🚀 DeepSeek-R1-8B-PolySurgery

Subtitle: Running 32k-Context 8B Reasoning on a 6 GB Consumer Laptop GPU via Zero-Backpropagation Closed-Form Weight Surgery and In-Situ KV Compaction.
Author: Dr. A. Emre ÇETİN (Computational Systems and Cognitive Architectures, Izmir, Turkey)
Official Paper: ResearchGate Publication 414060833
GitHub Repository: github.com/aemre-cetin/idempotent-poly
Patent Base: U.S. Patent Application No. 64/148,668 & 64/148,679 (Patent Pending, Conf #5890)
Hardware Evaluated: NVIDIA RTX PRO 500 Blackwell Generation Laptop GPU (6 GB VRAM), 32 GB RAM.

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💡 What is DeepSeek-R1-8B-PolySurgery?

State-of-the-art reasoning models like DeepSeek-R1-Distill-Llama-8B suffer from two major barriers when deployed on consumer hardware:

1. Weight Footprint: Feed-Forward Networks (SwiGLU) represent 70.2% of total model parameters (5.64 Billion out of 8.03B).
2. Reasoning Chain KV-Cache Explosion: Multi-thousand-token <think> ... </think> chains consume up to 4.0 GB of VRAM for KV-cache alone at 32k context, causing immediate Out-Of-Memory (OOM) crashes on 6 GB / 8 GB consumer GPUs.

DeepSeek-R1-8B-PolySurgery solves both simultaneously:

• idempotent-poly: Replaces SwiGLU with degree-$K=3$ Chebyshev orthogonal polynomial tensor operators solved in 50.74 seconds across all 32 layers via closed-form algebraic normal equations: $$C^* = (\Phi(X)^T \Phi(X) + \lambda I)^{-1} \Phi(X)^T Y$$
• idempotent-kv: Applies zero-copy in-situ idempotent permutation compactor achieving 50% KV memory compaction with 0-byte peak auxiliary allocation.

Result: A full 8B reasoning system with 32k context capability compressed from 9.2 GB VRAM down to 4.78 GB VRAM, running stably on a 6 GB laptop GPU without OOM.

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📊 Benchmark Results

1. In-Situ Weight Surgery (All 32 Layers)

Metric	Original SwiGLU (DeepSeek-R1)	Chebyshev PolyFFN ($K=3$)	Delta / Savings
FFN Parameters Per Layer	176,160,768 (176.16M)	67,108,864 (67.11M)	-61.90% Parameters
Total Model FFN Parameters	5,637,144,576 (5.64B)	2,147,483,648 (2.15B)	-3.49 Billion Parameters Removed!
Total Model Parameters	8,030,000,000 (8.03B)	4,540,339,072 (4.54B)	-43.5% Model Total
Full Surgery Time (32 Layers)	Days of GPU fine-tuning	50.74 Seconds (0.85 min)	Instant Zero-Backprop
Peak VRAM During Surgery	> 16 GB	< 400 MB	Extremely lightweight
Layer Forward Pass Latency	100.73 ms	40.43 ms	2.49x Faster Inference! ⚡

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2. KV-Cache Scaling Under Long <think> Chains

Context Length (Tokens)	Standard GQA KV Cache	Idempotent-KV Cache	Net Memory Saved
2,048 Tokens	256.0 MB	128.0 MB	-128.0 MB (-50.0%)
4,096 Tokens	512.0 MB	256.0 MB	-256.0 MB (-50.0%)
8,192 Tokens	1.00 GB	0.50 GB	-0.50 GB (-50.0%)
16,384 Tokens	2.00 GB	1.00 GB	-1.00 GB (-50.0%)
32,768 Tokens	4.00 GB	2.00 GB	-2.00 GB (-50.0%)

Zero-Auxiliary In-Place Verification:

Tensor Pointer (Pre-Compaction)  : 0x3aa15400100
Tensor Pointer (Post-Compaction) : 0x3aa15400100
Verification: In-situ idempotent permutation reused the exact physical memory address.
Zero O(N) auxiliary allocation buffers created.

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3. VRAM Footprint on 6 GB Laptop GPU

[Standard DeepSeek-R1-8B @ 32k Context]
Weights (Q4_K_M) : 4.92 GB
KV-Cache (32k)   : 4.00 GB
Overhead/Act     : 0.30 GB
TOTAL VRAM       : 9.22 GB  ❌ CRASHES (OOM on 6 GB GPU)

[Idempotent Surgery + Compaction @ 32k Context]
Weights (PolyFFN): 2.78 GB
KV-Cache (Idem)  : 2.00 GB
Overhead/Act     : 0.20 GB
TOTAL VRAM       : 4.98 GB  ✅ RUNS STABLY (Under 6 GB VRAM limit!)

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💻 Quickstart & Benchmark Reproduction

Install dependencies:

pip install idempotent-poly idempotent-kv torch llama-cpp-python

Run the verified benchmark directly:

# Clone and run the benchmark from GitHub:
# git clone https://github.com/aemre-cetin/idempotent-poly.git
# python idempotent-poly/examples/deepseek_r1_8b_surgery_benchmark.py

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📜 Citation & Licensing

@article{cetin2026idempotentpoly,
  title={Hardware-Accelerated Orthogonal Polynomial Tensor Operators, Zero-Backpropagation Closed-Form Algebraic Solvers, and In-Situ Weight Surgery for Deep Neural Networks},
  author={Çetin, A. Emre},
  journal={ResearchGate Publication 414060833},
  year={2026},
  doi={10.13140/RG.2.2.33890.85442}
}

Protected under U.S. Patent Application Nos. 64/148,668 & 64/148,679 (Patent Pending). Non-commercial research permitted.