gpt-oss-20b vs Nemotron 3.5 Lightning 30B A3B

gpt-oss-20b

gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.

Nemotron 3.5 Lightning 30B A3B

A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories.

gpt-oss-20b

1. Start it with ollama run gpt-oss:20b (the build that fits the reference machine), or vllm serve openai/gpt-oss-20b with a current vLLM release rather than the card's pinned vllm==0.10.1+gptoss wheel
2. Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it
3. Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in <|call|>, reasoning on the analysis channel
4. Set effort with Reasoning: high (or low, medium) in the system message, or reasoning_effort in the template; medium is the default
5. Ask for JSON by putting a JSON Schema under # Response Formats in the developer message
6. Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users
7. Expect one tool call per turn; run several in sequence rather than in parallel

Nemotron 3.5 Lightning 30B A3B

1. Quickest start is ollama run nemotron-3.5-lightning, about 19 to 25 GB of files depending on the platform
2. On the reference machine use llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja, since the card's Q4_K_M tag doesn't exist there and NVFP4 needs NVIDIA GPUs
3. With vLLM serve it with --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder (SGLang uses --reasoning-parser nemotron_3)
4. Use temperature=1.0 and top_p=0.95, as the card says
5. Thinking is on by default. Pass chat_template_kwargs with enable_thinking false for short tasks
6. For coding agents pass force_nonempty_content: true in chat_template_kwargs, which vLLM's nemotron_v3 parser reads so a cut-off reasoning trace still returns content
7. Go past 262,144 tokens only with the card's flags (VLLM_ALLOW_LONG_MAX_MODEL_LEN=1, --max-model-len 1048576) and enough memory