Localized Performance Benchmarks for DeepSeek-V4.1-Flash

New performance data has emerged regarding the localized execution of DeepSeek-V4.1-Flash-1017, a model variant from the Chinese AI developer DeepSeek. According to unverified reports shared on Reddit, the model can be run on consumer-grade hardware using various quantization methods.

In a reported test setup utilizing a single NVIDIA RTX 3090 GPU and 192 GB of DDR5 RAM on a Windows environment, the “ds4 Q2 GGUF” quantization achieved an average speed of 41 tokens per second (tok/s). The user reported that the output accuracy was within a point of the official DeepSeek API, specifically matching the API on 90.2% of general text prompts and 92.4% of code and math prompts.

The testing involved several quantization levels, including versions from ISTA-DASLab and Unsloth. The data suggests that performance scales with available system memory; for instance, reducing RAM from 192 GB to 128 GB resulted in a drop from 41 tok/s to 33 tok/s for the default Q2 quantization. The report noted that the model supports a context window of up to 1 million tokens. While these figures provide a benchmark for local deployment, they have not been independently verified by third-party labs.

The Competitive Landscape of Chinese AI Development

The performance of DeepSeek occurs amid ongoing scrutiny regarding the development methodologies of major Chinese AI firms. According to reports from Arstechnica and CISA, several prominent developers—including DeepSeek, Alibaba, Moonshot AI, MiniMax, StepFun, and Z.AI—have faced allegations from U.S. agencies and competitors like Anthropic and OpenAI of using large-scale model distillation from Western models.

DeepSeek has countered these claims by highlighting its proprietary architectural advancements. The company utilizes a Mixture-of-Experts (MoE) design and a reinforcement learning method known as Group Relative Policy Optimization (GRPO), which allows for training without a massive external critic model. Similarly, Moonshot AI has attributed its long-context capabilities to native linear attention systems and local algorithmic research rather than external distillation.

The ability of Chinese firms to train and run large-scale models is increasingly linked to domestic semiconductor developments. Despite U.S. export controls and sanctions aimed at companies like Huawei, reports indicate significant progress in China’s domestic chip industry.

Recent data suggests that Huawei’s Ascend 910C chip is being positioned as a competitor to Nvidia’s H100, with some reports suggesting the upcoming 910D may exceed that performance level. Additionally, companies like Ant have reported using domestic chips that yield results comparable to Nvidia’s H800.

The sector is also seeing advancements in specialized hardware, including:

  • Photonic Computing: Chinese institutes have reported the production of photonic chips capable of delivering high-speed computing.
  • Quantum Computing: Developments include the Zuchongzhi 3.0 superconducting processor and various quantum-resistant cryptographic chips.
  • Advanced Manufacturing: Reports indicate progress in 3-nm chip production and the development of domestic e-beam lithography machines to reduce reliance on foreign equipment.

While U.S. restrictions have impacted access to certain high-end technologies, market data suggests a shift in the landscape, with Nvidia’s market share in China reportedly falling below 60% as domestic alternatives emerge.