NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-30B-A3B-Instruct

NVIDIA Nemotron 3.5 Bolt E4B 4GB

A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results.

Qwen3-VL-30B-A3B-Instruct

A mid-sized vision-language cousin for screenshots, documents, charts and video that runs fast for its size, with about 3B active parameters. It needs about 23 GB for Alibaba's Q4_K_M GGUF with its vision file, loads in every major runtime under plain Apache 2.0, and three providers host it. With no published safety work, add guardrails before it drives a GUI.

NVIDIA Nemotron 3.5 Bolt E4B 4GB

1. Quickest start is ollama run nemotron-3.5-bolt:e4b, about 8 GB of files, and set num_ctx to 65536 or less, since NVIDIA supports 64K
2. With NVIDIA's GGUF use llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf -c 65536 -ngl 999 --jinja, and add --reasoning on --reasoning-budget <tokens> to cap thinking
3. With vLLM 0.31.0 or later pass --enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4 and the repository's chat_template.jinja
4. Pass enable_thinking explicitly in chat_template_kwargs, since the template defaults to off whatever the card says, and set skip_special_tokens false as the card's examples do
5. Use temperature=1.0 and top_p=0.95 in both modes, as the card says
6. Set conversational_mode true for spoken replies, which appends a brevity instruction to the system prompt
7. Answer a slow tool at once with a pending tool message, then send the result later as a user message starting TOOL RESULT, as the card's asynchronous pattern says
8. Keep persistent rules in the system prompt and send state updates as separate user or system messages in order, per the card's prompting guidelines

Qwen3-VL-30B-A3B-Instruct

1. Start it with llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768, or ollama run qwen3-vl:30b-a3b-instruct
2. In Ollama the plain qwen3-vl:30b tag is the Thinking edition, so name 30b-a3b-instruct
3. With the GGUF, load the mmproj vision file too, or images won't be read
4. Parse tool calls as JSON inside <tool_call> tags, or use SGLang's --tool-call-parser qwen
5. Use the shipped sampling, temperature 0.7, top_p 0.8 and top_k 20, from generation_config.json
6. Validate JSON output yourself, since structured output isn't documented
7. vLLM needs 0.11.0 or later, Transformers 4.57.0 or later and llama.cpp b6907 or later