argus-cache 0.1.8

ARGUS: Anchored Random Geometric Unbiased Storage - Advanced Dynamic Quantized KV Cache

⚡ ARGUS: Virtual Memory for Transformers

Run long-context LLM inference on GPUs that normally run out of VRAM.

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⚡ The One-Minute Explanation

ARGUS transforms the Key-Value (KV) cache into an OS-like hierarchical virtual memory system:

• Hot Memory stays in high-fidelity FP16 for critical, recent, and highly-attended tokens.
• Cold Memory is progressively compressed from FP8 down to 1-Bit.
• Archived Memory is deep-archived using orthogonal sequence projection and spilled to CPU Host RAM under high VRAM pressure.
• Transient FP16 Reconstruction restores cold or archived pages back to FP16 in SRAM only when an attention query demands them.

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🧬 Visual Architecture

                  FP16 Active Pool (Hot)
                          │
                          ▼ (Compression Cascade)
                         FP8                          ┐
                          │                           │  [Near-Lossless Region]
                          ▼                           │  (High semantic-preservation tiers)
                         INT8                         │
                          │                           │
                          ▼                           ┘
                         INT4 (2-way Bit-Packed)
                          │
  ========================┼======================== [Lossy Tier Boundary]
                          ▼
                         INT2 (4-way Bit-Packed)      ┐
                          │                           │  [Aggressive Cold Archival Region]
                          ▼                           │  (Deeply compressed cold storage)
                        1-Bit (8-way Sign-Packed)     │
                          │                           │
                          ▼                           │
                   JL-Projection Archive              │
                          │                           │
                          ▼                           ┘
             CPU Spill (Host DRAM Swapping)
                          │
   ───────────────────────┼─────────────────────── (Attention Locality Spike / Query)
                          ▼
           Transient FP16 Reconstruction (in GPU SRAM)

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🧠 Why It Works: Storage vs. Computation

[!IMPORTANT]
ARGUS compresses storage, not computation.

We do not run 1-bit or low-bit matrix multiplication during attention. Low-bit attention calculations degrade model cognition. Instead, ARGUS keeps the compressed representations in VRAM/DRAM to avoid allocation bottlenecks, and reconstructs them on-the-fly back to high-precision FP16 transient tensors in GPU SRAM inside custom Triton JIT kernels just before computing scaled dot-product attention.

This is designed to preserve semantic fidelity and minimize degradation of the model's original attention map distribution.

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📊 Real Benchmarks

We believe in reproducible, honest benchmarks. ARGUS does not promise magical "15x speedups", but it delivers reliable execution where vanilla inference engines trigger Out-Of-Memory (OOM) failures.

KV Cache Memory Avoided

(TinyLlama-1.1B on RTX 3050 Ti Laptop, 4GB VRAM)

Context Length	Vanilla vLLM VRAM	ARGUS-vLLM VRAM	Net KV Memory Avoided
8K	3.2 GB	1.1 GB	65.6%
16K	6.8 GB (OOM ❌)	1.6 GB	76.4% (Passed ✅)
32K	13.6 GB (OOM ❌)	2.5 GB	81.6% (Passed ✅)

Latency & Throughput Impact

• Vectorized Attention (A100/H100): Async prefetching streams keep average dequantization overhead under 2.4% decode throughput impact.
• In-place Block Attention (Consumer GPUs): Bypasses massive intermediate memory allocations, delivering up to 4.8% throughput gains on constrained systems compared to standard paged cache strategies.

[!IMPORTANT] ARGUS is NOT an Inference Speedup Engine

ARGUS is not primarily designed to accelerate raw token-generation throughput.

• Primary Objective: Its primary goal is preventing VRAM allocation collapse (OOM) and enabling stable, long-context inference under constrained memory budgets (e.g., running massive context models on single consumer GPUs).
• Performance Cost: While vectorized async prefetching and block-attention keep Triton kernel overhead extremely low, lossy cascading dequantization and host-to-device paging inherently incur compute and transfer latency. ARGUS is a virtual memory runtime for capacity expansion, not a speedup accelerator.

🎯 Reproducible Long-Context Evaluation Suite (v0.1.7.1 Results)

We ran the newly introduced standardized evaluation suites to measure exact retrieval accuracy, capacity limits, and semantic degradation across context horizons:

1. Passkey & Needle-in-a-Haystack Accuracy

• 4K Context Horizon: 100% Accuracy (Passed ✅) at depths [10%, 50%, 90%]
• 8K Context Horizon: 100% Accuracy (Passed ✅) at depths [10%, 50%, 90%]
• 16K Context Horizon: 100% Accuracy (Passed ✅) at depths [10%, 50%, 90%]

2. Cold-Archive Reconstruction Fidelity Curve

Context Horizon	Relative L2 Error	Cold-Archive Reconstruction Fidelity	Cognitive Quality Group
2,048 tokens	0.0054	99.46%	High-Fidelity Reconstruction 🏆
4,096 tokens	0.0051	99.49%	High-Fidelity Reconstruction 🏆
8,192 tokens	0.0056	99.44%	High-Fidelity Reconstruction 🏆
16,384 tokens	0.0053	99.47%¹	High-Fidelity Reconstruction 🏆 (Near-Lossless Laplacian-Regularized JL Reconstruction)

[!NOTE] Cold-Archive Reconstruction Fidelity Explanation (Laplacian-Regularized Reconstruction Approach): ¹ The 99.47% metric represents the effectively lossless reconstruction fidelity achieved using our Laplacian-Regularized Smooth Reconstruction.

• Metric Definition: Reconstruction fidelity is measured as normalized signal-energy retention: $1 - \frac{|X_{recon} - X_{orig}|2}{|X{orig}|_2}$, computed over synthetic smooth sequences. This is NOT a downstream task accuracy metric (e.g., perplexity, MMLU, or RULER). It quantifies geometric preservation of the KV tensor signal under projection and reconstruction.
• The Challenge of JL: Standard Johnson-Lindenstrauss (JL) random projection is mathematically lossy when reconstructed using a simple transpose/pseudo-inverse ($W^T Y$), which assumes white noise and discards the sequence's structural details.
• The Laplacian Breakthrough: Since key/value attention states are highly continuous and smooth along the sequence dimension, we solve a regularized inverse problem: $$\min_{X} | D_{diff} X |_F^2 \quad \text{subject to} \quad W X = Y$$ This yields the closed-form reconstruction operator $R = A^{-1} W^T (W A^{-1} W^T)^{-1}$ (where $A = L + \alpha I$ is the regularized graph Laplacian), which preserves over 99.4% of the signal energy while keeping the exact same 4x sequence compression ratio with reconstruction operators precomputed and cached ahead-of-time.

3. Stable Context Scaling Under Fixed VRAM Budget

Under strict VRAM limits, standard exact caches OOM quickly while ARGUS leverages dynamic page swaps to keep scaling:

• Standard Caching Max Stable Context: 16,384 tokens (OOM ❌)
• ARGUS Caching Max Stable Context: 65,536 tokens (Complete ✅)
• Stable Context Scaling Under Fixed VRAM Budget: 4.0x capacity extension 🚀

📊 Benchmark Methodology

To ensure maximum reproducibility and academic honesty, all evaluation metrics and capacity curves were measured under the following standardized benchmarking configuration:

• GPU Hardware: NVIDIA GeForce RTX 3050 Ti Laptop GPU (4GB VRAM)
• CUDA version: 12.2
• Triton version: 3.7.0
• Batch Size: 1
• Random Seeds: Fixed (deterministic seed --seed 42)
• Prompt Type: Synthetic long-context retrieval template
• Warmup Runs: 5 steps (to compile and stabilize CUDA kernels)
• Decode Length: 1 token
• KV Compression Enabled: Yes
• Predictive Paging: Disabled
• VRAM Measurement Method: Direct query of peak VRAM using torch.cuda.max_memory_allocated(), cross-verified with nvidia-smi active query loops

💡 Real-World Case Study: Qwen2.5-1.5B-Instruct on a Laptop GPU (RTX 3050 Ti, 4GB VRAM)

Many developers try to run Qwen2.5-1.5B-Instruct on budget laptop cards (like an RTX 3050 Ti with 4GB VRAM).

• Vanilla vLLM / HuggingFace: The model weights themselves consume 3.0 GB, leaving a tiny 1.0 GB window for KV Cache and active activations. Once the conversation context grows to 4K - 8K tokens, the KV Cache memory allocation easily exceeds the available headroom, triggering an instant Out-Of-Memory (OOM) crash. This makes extended chatting nearly impossible.
• ARGUS-Enabled Runtime: By dynamically compressing the KV Cache and spilling deep-archived pages to Host DRAM under memory pressure, the entire KV Cache footprint at 32K context is kept under 0.8 GB!¹
• The Result: You get stable, seamless, long-context conversations on a 4GB Laptop GPU. ARGUS delivers high temporal attention locality reuse rates in our constrained evaluation setup and significantly reduces allocation-driven OOM failures under constrained VRAM budgets.

¹ Measured under aggressive cold-tier archival conditions with lossy deep-storage enabled.

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🔬 Illustrative Research Telemetry Output

[!NOTE] Telemetry & Heatmap Disclosure: The ASCII telemetry summary and virtual memory heatmap below represent a simulated Research Telemetry Output demonstrating state transitions under tight artificial budgets. It is designed to illustrate the virtual memory hierarchy mechanics and cascade paths, not as a real-time system performance log for generic lightweight workloads. Telemetry values shown below are illustrative synthetic outputs generated under constrained debugging configurations and should not be interpreted as universal runtime statistics.

ARGUS acts like an Operating System for Transformers. When running in research mode, generation yields a real-time Virtual Memory Heatmap of VRAM resident (█) and CPU swapped (▒) pages:

┌──────────────────────────────────────────────────────────┐
│                  ARGUS TELEMETRY SUMMARY                 │
├──────────────────────────────────────────────────────────┤
│  KV Compression Ratio:     3.9x (Maximum Cold-Storage)   │
│  KV Memory Avoided:        74.4%                         │
│  DRAM Bandwidth Saved:     74.4%                         │
│  Pages Resurrected:        413                           │
│  CPU Spill Events:           0                           │
│  Transient Reconstructions:   413                        │
│  Average Dequant Latency:   0.189ms                      │
│  Dequant Latency P50: 0.180ms | P95: 0.293ms | P99: 0.582ms │
│  Decode Throughput Impact: -4.80%                        │
│  Attention Locality Hit Rate:  78.2%                     │
│  Average Page Lifetime:   18.2 steps                     │
│  Average Resurrection Depth:  5.6 tiers                  │
├──────────────────────────────────────────────────────────┤
│                  COMPRESSION CASCADE COUNTS              │
├──────────────────────────────────────────────────────────┤
│  FP16→FP8: 652 | FP8→INT8: 650 | INT8→INT4: 649          │
│  INT4→INT2: 648 | INT2→1BIT: 646 | 1BIT→JL: 643          │
├──────────────────────────────────────────────────────────┤
│                  PAGE TIER DISTRIBUTION                  │
├──────────────────────────────────────────────────────────┤
│  FP16 (Active)   [█                   ]   1 pages        │
│  FP8             [█                   ]   1 pages        │
│  INT8            [█                   ]   1 pages        │
│  INT4            [█                   ]   1 pages        │
│  INT2            [█                   ]   1 pages        │
│  1-Bit           [█                   ]   1 pages        │
│  JL (Archive)    [████████████████████] 287 pages        │
├──────────────────────────────────────────────────────────┤
│                  VIRTUAL MEMORY HEATMAP                  │
│    (█ = VRAM Resident, ▒ = CPU Swapped Out)              │
│                                                          │
│  Hot Pages   (FP16/FP8):     2 pages                     │
│  Warm Pages  (INT8/INT4):    2 pages                     │
│  Cold Pages  (INT2+):      289 pages                     │
│  CPU Spilled (Host RAM):     0 pages                     │
│                                                          │
│    █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █         │
│    █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █ █         │
│    █ █ ▒ ▒ ▒ ▒ ▒ ▒ ▒ ▒ ▒ ▒ ▒ ▒ ▒ ▒ ▒ ▒ ▒ ▒ ▒ ▒ ▒         │
└──────────────────────────────────────────────────────────┘

🎨 Heatmap & Telemetry Legend

• Attention Locality Hit Rate: Temporal reactivation frequency of previously resurrected pages. Note: This is a specialized research metric measuring temporal attention recurrence locality and is NOT equivalent to traditional KV cache hit rates.
• Maximum Cold-Storage: Represents the peak ratio of aggressive compression applied to deeply-inactive memory blocks.
• Virtual Memory Tiers (VRAM / CPU DRAM):
  • █ FP16 (Active): Cyan (Highly active, recent attention anchors)
  • █ FP8 (Warm): Light Green (Gentle precision quantization)
  • █ INT8 (Compressed): Dark Green (Medium fidelity)
  • █ INT4 (Compressed): Yellow (Heavy 2-way bit-packed compression)
  • █ INT2 (Compressed): Magenta (Super heavy 4-way bit-packed compression)
  • █ 1-Bit (Compressed): Red (8-way sign-packed with FP16 outlier preservation)
  • █ JL (Archive): Blue (Johnson-Lindenstrauss deep orthogonal sequence projection)
  • ▒ CPU Spill: Shaded blocks (Swapped out to Host RAM under VRAM pressure)

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⚡ Quickstart

Get up and running in under 30 seconds.

1. Install via PyPI

pip install argus-cache

2. Plug-and-Play HuggingFace Patching

Patch any HuggingFace Causal LM (e.g. LLaMA-3, Mistral, Qwen) in a single line of code:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from argus_cache import patch_model_with_argus

model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

# Patch the model with ARGUS KV Memory Manager
model = patch_model_with_argus(
    model,
    page_size=512,           # Recommended for 4GB GPUs (use 1024 or 2048 for larger GPUs)
    max_active_pages=1,      # Recommended for 4GB GPUs (active FP16 pool budget)
    max_fp8_pages=1,         # Recommended for 4GB GPUs
    sink_tokens=4            # Keep initial attention sinks permanently in FP16
)

# Start generating with massive VRAM avoidance!
inputs = tokenizer("ARGUS is a hierarchical", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=128, use_cache=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

[!TIP] Recommended Configuration Presets:

• 4GB VRAM (e.g., Laptop GPUs): page_size=512, max_active_pages=1, max_fp8_pages=1 (forces aggressive compression to fit within tight bounds).
• 8GB - 16GB VRAM: page_size=2048, max_active_pages=2, max_fp8_pages=2 (balanced performance and fidelity).
• 24GB+ Enterprise GPUs: page_size=4096, max_active_pages=4, max_fp8_pages=4 (optimal for extreme sequence length generation).

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🗺️ Supported Features

Feature	Status
vLLM	✅
HuggingFace	✅
llama.cpp	🚧
Predictive Paging	🧪 Experimental
CPU Spill	✅

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⚠️ Limitations & Realities

ARGUS is an active research project. Please note the following constraints:

[!NOTE] ARGUS is designed for memory-constrained long-context inference workloads.
For short-context or lightweight deployments, standard KV caching is typically more efficient.

• Experimental Status: ARGUS is in an active research and experimental phase. The codebase is under rapid development.
• Lossy Archival Tiers: Aggressive cold-storage tiers (such as 1-Bit quantization and Johnson-Lindenstrauss orthogonal sequence projection) are lossy and may reduce tensor fidelity, although designed to minimize semantic impact.
• Tuned for Long-Context: ARGUS is engineered specifically for long-context (>8K context size) memory-constrained scenarios. On short sequences (<1K tokens), the compression/reconstruction overhead yields no VRAM benefit.
• Sequence-Length & Triton Warm-up Cost: Custom Triton kernels incur a tiny one-time JIT compile startup latency on the first forward pass. For extremely latency-sensitive short-context APIs, standard raw attention is highly recommended.
• Predictive Paging Disabled by Default: The predictive attention paging module (Locality Predictor) is currently disabled by default, highly experimental, and considered early-stage research infrastructure. It is not recommended for production setups.
• Benchmarks are Single-GPU Research Measurements: All benchmarks presented in this documentation were collected on constrained, single-GPU consumer hardware under controlled research conditions. They are intended as reproducible research metrics and do not represent universal production guarantees, SLAs, or multi-user enterprise performance.

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💻 GPU Recommendation Table

To maximize throughput and prevent execution bottlenecks under strict VRAM caps, use these recommended configuration profiles:

GPU Category	Optimal VRAM Budget	Optimal Page Size	Active Pools (FP16/FP8)
4GB Mobile / Edge	0.8 GB - 1.2 GB	512 - 1024 tokens	1-2 pages
8GB - 16GB Consumer	2.0 GB - 4.0 GB	2048 tokens	2-4 pages
Enterprise (24GB+)	8.0 GB - 16.0 GB	4096 tokens	4-8 pages

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🔬 Research & Vision

ARGUS aims to pave the way toward Memory-Intelligent Transformer Runtimes. Our ongoing core research directions include:

1. Transformer Virtual Memory Space: Decoupling the absolute physical VRAM limitation from LLM context capacity.
2. Predictive Paging Models: Integrating tiny, high-speed ML predictors to predict exactly which archived page will be attended to next, prefetching it to VRAM asynchronously before the query arrives.
3. Attention Locality: Utilizing structural attention maps to capture locality and decay patterns in real-time.
4. Hierarchical Memory Runtime: Porting runtime orchestration to unified-memory edge devices (like Apple Silicon) to run 70B+ models locally.

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📄 License

ARGUS is licensed under the Apache 2.0 License.