A receipt-heavy proof run for three local runtime lanes: GLM 5.2, MiniMax M3, and DeepSeek V4 Flash.
I published a small proof run from a local model evaluation lane.
The prompt was simple: make a fullscreen browser fireworks animation.
The actual test was not simple.
This run used three different local runtime lanes:
- GLM 5.2 through a GLM-capable llama.cpp server
- MiniMax M3 through a MiniMax-specific llama.cpp PR build
- DeepSeek V4 Flash through DS4, a separate DeepSeek-specific runtime
That is the part I care about.
The benchmark was not only “which model made nicer fireworks?” It was whether I could keep incompatible local model runtimes isolated, start them one at a time, validate their browser artifacts, stop them cleanly, and preserve the receipts.
Result:
GLM 5.2: published
MiniMax M3 Q5: excluded after hard-gate failure
DeepSeek V4 Flash: published
MiniMax actually made a lively scene, but it failed the hard gate because visible non-canvas overlays helped the output. A model can make something that looks good and still fail the contract, so that artifact was excluded from the public catalog.
Blog post and animations:
https://discostew.dev/posts/local-model-fireworks-approval-results/
GLM 5.2 animation:
https://discostew.dev/benchmarks/fireworks-approval-2026-06-21/glm-5-2/
DeepSeek V4 Flash animation:
https://discostew.dev/benchmarks/fireworks-approval-2026-06-21/deepseek-v4-flash/
Runtime sources:
GLM lane used ggml-org llama.cpp at b9716:
https://github.com/ggml-org/llama.cpp/tree/b9716
MiniMax M3 lane used the preliminary MiniMax-M3 llama.cpp PR:
https://github.com/ggml-org/llama.cpp/pull/24523
Fetch command for that PR branch:
git fetch origin pull/24523/head:minimax-m3
DeepSeek V4 Flash used DS4:
https://github.com/antirez/ds4
Model downloads:
GLM 5.2 GGUF, UD-Q3_K_M:
https://huggingface.co/unsloth/GLM-5.2-GGUF/tree/main/UD-Q3_K_M
MiniMax M3 GGUF, UD-Q5_K_M:
https://huggingface.co/unsloth/MiniMax-M3-GGUF/tree/main/UD-Q5_K_M
DeepSeek V4 Flash GGUF for DS4:
https://huggingface.co/antirez/deepseek-v4-gguf
The durable lesson: local AI work is not just about getting a model to run. It is about keeping the runtime, artifact, validation, and failure mode inspectable.