Kv-cache

ReasoningKVC: Block-Aware Attention Mask KV Compression for LLM Reasoning Chains

For xAI Colossus — 40% KV masked, ΔPPL +0.02, latency even better than baseline, savings up to $51M/month on 100K H100.

Results (T4 GPU, March 10, 2026)

Compression: 40% KV masked (pruned/zeroed)

ΔPPL: +0.0200 (target < 0.05)

Latency overhead: from -43.6% to -0.5% (speedup)

Throughput: +80–130% (1625 → 3803 tokens/sec)

Tests: 4K / 8K / 12K (ready for 16K–32K)

Method: Attention mask + structure-aware block detection (perplexity + keyword boundaries)

Business impact (estimate)

5K H100 → $2.59M/month

20K H100 → $10.37M/month

100K H100 (xAI Colossus) → $51.84M/month

How to use git clone https://github.com/your-username/reasoning-kvc-attention-mask.git cd reasoning-kvc-attention-mask

Block-aware KV cache compression for long reasoning chains in LLM inference.

Key Idea

Reasoning chains contain natural structural boundaries:

Step 1 → Step 2 → Verification → Conclusion

ReasoningKVC detects these blocks and selectively prunes low-importance KV tokens while protecting anchors and recent blocks.

This reduces KV-cache size while preserving reasoning quality.

Results

Model: Qwen2.5-1.5B
Hardware: Tesla T4 (Google Colab)

Sequence	Tokens	KV Masked	ΔPPL
~4K	4096	~38%	+0.01
~8K	8192	~41%	+0.02
~12K	12288	~40%	+0.01

Quality preserved (ΔPPL < 0.05).

Potential Impact

Long-context reasoning models (e.g. Grok-style think traces) store massive KV caches.

Example (70B model):

32K tokens KV cache ≈ 18GB
40% compression → 7.2GB saved per request.

This increases batch capacity and reduces GPU inference cost.

Colab Benchmark

Run the full benchmark here:

https://colab.research.google.com/drive/1lltRreClnmzQFN8W6UoreA8Pggohwq2q#scrollTo=rbQdtDeuoR87

Report

Full benchmark PDF included in this repo.

Algorithm Overview

1. Hybrid segmentation

   • regex transitions
   • perplexity spike detection

2. Block-aware pruning

   • protect recent reasoning blocks
   • anchor tokens preserved
   • importance threshold

3. Safe masking

   • attention_mask zeroing
   • no positional mismatch
   • no retraining required

Integration Target

This method is designed to integrate with inference engines such as:

• vLLM
• TensorRT-LLM
• DeepSpeed-Inference

License

MIT