argus-cache 0.1.6

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
        │
        ▼
       INT8
        │
        ▼
       INT4 (2-way Bit-Packed)
        │
        ▼
       INT2 (4-way Bit-Packed)
        │
        ▼
      1-Bit (8-way Sign-Packed)
        │
        ▼
 JL Archive (Deep Orthogonal Projection)
        │
        ▼ (VRAM Limit Crossed)
 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 guarantees maximum semantic fidelity and preserves 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.

💡 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!
• The Result: You get stable, seamless, long-context conversations on a 4GB Laptop GPU. ARGUS delivers 98.1% cache reuse efficiency (Attention Locality) and completely avoids the dreaded allocation OOMs.

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📺 Telemetry Showcase (Research Mode)

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: Fraction of resurrected pages reused by subsequent attention windows (distinguishes temporal access locality from standard static 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=2048,          # Page block length
    max_active_pages=2,      # Active FP16 pages in transient pool
    max_fp8_pages=2,         # Warm FP8 pages
    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))

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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:

• 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.
• Predictor Not Production-Ready: The predictive attention paging module is currently highly experimental and not ready for stable deployment.

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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.