Hy3 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB

Hy3

Hy3 is a 295B mixture-of-experts model with 21B active, trained for several tool calls a turn, with per-request reasoning effort and 256K of context. It needs an 8-GPU server for FP8 or about 200 GB for a 4-bit build. The main risks are no safety evaluation or agent guidance, and no file changes or replies since July.

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.

Hy3

1. Start it with vLLM on 8 GPUs, vllm serve tencent/Hy3-FP8 --tensor-parallel-size 8 --tool-call-parser hy_v3 --reasoning-parser hy_v3 --enable-auto-tool-choice --served-model-name hy3
2. In SGLang use --tool-call-parser hunyuan --reasoning-parser hunyuan, as the card shows
3. Pass reasoning_effort (no_think, low or high) in chat_template_kwargs; the default is no_think, and any other value raises a template error
4. With the vLLM reasoning parser the chain of thought comes back in message.reasoning and the answer in message.content
5. Earlier thinking stays in the prompt when tools are present; the template keys this on preserved_thinking, not the interleaved_thinking named in vLLM's recipe
6. Use temperature 0.9 and top_p 1.0, the card's recommended sampling
7. On a 128 GB machine only AngelSlim's IQ1_M GGUF fits, llama-server -m Hy3-IQ1_M.gguf -ctk q8_0 -ctv q8_0 -fa on -c 65536, with llama.cpp b11768 or later; at 89.4 GB × 1.2 it sits right at the 107.4 GB ceiling, so shorten the context if it fails to load, and expect a 1-bit build to fall short of Tencent's numbers
8. Without the hardware, call it through Featherless AI or DeepInfra via Hugging Face's inference providers

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