How DeepSeek‑V3.1’s New FP8 Precision Supercharges Domestic Chip Performance

DeepSeek‑V3.1 has been officially released, featuring the UE8M0 FP8 Scale precision specially designed for the next generation of domestic chips. By combining an 8‑bit unsigned exponent with a zero‑tail mantissa, the dynamic range expands to 76 orders of magnitude, reducing memory consumption by 50‑75% and allowing chips like Ascend 910B to run 128K‑context large language models efficiently.

The domestic chip ecosystem is actively adapting to FP8 precision: MooreThread MTTS5000 and Suiyuan L600 already support native FP8; Cambricon and HaiGuang achieve compatibility through software optimizations; and upcoming Huawei and other next‑gen chips will integrate FP8 units, driving significant gains in compute density and energy efficiency.

UE8M0 FP8 matches the long‑tail distribution of LLM weights, improving perplexity on language tasks by 15‑20% over INT8 and simplifying arithmetic to exponent addition, which lowers circuit complexity. National policies have added DeepSeek to the standard computing power library, prompting operators and energy firms to prioritize domestic chip modules.

Despite these advances, challenges persist: some chip architectures lack full IP independence, INT8 remains advantageous in edge scenarios, and the FP8 software stack is still maturing. DeepSeek’s strategy of using application demand to push hardware innovation aims to build a robust ecosystem, but breakthroughs in IP autonomy, mixed‑precision optimization, and cross‑vendor standards are needed for Chinese AI compute to become globally competitive before 2030.

1. DeepSeek‑V3.1’s Technical Innovation and Domestic Chip Synergy

The core breakthrough of DeepSeek‑V3.1 is the UE8M0 FP8 Scale precision. UE8M0 FP8 uses an 8‑bit unsigned exponent and a zero‑tail mantissa, extending the dynamic range from 2⁻¹²⁷ to 2¹²⁸. This matches the compute bottlenecks of domestic chips, allowing more integer units per die and cutting each parameter from 4 bytes (FP32) to 1 byte, thus halving memory usage. Combined with micro‑scaling (one shared scaling factor per 32 elements), the design lets algorithms compensate for hardware limits, enabling mid‑range chips to approach high‑end performance.

2. Domestic Chip Ecosystem Adaptation Race

DeepSeek’s UE8M0 FP8 is explicitly targeted at upcoming Chinese chips, prompting rapid industry response. MooreThread MTTS5000 and Suiyuan L600 already provide native FP8, boosting compute density and lowering power. Cambricon, HaiGuang, and others achieve FP8 compatibility via software, delivering 30‑40% performance gains. Future Huawei Ascend 910D and other chips will embed FP8 units, fostering a soft‑hard co‑design that brings Chinese AI compute closer to international levels, as evidenced by industrial IoT deployments and billions of daily inference calls.

3. Technological Restructuring of Industry Competitiveness

UE8M0 FP8 reshapes the computation paradigm: unlike linear INT8, its exponential distribution aligns with the long‑tail weight characteristics of LLMs (≈90% of weights lie within ±0.1). Experiments show a 15‑20% perplexity improvement over INT8, while simplifying multiplication to exponent addition reduces circuit complexity, allowing more compute units on the same silicon area.

4. Future Challenges and Keys to Breakthrough

Key hurdles include incomplete IP autonomy of chip architectures, INT8’s power advantage in edge cases, and the immaturity of the FP8 software stack. DeepSeek’s approach of letting application needs drive hardware innovation seeks to build an ecosystem moat. Overcoming IP independence, mixed‑precision optimization, and cross‑vendor standardization will be essential for Chinese AI compute to achieve genuine global competitiveness by 2030.