Kimi K3 Slimmed Down from 711GB to 478GB: A Targeted Experiment in Multilingual Layer Removal

Kimi K3 on a Diet: The 711GB to 478GB Compression Experiment

A Reddit developer going by "hellohazine" recently sparked an interesting discussion with a model compression experiment. They trimmed the Unsloth quantized version of Kimi K3 from 711GB down to 478GB — a reduction of nearly one third. The trick wasn't brute-force quantization, but rather selectively removing the model's multilingual components while keeping its English capabilities fully intact.

What makes this approach clever is that it doesn't sacrifice the model's core intelligence. It's more like targeted liposuction — stripping out multilingual weights that English-only users will almost never touch. The original poster called it a "brilliant move" and encouraged the community to apply similar techniques to future model releases.

Why Removing Multilingual Weights Is an Efficient Compression Strategy

Large language models — especially those built on MoE (Mixture of Experts) architectures — dedicate a substantial portion of their parameter budget to multilingual support. For the vast majority of users working in a single language like English, those weights are essentially dormant during inference, yet they still consume real VRAM and storage.

By surgically pruning the expert layers tied to multilingual functionality, developers can meaningfully lower the barrier to local deployment without noticeably degrading performance in their target language. For local inference enthusiasts, going from 711GB to 478GB translates directly to lower hardware costs and a much more realistic path to actually running the model.

The post also raised a compelling follow-up question: if this approach were applied to models like Qwen 3.8 MAX or DeepSeek V4 Flash, how much space could be saved? This could become an important optimization direction for how the open-source community distributes large models.

Benchmark Results: Evaluating the 478GB Version's Coding Ability

The post included detailed testing notes from a model editor who used SWE-Lancer benchmarks — specifically the "task selection" and "per-task" evaluation modes — to assess coding performance.

The IQ2-XXS Trimmed Version Actually Passed the Coding Tests

The tester compared several quantized variants:

• k27_q2_2bit (2-bit version of Kimi K2.7)
• reap640_iq1s
• reap576_iq2xxs (the 478GB trimmed version)

Since none of these models fit entirely in Mac RAM, the tester used a clever workaround: streaming expert models from SSD in real time via llama.cpp's MoE streaming feature. Three SWE-Lancer tasks were used (IDs 14294 / 15815_1 / 15925).

The results were surprising: the standard Kimi-K3-REAP-512GB version "completely failed" all three tasks, while reap576_iq2xxs (478GB) — derived from the same base weights with additional pruning — successfully solved all three.

Could Pruning Expert Layers Actually Improve Performance on Certain Tasks?

The tester approached this unexpected result with appropriate scientific caution. Their first instinct was to blame benchmark timeouts — streaming decoding is slow, averaging around 2.5 hours per task — but the logs showed no timeout events.

Since these were single-run trials, the most likely explanation is some task-specific quirk in the test environment. That said, the tester left open an intriguing hypothesis: pruning certain expert weights might, in rare cases, actually improve coding performance.

If this holds up under scrutiny, it would be worth investigating seriously — it could suggest that some expert layers aren't just neutral for certain tasks, but actively introduce noise that hurts performance.

Verification Is Expensive: A Call for Community Help

To reach a reliable conclusion, the tester plans to rent a machine with sufficient VRAM and run a controlled comparison between pruned and unpruned versions under identical conditions. The obstacle is cost: even using RunPod for cloud compute, running the full SWE-Lancer benchmark suite is estimated at $1,800.

So the post doubles as a community call for help: if anyone has a machine capable of running the full 2-bit version, they're hoping someone can reproduce the tests. The comparison data (k27_q2_2bit vs reap640_iq1s vs reap576_iq2xxs) is already publicly available in a GitHub repo for anyone who wants to dig in.

Targeted Pruning: A Viable Path for Local LLM Deployment

This experiment is a good example of the open-source AI community at its best — from hellohazine's original pruning idea, to Hannibalj2ca's suggestion of the IQ2-XXS approach, to the tester's meticulous benchmark notes and honest request for help. Each step pushes the boundaries of what's possible with model compression and local deployment.

While "pruning expert layers improves coding performance" remains a hypothesis that needs more rigorous testing, the experiment does establish one thing clearly: targeted, use-case-specific pruning is a viable way to lower the barrier to deploying large models. For developers who want to run frontier models on consumer or workstation hardware, this is a direction worth watching.

Model link: huggingface.co/hellohazime/Kimi-K3-REAP-512GB-GGUF Test data: github.com/01554/kimi-k3-gguf-prune