DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 is a text-generation model released under the MIT license on the deepseek-ai repository. It is described as the official release of DeepSeek-V4-Flash, superseding a preview version, with enhanced agentic capabilities. It shares the same model structure as DeepSeek-V4-Flash-DSpark and includes a speculative decoding module. The release does not include a Jinja-format chat template; instead, an encoding folder provides Python scripts and test cases for converting OpenAI-compatible messages to model inputs and parsing outputs. The reasoning_effort parameter supports three levels. Benchmark tables compare its reported scores with other models.
At a glance
- Parameters
- 304.18B
- Architecture
- deepseek_v4
- Context length
- 1,048,576
- License
- mit
- Disk space
- 155.4 GB
- Software
- SGLang, vLLM, Publisher PyTorch reference
Will it run on your machine?
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Add my hardwareBest for
- Text generation
- Agentic tasks
- Code agent tasks
- Tool-use benchmarks
- Full-stack development tasks (internal DSBench)
Tradeoffs
- Does not include a Jinja-format chat template
- Requires the dedicated encoding folder for message encoding and output parsing
- Speculative decoding module is included but no compatibility or performance details are provided
- Numerical hardware requirements are not stated
- Benchmark results are reported under specific evaluation settings and may not reflect other conditions
Ways to run it
SGLang · Linux
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
sglang serve --trust-remote-code --model-path deepseek-ai/DeepSeek-V4-Flash-0731 --tp 4 --moe-runner-backend flashinfer_mxfp4 --speculative-algorithm DSPARK --mem-fraction-static 0.90 --chunked-prefill-size 4096 --swa-full-tokens-ratio 0.1vLLM · Linux
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
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"}'Publisher PyTorch reference · Linux / rented GPU
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
torchrun --nproc-per-node ${MP} generate.py --ckpt-path ${SAVE_PATH} --config ${CONFIG} --interactiveExplore its uses
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