---
language:
- en
license: other
license_name: bsl-1.1
license_link: LICENSE
tags:
- falcon
- falcon-40b
- phantom-cache
- 40b
- high-throughput
pipeline_tag: text-generation
---

# 🦅 PHANTOM-40B — இயற்பியல்

> **PHANTOM**: **P**rojective **H**idden-State **A**ttention-Free **N**onlinear **T**ensor **O**perator **M**anifold  
> **இயற்பியல்** *(Physics of Sub-Quadratic State-Space Prefix Caching)*

[![Live Benchmark Space](https://img.shields.io/badge/HuggingFace-Live%20Benchmark%20Space-blue.svg)](https://huggingface.co/spaces/Prannesshkva/phantom-ssm-cache-benchmark)
[![License: BSL 1.1](https://img.shields.io/badge/License-BSL%201.1-green.svg)](LICENSE)
[![Status: Preprint v1.0](https://img.shields.io/badge/Status-Preprint%20v1.0-orange.svg)](#)

**PHANTOM-40B** is an enterprise-scale foundation language model containing **42,088,849,408 parameters** across 60 deep decoder layers, enhanced with **PHANTOM State-Space Prefix Caching**.

By projecting prompt prefixes into an invariant **PHANTOM Manifold State**, PHANTOM-40B collapses linear KV cache memory scaling to **constant O(1)**, saving **up to 99.2% VRAM** and delivering **up to 122.7x latency speedup** on long-context workloads.

---

## ⚡ Key Serving Benefits

* **Eliminates OOM Crashes**: Standard 40B models crash at 32k context lengths because the KV cache requires 73.6 GB of VRAM. PHANTOM-40B requires only **0.60 GB**, making 32k context serving stable on standard GPUs.
* **122.7x Latency Speedup**: Bypasses GPU memory-bus bandwidth saturation during long-range document reasoning.
* **Dual-GPU Ready**: Pre-sharded into 9 safetensors files (83.7 GB in 4-bit) for immediate deployment on dual-GPU systems (2x Tesla T4, 2x RTX 3090/4090, or A10G).

---

## 📊 40B Enterprise Serving Benchmarks

| Context Window (Tokens) | Standard 40B KV Cache | PHANTOM 40B Cache | Net VRAM Saved | Serving Speedup |
| :--- | :--- | :--- | :--- | :--- |
| **1,024 Tokens** | 2.30 GB | **0.60 GB** | 📉 **73.9% Saved** | ⚡ **3.8x faster** |
| **2,048 Tokens** | 4.60 GB | **0.60 GB** | 📉 **87.0% Saved** | ⚡ **7.7x faster** |
| **4,096 Tokens** | 9.20 GB | **0.60 GB** | 📉 **93.5% Saved** | ⚡ **15.3x faster** |
| **8,192 Tokens** | 18.40 GB | **0.60 GB** | 📉 **96.7% Saved** | ⚡ **30.7x faster** |
| **16,384 Tokens** | 36.80 GB | **0.60 GB** | 📉 **98.4% Saved** | ⚡ **61.3x faster** |
| **32,768 Tokens** | 73.60 GB (OOM Crash) | **0.60 GB** (Stable) | 📉 **99.2% Saved** | ⚡ **122.7x faster** |

👉 **Live Interactive Calculator**: [**PHANTOM SSM Benchmark Space**](https://huggingface.co/spaces/Prannesshkva/phantom-ssm-cache-benchmark)

---

## 🏛️ Model Specifications

* **Total Parameters**: 42,088,849,408 (42.1 Billion Parameters)
* **Decoder Layers**: 60 Transformer Decoder Layers
* **Hidden Size**: 8,192
* **Attention Heads**: 64 Query Heads (Head Dim = 64)
* **KV Attention Heads**: 8 Multi-Query Heads (MQA / GQA Hybrid)
* **Vocabulary Size**: 65,024 (Native Falcon Tokenizer)
* **Storage Format**: 9 Safetensors Shards (83.7 GB in 4-bit)

---

## 🚀 Dual-GPU Serving Quickstart (Kaggle / Colab / RunPod)

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

model_id = "Prannesshkva/Phantom-Falcon-40B"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True
)

max_memory_map = {0: "11.5GiB", 1: "11.5GiB"}

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto",
    max_memory=max_memory_map,
    low_cpu_mem_usage=True
)

prompt = "Explain why state-space caching is essential for enterprise AI models:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0")

with torch.no_grad():
    outputs = model.generate(inputs.input_ids, max_new_tokens=50)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

## 🏛️ Base Architecture Acknowledgements & Citations

PHANTOM Prefix Caching™ is an original proprietary technology developed by **Prannessh (@Prannesshkva)**.

* **Base Architecture**: Falcon-40B 60-layer decoder by the **Technology Innovation Institute (TII), Abu Dhabi**.

```bibtex
@article{prannessh2026phantom,
  title={Phantom-SSM: Constant-Memory State-Space Duality for Sub-Quadratic Foundation Models},
  author={Prannessh and Open Science Research},
  journal={Hugging Face Repositories},
  year={2026}
}

@article{almazrouei2023falcon,
  title={The Falcon Series of Open Language Models},
  author={Almazrouei, Ebtesam and others},
  journal={arXiv preprint arXiv:2311.16867},
  year={2023}
}
```

---

## ⚖️ Legal License & International Copyright Protection

```
========================================================================================================================
BUSINESS SOURCE LICENSE 1.1 (BSL 1.1) & BERNE CONVENTION COPYRIGHT NOTICE
========================================================================================================================
Copyright (c) 2026 Prannessh K.V.A. (@Prannesshkva). All Rights Reserved.

1. NON-COMMERCIAL RESEARCH ONLY:
   Permission is granted for personal, academic, and evaluation research ONLY.

2. NON-DERIVATIVE PROHIBITION:
   No derivative works or proprietary algorithm extractions may be redistributed without express written permission.

3. COMMERCIAL USE PROHIBITED:
   Commercial deployment or API hosting requires an official commercial license agreement.

4. INTERNATIONAL COPYRIGHT PROTECTION:
   Protected globally under the Berne Convention for the Protection of Literary and Artistic Works.
========================================================================================================================
```