scene (multiview synthesis), and perform navigation-like tasks on 2D maps or simulated 3D environments. This is unique among models at this size and is derived from interleaved pretraining on structured multimodal data.

Concrete use cases:

• Local image captioning and visual Q&A for accessibility tools or data labeling.
• On-device image editing without cloud dependencies—useful for content creation apps.
• Rapid prototyping of multimodal agents that need both understanding and generation in a single loop.
• Research into embodied AI and spatial reasoning with synthetic environments.

Because only 7B parameters are active, running BAGEL is feasible on consumer hardware with modest VRAM, but the full 14B weight set must still be loaded. Quantization is the lever.

VRAM estimates by quantization:

Recommended quantization for most users: Q4_K_M strikes the best balance of quality loss (minimal) and VRAM headroom, allowing batch processing or vision encoding overhead. For purely text tasks, Q5_K_M is also viable on 16GB cards.

Expected performance (Q4_K_M, single RTX 4090, 4-bit offloading):

• Text generation: ~30–45 tokens/second
• Image generation (512x512): 5–15 seconds per image
• Visual Q&A with image input: ~40–80 tokens/second

Getting started: The quickest path is via Ollama (when community support arrives) or directly with the official bagel-mot library. ByteDance provides a Dockerfile and inference scripts on [GitHub](https://github.com/bytedance-seed/BAGEL). For CPU-only inference, you can use llama.cpp with MoE support (weights need conversion), but expect ~2–3 tokens/second for text.

Hardware advice: For multimodal workloads, prioritize GPU VRAM over raw compute. Vision encoding (SigLIP + VAE) adds ~2–3 GB overhead when caching image embeddings. A RTX 4090 24GB is the sweet spot for Q5_K_M with image processing. The M4 Max 48GB can run FP16 with offloading, but peak memory bandwidth is lower—expect slower image generation.

For a deep dive on running MoE models on consumer GPUs, see our [guide on BAGEL-7B-MoT hardware requirements](#). Quantization tricks and memory mapping strategies are covered there.

BAGEL-7B-MoT vs Qwen2.5-VL-7B

Qwen2.5-VL is a dense 7B vision-language model with strong understanding and reasoning, but it cannot generate images. BAGEL adds generation and editing capabilities at the cost of slightly higher total VRAM (due to vision decoders). If you need only image understanding and text, Qwen2.5-VL is lighter. If you want a single model that does both, BAGEL wins.

BAGEL-7B-MoT vs Llama-3.2-11B-Vision (MoE)

Llama 3.2 uses an 11B MoE with ~8B active parameters. It has larger context (128k) and stronger pure-text benchmarks, but BAGEL significantly outperforms it on multimodal understanding and generation tasks. BAGEL’s dual-encoder design gives it better image fidelity and editing fidelity. Choose Llama if your primary load is long-context text with occasional vision; choose BAGEL for image-heavy workflows.

BAGEL-7B-MoT vs SD3 (Dedicated text-to-image)

SD3 is specialized for image generation and edges ahead in prompt adherence and variety. BAGEL is competitive on quality but slightly slower and less flexible in resolution. However, BAGEL is a single model that also does understanding, editing, and world modeling—SD3 is only a generator. If you need a Swiss Army knife for multimodal tasks, BAGEL’s efficiency per parameter makes it the better local choice.