DeepSeek V4.1 Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB

DeepSeek V4.1 Flash

A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories.

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.

DeepSeek V4.1 Flash

1. DeepSeek's reference run is its inference folder, convert.py with --model-parallel 8 --expert-dtype fp4, then torchrun --nproc-per-node 8 generate.py --ckpt-path "$SAVE_PATH" --config config.json --interactive
2. In vLLM 0.32.0 the parsers are named deepseek_v41 for both --tool-call-parser and --reasoning-parser. That's vLLM's naming, DeepSeek doesn't document a vLLM command
3. Build prompts with encoding.py or pip install deepseek-recipe, not the September repository's chat_template.jinja, which an open pull request says trails the 16 October encoding
4. Tool calls come back in a DSML calls block with a space before calls, invoke and parameter, unlike V4
5. Set reasoning_effort to 1 to 100, or low, high or max (50, 75, 100), in thinking mode only. Default is 75
6. Sample at temperature 1.0 and top_p 1.0 and allow 256K or more output tokens, as the card recommends
7. Without the hardware, call DeepSeek's API as deepseek-flash, which serves this exact update

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