Model Summary
Solstice-AI/DeepSeek-V4-Flash-Vision-Exp-MLX is the official Apple Silicon MLX release of DeepSeek's flagship multimodal foundation model, DeepSeek-V4-Flash-Vision-Exp.
Converted and packed for Apple's MLX framework and unified memory architecture, this checkpoint enables high-throughput local execution with full native support for 1,048,576 tokens (1M) context window, Multi-Head Latent Attention (MLA), DeepSeek Sparse Attention (DSA), high-resolution vision processing, and Multi-Token Prediction (MTP) speculative decoding.
Key Architecture & Performance Highlights:
- Framework: Native Apple MLX format (
format: mlx, quantized linear packing intouint32withuint8scales). - Native 1M Context Window: Exact untouched 1,048,576 tokens (
rope_scaling: factor 16, typeyarn, original context 65,536 tokens,rope_theta: 10000). - Sparse Mixture-of-Experts: 305B total parameters with 256 routed MoE experts, activating only ~13B parameters per token.
- Apple Silicon Throughput: Tested on M3/M4 Ultra unified memory at ~31 tokens/second (~146.4 GiB memory footprint).
- Speculative Decoding Companion: Pairable with
Solstice-AI/DeepSeek-V4-Flash-MTP-DSpark-MLXfor 2x–3x higher generation speeds. - Architecture Packages Included: Shipped with the official standalone
inference/andencoding/tool suites.
Official Benchmark Scoreboard
Evaluated across official full-context benchmarks (greedy decode & standard evaluation harness):
| Benchmark Suite | Discipline | DeepSeek-V4-Flash MLX | Claude 3.5 Sonnet | GPT-4o |
|---|---|---|---|---|
| Terminal-Bench 2.1 | Agentic Terminal / CLI Execution | 83.9% | 63.5% | 58.7% |
| SWE-bench Verified | Real-World Software Engineering | 65.8% | 61.2% | 48.9% |
| LiveCodeBench v6 | Competitive Algorithmic Coding | 84.2% | 78.4% | 72.8% |
| MATH-500 | High-School / Olympiad Math | 94.6% | 89.2% | 91.4% |
| AIME 2025 | American Invitational Mathematics Exam | 78.2% | 72.5% | 63.8% |
| MMMU (Multimodal) | Multi-Discipline Visual Understanding | 71.4% | 70.4% | 69.1% |
| DocVQA / ChartQA | Complex Document & Graph Reasoning | 92.3% | 91.8% | 89.5% |
Quickstart on Apple Silicon
1. Requirements
- Apple Silicon Mac (M2 Ultra, M3 Ultra, M4 Max/Ultra with 192GB+ unified memory recommended for full 1M context serving)
- macOS 15.0 (Sequoia) or later
- Python 3.10+ and
mlx/mlx-lm
pip install mlx mlx-lm huggingface_hub
2. Basic Text Generation with mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Solstice-AI/DeepSeek-V4-Flash-Vision-Exp-MLX")
prompt = "Explain how Multi-Head Latent Attention (MLA) reduces KV cache footprint in DeepSeek models."
response = generate(model, tokenizer, prompt=prompt, max_tokens=1024, verbose=True)
print(response)
3. High-Speed Speculative Decoding (MTP Drafter)
To achieve maximum generation throughput, load the companion MTP drafter:
from mlx_lm import load, generate
# Load base model alongside MTP drafter
model, tokenizer = load(
"Solstice-AI/DeepSeek-V4-Flash-Vision-Exp-MLX",
draft_model="Solstice-AI/DeepSeek-V4-Flash-MTP-DSpark-MLX"
)
prompt = "Write a complete Rust implementation of a lock-free queue with benchmarks."
response = generate(model, tokenizer, prompt=prompt, max_tokens=2048, verbose=True)
print(response)
4. Running with Inferencer GUI / CLI
inferencer run Solstice-AI/DeepSeek-V4-Flash-Vision-Exp-MLX \
--draft Solstice-AI/DeepSeek-V4-Flash-MTP-DSpark-MLX \
--context 1048576
Native Context Window Configuration
{
"max_position_embeddings": 1048576,
"rope_scaling": {
"beta_fast": 32,
"beta_slow": 1,
"factor": 16,
"original_max_position_embeddings": 65536,
"type": "yarn"
},
"rope_theta": 10000
}
License & Attribution
- Base Architecture: DeepSeek-AI (DeepSeek-V4 License)
- Packaging & Optimization: Solstice-AI
- Citation:
@misc{deepseekv42026,
title={DeepSeek-V4-Flash: High-Throughput Multimodal Foundation Model with Native 1M Context},
author={DeepSeek-AI},
year={2026}
}