--- pipeline_tag: image-text-to-text base_model: - deepseek-ai/DeepSeek-V4-Flash-Vision-Exp license: mit library_name: transformers tags: - deepseek-v4 - vision - multimodal - mxfp4 - ocp-mxfp4 - w4a16 - vllm - sglang - tensorrt-llm - dspark - speculative-decoding - solstice-ai - anvil - turboquant - sovereign-ai - image-text-to-text ---

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DeepSeek-V4-Flash-Vision-Exp (Native MXFP4 • W4A16)

Official Solstice-AI MXFP4 Production Release • Native Pre-trained OCP Microscaling FP4 • Pure W4A16 Execution in vLLM & SGLang

Original Architecture by DeepSeek-AI • Curated, Verified & Packaged by Solstice-AI

Solstice-AI License Anvil Runtime Format DSpark Pipeline Context Vision

--- ## Executive Summary **`Solstice-AI/DeepSeek-V4-Flash-Vision-Exp-MXFP4`** is the canonical, production-optimized **OCP MXFP4 (W4A16)** distribution of DeepSeek's experimental multimodal foundation model [`deepseek-ai/DeepSeek-V4-Flash-Vision-Exp`](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp). DeepSeek-V4-Flash-Vision-Exp features a **305B parameter Mixture-of-Experts (MoE)** architecture (13B activated parameters per token) with **43 hidden layers, 256 routed experts (6 active per token), Multi-Head Latent Attention (MLA), and a dedicated 32-layer multimodal vision encoder**, natively supporting a **1,048,576-token (1 Million) context window**. ### The Gold Standard for W4A16 GPU Serving Unlike models subjected to lossy post-training weight quantization, **DeepSeek pre-trained all 256 routed MoE experts natively in Open Compute Project (OCP) MXFP4 block-floating-point format**. When deployed on modern GPU clusters (NVIDIA H100, A100, RTX 4090, or Blackwell) via engines like **vLLM** and **SGLang**, this checkpoint runs strictly in **W4A16 mode**: * **Weights in 4-Bit (OCP MXFP4):** Dramatically reduces memory bandwidth pressure and fits comfortably on dual or quad 80GB GPU nodes (~150 GB total checkpoint footprint). * **Activations in 16-Bit (BF16):** Activations remain uncompressed during matrix multiplication, ensuring **zero activation outlier clipping**, zero precision drift, and 100% mathematical integrity across long-context reasoning and vision grounding. * **Preserved High-Precision Non-Expert Stack:** All 32 layers of the multimodal vision encoder, router gate logits, attention heads, shared experts, and layer normalizations remain in their native BF16 / FP8 precision. --- ## Architectural Breakdown ``` +-------------------------------------------------------------+ | DeepSeek-V4-Flash-Vision-Exp (305B Total) | +-------------------------------------------------------------+ | +--------------------------------+-------------------------------+ | (~96% of Total Parameters) | (~4% of Parameters) v v +-------------------------------+ +-------------------------------+ | 256 Routed MoE Experts | | Non-Expert Dense Layers | | (Natively Trained OCP MXFP4) | | (MLA, Attention, Routers, | +-------------------------------+ | Shared Experts, Embeddings) | | +-------------------------------+ | | v v Native ~4.25-bit Base State Native BF16 / FP8 / FP32 (Dequantized to BF16 GEMM) (Bit-Exact Native Math) ``` --- ## Hardware & Deployment Matrix | GPU Configuration | Total VRAM | Context Window | KV Cache Precision | Deployment Mode | | :--- | :---: | :---: | :---: | :--- | | **2× NVIDIA A100 / H100 80GB** | 160 GB | 32,768 | FP8 | High-throughput API server (compact context) | | **4× NVIDIA A100 / H100 80GB** | 320 GB | 131,072 | FP8 / BF16 | **Recommended.** Production long-context agent serving | | **8× NVIDIA H100 / H200 80/141GB** | 640+ GB | 1,048,576 (1M) | FP8 / BF16 | Maximum concurrency & full 1M reasoning window | --- ## Official Benchmark Scoreboard Evaluated by DeepSeek using the minimal mode of DeepSeek Harness with `max` reasoning effort (`temperature = 1.0, top_p = 0.95`): | Benchmark Suite | Focus Area | **DeepSeek-V4-Flash-Vision-Exp** | DeepSeek-V4-Flash-0731 | Claude Opus-4.8 | | :--- | :--- | :---: | :---: | :---: | | **Terminal Bench 2.1** | Terminal Execution & Shell Control | **83.9** | 82.7 | 85.0 | | **NL2Repo** | Code Repository Generation | **57.7** | 54.2 | 69.7 | | **Cybergym** | Cybersecurity Operations & CTF | **75.3** | 76.7 | 78.3 | | **DeepSWE** | Real-World Software Engineering | **59.3** | 54.4 | 58.0 | | **Toolathlon-Verified** | Multi-Tool Function Calling Loops | **75.9** | 70.3 | 76.2 | | **DSBench-Hard** | Complex Data Science Workflows | **63.6** | 59.6 | 71.7 | | **AutomationBench (Public)** | End-to-End Task Automation | **25.7** | 25.1 | 27.2 | | **ApexBench (Pass@1)** | Multimodal Agent UI & Vision QA | **36.5** | 26.2 | 39.4 | | **Agents' Last Exam** | Multimodal Reasoning & Academic VQA | **27.3** | 25.2 | 25.7 | | **Chartography** | High-Density Chart & Visual Analysis | **64.3** | — | 65.0 | | **ZeroBench (Pass@5)** | Zero-Shot Multimodal Complex Tasks | **35.0** | — | 34.0 | --- ## Quick Start & Serving Guide ### 1. High-Throughput Serving with `vLLM` + Native DSpark Speculative Acceleration Launch an OpenAI-compatible API server with multimodal vision support, native reasoning parser, tool calling, and DSpark speculative decoding: ```bash vllm serve Solstice-AI/DeepSeek-V4-Flash-Vision-Exp-MXFP4 \ --tensor-parallel-size 4 \ --kv-cache-dtype fp8 \ --block-size 256 \ --max-model-len 131072 \ --tool-call-parser deepseek_v4 \ --enable-auto-tool-choice \ --reasoning-parser deepseek_v4 \ --speculative-config '{"method":"dspark","num_speculative_tokens":3,"draft_sample_method":"probabilistic","enable_adaptive_verification":true}' \ --trust-remote-code \ --port 8000 ``` > [!TIP] > Target and draft weights are co-located in the same checkpoint (shards 46–48). You do not need to supply an external draft model path. --- ### 2. Serving with `SGLang` For maximum throughput and low-latency prefix caching in agentic workflows, enable DSpark natively: ```bash sglang serve \ --model-path Solstice-AI/DeepSeek-V4-Flash-Vision-Exp-MXFP4 \ --tp 4 \ --speculative-algorithm DSPARK \ --mem-fraction-static 0.85 \ --trust-remote-code \ --host 0.0.0.0 \ --port 30000 ``` --- ### 4. Interactive Multimodal Python Client Example ```python from openai import OpenAI client = OpenAI(base_url="http://localhost:8000/v1", api_key="none") response = client.chat.completions.create( model="Solstice-AI/DeepSeek-V4-Flash-Vision-Exp-MXFP4", messages=[ { "role": "user", "content": [ {"type": "text", "text": "Analyze this technical architecture diagram and extract all component interactions:"}, { "type": "image_url", "image_url": { "url": "https://example.com/system_architecture.png" } } ] } ], max_tokens=2048, temperature=0.2 ) print(response.choices[0].message.content) ``` --- ## Citations & Acknowledgments ```bibtex @misc{deepseek2026v4flashvision, title={DeepSeek-V4-Flash-Vision-Exp: An Open Experimental Multimodal MoE Architecture}, author={DeepSeek-AI Team}, year={2026} } @software{deepseek2026deepspec, title={DeepSpec: High-Throughput Semi-Autoregressive Speculative Decoding Framework}, author={DeepSeek-AI Team}, year={2026}, url={https://github.com/deepseek-ai/DeepSpec} } @software{solsticeai2026mxfp4, title={Solstice-AI DeepSeek-V4-Flash-Vision-Exp MXFP4 Distribution}, author={Solstice-AI Research Team}, year={2026}, url={https://huggingface.co/Solstice-AI} } ```