The DeepSeek V4 Flash 0731 update demonstrates impressive local AI performance on a multi-GPU setup, excelling in reasoning, token processing speed, and handling large context windows, though it struggles with very large creative generation tasks due to its slow, token-heavy approach. The reviewer recommends it as a strong choice for users with powerful hardware, highlighting its accuracy and efficiency while noting upcoming model variants and features that may further enhance its capabilities.
The video reviews the latest DeepSeek V4 Flash 0731 update, focusing on its performance running locally on a multi-GPU setup including 4x 3090s, a 14090, and a 5060Ti. Due to VRAM limitations, the reviewer opts to run the Q4 model variant instead of the full Q8, noting that Q3 models optimized for quad 3090 setups are forthcoming. The model supports a massive context window of up to 1 million tokens, with configurable parameters like max and high reasoning modes, and specific settings recommended for tasks like genetic work. The reviewer uses the Llama C++ interface to run the model and highlights its efficient VRAM utilization and impressive prompt processing speeds.
The reviewer tests the model with a variety of prompts, starting with a complex moral dilemma scenario called “Armageddon with a twist.” The model delivers a nuanced refusal to take on a coercive role, demonstrating deep reasoning about morality and the implications of forced actions, which the reviewer finds impressive and insightful. The model processes this prompt at a rate of around 127 tokens per second and maintains strong reasoning capabilities throughout. Additional tests include straightforward questions like counting letters in a word and solving classic word problems, where the model performs accurately and efficiently.
One of the more demanding tests involves generating an SVG image of a cat walking on a fence, which pushes the model’s token consumption and processing speed to the limits. Despite the model’s thorough and detailed approach—spending over 30 minutes and generating more than 34,000 tokens—the output is slow and incomplete, with the cat and fence elements not clearly rendered. The reviewer notes that the model tends to overthink and double-check excessively, which can hinder concise and timely outputs for complex creative tasks. This highlights a trade-off between depth of reasoning and practical speed.
Throughout the review, the DeepSeek V4 Flash 0731 is praised for its speed and accuracy on typical reasoning and problem-solving tasks, outperforming many contemporary models like Kimmy K3. The model’s ability to handle large context windows and maintain high precision (especially in Q8 mode) makes it a strong contender for local AI enthusiasts with powerful hardware. The reviewer recommends it as a primary model for users with rigs similar to theirs and points to upcoming Q3 variants as promising options for those with slightly less VRAM.
In conclusion, while DeepSeek V4 Flash 0731 excels in reasoning, token processing speed, and handling complex prompts, it struggles with very large creative generation tasks due to its token-heavy and slow approach. The reviewer suggests experimenting with different reasoning modes (like low or verbose) to balance output quality and speed. They encourage viewers to explore the model further via resources on digitalspaceport.com and note that ongoing developments, such as potential MTP adapter integration, could enhance performance in future iterations. Overall, DeepSeek V4 Flash 0731 is positioned as a powerful and versatile local AI model worth considering for serious users.