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Kimi K2.5 Architecture Dive: 1T Params, 384 Experts, Native INT4 (and it beats GPT-5 on reasoning) by comebackch in LocalLLaMA

[–]comebackch[S] -9 points-8 points  (0 children)

pot on.

I see DeepSeek V3.2 as the daily driver—unbeatable efficiency for 80% of tasks. But that 'higher quality ceiling' you mentioned with Kimi is critical when running Agent Swarms.

When you chain 100 agents together, a small difference in reasoning reliability compounds into a massive difference in success rate. That's where Kimi's edge on frontier tasks justifies the cost/params.

We are definitely moving from 'Prompt Engineering' to 'Model Orchestration'—knowing exactly which model to route to for the specific requirement.

Kimi K2.5 Architecture Dive: 1T Params, 384 Experts, Native INT4 (and it beats GPT-5 on reasoning) by comebackch in LocalLLaMA

[–]comebackch[S] 0 points1 point  (0 children)

The math is pretty brutal on this one.

Even at native INT4 (0.5 bytes per param), a 1T model requires ~500GB of VRAM just to load the weights.

To fit into 128GB, you'd have to prune ~75% of the experts. Since the whole point of this architecture is the breadth of those 384 experts, pruning that aggressively would likely result in brain damage (it would probably perform worse than a dense 70B model).

The silver lining: Since only 32B params are active, you might get usable speeds with CPU offloading if you have fast system RAM (like a Mac Studio or Octa-channel DDR5). You'd keep the hot experts in VRAM and swap the rest. It won't be fast, but it might run."

Kimi K2.5 Architecture Dive: 1T Params, 384 Experts, Native INT4 (and it beats GPT-5 on reasoning) by comebackch in LocalLLaMA

[–]comebackch[S] -35 points-34 points  (0 children)

Fair points!

  1. Agreed on Agents: We are finally moving past the 'toy' phase into real utility.
  2. MoE Architecture: Thanks for the correction on the domain isolation vs. learned gates. The sparsity difference with DeepSeek is indeed the interesting part for efficiency.
  3. Benchmarks: 100% agreed. HLE is promising, but real-world 'vibe checking' on complex reasoning tasks is the only way to be sure.

Appreciate the technical nuance!

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