⚡ update v0.3 preview

README.md

@@ -23,7 +23,7 @@ Our vision for KTransformers is to serve as a flexible platform for experimentin
 
 <h2 id="Updates">🔥 Updates</h2>
 
-* **Fed 10, 2025**: Support DeepseekR1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~64X speedup. The Detailed tutorial is [here](./doc/en/DeepseekR1_V3_tutorial.md)
+* **Fed 10, 2025**: Support DeepseekR1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~64x speedup. The Detailed tutorial is [here](./doc/en/DeepseekR1_V3_tutorial.md)
 * **Aug 28, 2024**: Support 1M context under the InternLM2.5-7B-Chat-1M model, utilizing 24GB of VRAM and 150GB of DRAM. The detailed tutorial is [here](./doc/en/long_context_tutorial.md).
 * **Aug 28, 2024**: Decrease DeepseekV2's required VRAM from 21G to 11G.
 * **Aug 15, 2024**: Update detailed [TUTORIAL](doc/en/injection_tutorial.md) for injection and multi-GPU. 

doc/en/DeepseekR1_V3_tutorial.md

@@ -47,6 +47,12 @@ The main acceleration comes from
 - Intel AMX instruction set and our specially designed cache friendly memory layout
 - Expert selection strategy that selects fewer experts based on offline profile results of out of domain data
 
+
+*From our research on DeepSeekV2, DeepSeekV3 and DeepSeekR1, 
+when we slightly decrease the activation experts num in inference, 
+the output quality doesn't change,But the speed of decoding and prefill 
+is speed up which is inspiring. So our showcase makes use of this finding*
+
 ## how to run
 ### v0.2 showcase
 #### single socket version(32 cores)