# GPT-4/o1-level Local VSCode Copilot on a Desktop with only 24GB VRAM
-[SUMMARY](#summary)
-[Prerequisites](#prerequisites)
-[Bench Result](#bench-result)
-[V0.2](#v02)
-[Settings](#settings)
-[Memory consumption:](#memory-consumption)
-[Benchmark Results](#benchmark-results)
-[V0.3-Preview](#v03-preview)
-[Settings](#settings-1)
-[Memory consumptions:](#memory-consumptions)
-[Benchmark results](#benchmark-results-1)
-[How to Run](#how-to-run)
-[V0.2 Showcase](#v02-showcase)
-[Single socket version (32 cores)](#single-socket-version-32-cores)
-[Dual socket version (64 cores)](#dual-socket-version-64-cores)
-[V0.3 Showcase](#v03-showcase)
-[Dual socket version (64 cores)](#dual-socket-version-64-cores-1)
-[Some Explanations](#some-explanations)
-[FAQ](#faq)
# SUMMARY
> **Fed 10, 2025**: Support DeepseekR1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~28x speedup.<br>
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@@ -137,4 +158,6 @@ DeepSeek's MLA operators are highly computationally intensive. While running eve
- Intel AMX Optimization – Our AMX-accelerated kernel is meticulously tuned, running several times faster than existing llama.cpp implementations. We plan to open-source this kernel after cleansing and are considering upstream contributions to llama.cpp.
5. Why Intel CPUs?
Intel is currently the only CPU vendor that supports AMX-like instructions, which delivers significantly better performance compared to AVX-only alternatives.
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Intel is currently the only CPU vendor that supports AMX-like instructions, which delivers significantly better performance compared to AVX-only alternatives.
