Expired60% confidenceFactExact time
DS4 applies asymmetric structure-aware quantization, maintaining shared experts, routing networks, projection matrices, and attention layers at Q8 or FP16 high precision.
2
Sources
60%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
Hands-On Testing of DS4 Engine by Redis Creator: How Does DeepSeek V4 Perform Locally on a 128GB Mac?
bilibili苦苦菜-分享版6/6/2026
Related Claims
Unverified工程实践中建议将路由器层、注意力投影矩阵及门控网络保留在 BF16/FP16 精度,仅对专家 FFN 权重进行激进量化69% similarUnverifiedThe NVFP4 version of Qwen3-27B uses sensitivity-aware mixed precision: feed-forward layers run in FP4 (E2M1) with FP8 block scaling, while attention projections, DeltaNet recurrent layers, the vision encoder, and multi-token prediction heads remain in FP8 or BF16.67% similarUnverifiedThe M5 Ultra's FP8 and FP4 floating-point compute performance is identical to its BF16 performance, and the gap with the Spark narrows noticeably when dropping to FP4 precision.65% similarVerifiedQuantization compresses neural network weights from high-precision floating-point numbers (such as FP32 and FP16) to low-bit integers (such as INT8 and INT4), with modern quantization schemes including GPTQ, AWQ, and GGUF's K-quant series65% similarUnverifiedNVFP4 is a 4-bit floating-point quantization format used to reduce memory usage and improve inference throughput while preserving model accuracy.64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/51720API
curl https://kongchang.com/api/v1/knowledge/claims/51720MCP
get_claim(id=51720)