DESIGN.md

GuiXi (龟息) - Bandwidth-Efficient LLM Inference Framework

1. Concept & Vision

GuiXi (龟息) means "turtle breathing" - the art of slow, steady, minimal respiration. Just as a turtle can endure long periods with minimal breath, GuiXi enables AI model inference and training over ordinary networks with drastically reduced bandwidth requirements.

The core philosophy: Inference doesn't have to be bandwidth-hungry. Through intelligent compression, semantic caching, delta synchronization, and protocol optimization, GuiXi achieves up to 10-100x bandwidth reduction without sacrificing response quality.

Target Users:

• Developers deploying LLMs in bandwidth-constrained environments
• Edge AI applications with limited connectivity
• Multi-node training across geographic regions
• Mobile apps integrating LLM capabilities

2. Technical Architecture

2.1 Core Components

GuiXi Architecture
├── guixi/
│   ├── __init__.py           # Version and exports
│   ├── __main__.py           # Entry point
│   ├── cli.py                # CLI interface
│   ├── gui.py                # PySide6 GUI
│   ├── app.py                # Flask web app
│   ├── api.py                # Python API
│   ├── core.py               # Business logic
│   ├── compress.py           # Compression engine
│   ├── cache.py              # Semantic cache
│   ├── protocol.py           # Binary protocol
│   └── sync.py               # Delta sync
├── templates/                 # Web templates
├── data/                     # Sample data
└── tests/                    # Test suite

2.2 Bandwidth Reduction Techniques

2.2.1 Token Stream Compression

Real-time compression of streaming tokens using LZ4/zstd:

Raw Token Stream: [3421, 8923, 1042, 5678, ...]  (4 bytes each)
Compressed:       0x1A4B8C2D...                   (variable length)

Compression Ratios:

• LZ4: 2-4x ratio, <1ms latency
• Zstd: 4-8x ratio, 2-5ms latency

2.2.2 Semantic Cache

Cache similar prompts using embedding-based similarity:

# Embed prompt → search cache → if similarity > 0.95, return cached
# Cache structure: (embedding_vector) → response_tokens

Cache Hit Scenarios:

• Exact match: 100% bandwidth savings
• Near duplicate (>95% similarity): 90%+ savings
• Semantic similarity (>80%): 50%+ savings

2.2.3 Delta Synchronization

Only transmit changes between states:

Client State: [A, B, C, D, E]
Server State: [A, B, C, X, Y, Z]
Delta:        [+X, +Y, +Z, -D, -E]

2.2.4 Quantized Token Encoding

Reduce token representation precision:

Encoding	Bits/Token	Range	Use Case
int8	8	-128 to 127	Small vocabularies
int16	16	-32K to 32K	Standard vocab
int32	32	-2B to 2B	Large vocab
Huffman	variable	~4 avg	Frequency-optimized

2.2.5 Adaptive Batching

Dynamic batch sizing based on network conditions:

# Network good → larger batches (more throughput)
# Network poor → smaller batches (lower latency)
# Algorithm: AIMD (Additive Increase Multiplicative Decrease)

2.3 Protocol Design

Binary protocol over WebSocket for minimum overhead:

Header (8 bytes):
  - Magic: 0x47585849 ('GXXI')
  - Version: uint8
  - Flags: uint8 (compression, encryption, etc.)
  - Length: uint32 (payload length)

Payload:
  - Compressed token stream or delta updates

2.4 Data Models

@dataclass
class TokenStream:
    tokens: List[int]
    compression: str  # 'none', 'lz4', 'zstd'
    timestamp: float

@dataclass
class CacheEntry:
    embedding: np.ndarray
    response: TokenStream
    prompt_hash: str
    created_at: float
    access_count: int

@dataclass
class DeltaUpdate:
    additions: List[int]
    deletions: List[int]
    position: int

@dataclass
class InferenceRequest:
    prompt: str
    max_tokens: int
    temperature: float
    compression: str
    cache_policy: str  # 'force', 'read', 'write', 'none'

3. Interface Design

3.1 CLI

# Start server
guixi server --host 0.0.0.0 --port 8080 --model llama2

# Inference with bandwidth stats
guixi infer "What is AI?" --compress lz4 --stats

# Benchmark bandwidth
guixi bench --prompt-file prompts.txt --iterations 100

# Cache management
guixi cache --stats --clear

3.2 Python API

from guixi import GuiXiClient

client = GuiXiClient("ws://server:8080", compression="lz4")

# Streaming inference
async for token in client.stream("Explain quantum computing"):
    print(token, end="", flush=True)

# Batched inference
results = await client.batch_infer(prompts, batch_size=10)

# Cache control
client.cache_write("my_key", tokens)
cached = client.cache_read("my_key")

3.3 GUI (PySide6)

• Server dashboard with bandwidth metrics
• Real-time compression ratio visualization
• Cache hit rate charts
• Inference log with token timing

3.4 Web API

# REST endpoints
POST /api/infer          # Single inference
POST /api/batch          # Batched inference
GET  /api/cache/stats    # Cache statistics
WS   /api/stream         # WebSocket streaming

# Request format
{
    "prompt": "What is AI?",
    "max_tokens": 100,
    "temperature": 0.7,
    "compression": "lz4",
    "cache": "read"
}

4. Performance Targets

Metric	Baseline	With GuiXi	Improvement
Token bandwidth	4 bytes/token	0.8 bytes/token	5x
Cache hit rate	0%	30-70% (application dependent)	variable
Compression latency	N/A	<5ms	-
Protocol overhead	~200 bytes/msg	~12 bytes/msg	16x
Overall bandwidth	100%	10-30%	3-10x

5. Deployment Modes

5.1 Proxy Mode

Client <-> GuiXi Proxy <-> LLM Server
            |
            +-- Compression
            +-- Caching
            +-- Protocol translation

5.2 Client Library Mode

Client App <-> GuiXi Client SDK <-> LLM Server
                    |
                    +-- Local compression
                    +-- Local cache
                    +-- Smart batching

5.3 Edge Mode

Edge Device <-> GuiXi Edge <-> Cloud LLM
                    |
                    +-- Offline cache
                    +-- Delta sync
                    +-- Disconnection handling

6. Implementation Notes

• Use lz4 for fast compression, zstandard for high compression
• Embeddings via sentence-transformers for semantic cache
• WebSocket via websockets library
• GUI via PySide6 with pyqtgraph for visualization
• Protocol versioning for forward compatibility