Muse Glimmer 30B vs Qwen3.5-4B

Muse Glimmer 30B

A dense 30B multimodal cousin for local agent and coding work with images, documented tool calls and adjustable reasoning. Meta's 17 GB GGUF fits a 24 GB card and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. The main risks are one tool call per turn, no documented JSON output and a usage policy that adds limits to Apache 2.0.

Qwen3.5-4B

A small vision-language cousin for tool calls, light agent steps and image or video reading on almost any machine, with switchable thinking. Ollama's 3.3 GB build or the 9.3 GB BF16 files run in every major runtime under plain Apache 2.0. The main risks are no safety results, no structured-output guarantee and no answers from Alibaba since March.

Muse Glimmer 30B

1. Start it with llama serve -hf meta-models/Muse-Glimmer-30B-GGUF --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 --chat-template-kwargs '{"reasoning_strength":"high"}' on llama.cpp b10353 or later, or ollama run muse-glimmer on Ollama 0.32.7 or later
2. In vLLM pass --tool-call-parser muse_glimmer --reasoning-parser muse_glimmer, in SGLang muse for both, in ExecuTorch --tool-parser atem. Without them tool calls come back as plain text
3. Set reasoning per request with chat_template_kwargs.reasoning_strength (low, medium, high or xhigh). llama.cpp and ExecuTorch don't read reasoning_effort, and ExecuTorch rejects it with a 400
4. Expect one tool call per turn and return each result before the next. ExecuTorch's server rejects tool_choice="required"
5. Keep temperature 1.0, top_p 0.95 and top_k 64, don't run greedy, and never stop on <|eom|>
6. Leave max_tokens room for reasoning, since in llama.cpp a request cut off mid-thought returns empty content with finish_reason length
7. For images add Meta's 1.4 GB mmproj GGUF, which takes the build to about 22 GB with the runtime's fifth

Qwen3.5-4B

1. Smallest start is ollama run qwen3.5:4b (Q4_K_M, about 4 GB with context)
2. Full precision vllm serve Qwen/Qwen3.5-4B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder
3. Turn thinking off with "chat_template_kwargs": {"enable_thinking": false} in the request body, since it's on by default and /think and /nothink aren't supported
4. There's no generation_config.json, so pass the card's sampling yourself, for example temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5 without thinking
5. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
6. Validate any JSON it returns, since structured output isn't documented