DeepSeek-V4-Flash-0731 vs Qwen3-30B-A3B-Instruct-2507

DeepSeek-V4-Flash-0731

A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though six inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.

Qwen3-30B-A3B-Instruct-2507

A quick general-purpose cousin, with 3.3B of 30.5B parameters active per token, for instruction following, tool calling and long documents without a thinking step. It needs about 21 to 23 GB at 4 bits, and several providers host it. The main risk is neglect, with no file changes in over a year, no answers from Alibaba and no safety results.

DeepSeek-V4-Flash-0731

1. vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{"use_fp4_indexer_cache": true}' --speculative-config '{"method":"dspark","num_speculative_tokens":7,"draft_sample_method":"greedy"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 (the card's 4x GB300 example plus the parser flags from vLLM's recipe)
2. Build prompts with encoding_dsv4.py, deepseek-recipe's V4 encoder or vLLM's --tokenizer-mode deepseek_v4, since the repository has no Jinja template
3. Tool calls come back in a DSML tool_calls block with invoke elements, string parameters carry string="true" and JSON values string="false"
4. Set temperature 1.0 and top_p 0.95 for agent work, and allow up to 384K output tokens at reasoning effort high or max
5. Without the hardware, call it through a Hugging Face inference provider (Together, DeepInfra, Novita, Baseten, Scaleway, Featherless), not DeepSeek's API, where deepseek-v4-flash now means V4.1 Flash
6. LM Studio's library build is a 156.4 GB MXFP4 GGUF and LM Studio states 156 GB minimum system memory

Qwen3-30B-A3B-Instruct-2507

1. Quickest start is ollama run qwen3:30b-instruct, 19 GB. Plain qwen3:30b and qwen3:30b-a3b are the Thinking-2507 edition, not this one
2. On our reference machine use BF16 with vllm serve Qwen/Qwen3-30B-A3B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes, 61 GB of weights fits the GPU share
3. Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags
4. Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed
5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition
6. Leave config.json as it is unless you need more than 262,144 tokens, the 1M mode swaps in config_1m.json and runs one sequence at a time
7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented