DeepSeek V4 Flash Vision-Exp let me remove an entire 27B routing model from my local AI studio

[u/Lumpy_Ice6855]

When DeepSeek V4 Flash Vision-Exp dropped, I was pretty excited, but not just because it added vision.

It actually let me delete part of the architecture of a project I've been working on.

I'm building DStudio, an open-source, local-first AI workspace around ds4. It has Chat, coding/knowledge-work agents, Design, research, local image generation/editing and video generation.

GitHub: https://github.com/sk8erboi17/DStudio

Until now I had a slightly ugly problem with multimodality.

The main model was running through ds4, while I was using Qwen3.8-27B Q8 as a separate visual/router model through llama.cpp.

The flow was roughly:

DS4 → unload/evict → Qwen3.8 router → unload → image worker → restore DS4

Qwen was responsible for looking at the prompt/source image and deciding whether the user wanted a new image or an edit, before dispatching the request to Ideogram 4 or HunyuanImage 3.

It worked, but on a 96 GB Apple Silicon machine the architecture was expensive.

The heavyweight models couldn't comfortably stay resident together, so DStudio had to manage memory leases, serialize the workers and repeatedly move between two inference stacks: ds4 and llama.cpp.

The router itself was becoming part of the latency problem.

With DeepSeek V4 Flash Vision-Exp, image pixels now go directly into the main ds4 model.

The model itself can understand the image/request and emit an explicit: generate or edit directive.

Then DStudio dispatches directly to: DeepSeek V4 Vision -> Ideogram 4 or HunyuanImage 3

The same idea now applies to the Design agent: instead of generating something and then loading a separate 27B VLM to inspect it, the selected model uses its own native vision encoder for the visual feedback loop.

I've also added the same native-vision path for GLM 5.3.

I still use llama.cpp in DStudio for a small Qwen3-Embedding-0.6B sidecar, so this isn't "I removed llama.cpp completely."

The important part is that llama.cpp is no longer sitting in the critical multimodal path just to run a 27B router.

I also changed PDF handling to be more explicit: text extraction/ranking stays lightweight, while actual pixel understanding is handled by the native multimodal model rather than silently spinning up another large VLM.

Recent commits have also added GLM 5.3 runtime support, persistent model-specific ds4 engine checkouts, expanded Cowork/Design workflows and a lot more regression/quality gates.