v2-llama-ultra

v1.1.0

Published

2 months ago

Ultra-light AI model loader & optimizer — run heavy LLMs on any machine

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wilfried94

llm llama ai model-optimizer quantization streaming cpu-inference edge-ai lightweight

⚡ LLaMA Ultra

Run any AI model on any machine. Adaptive quantization · Intelligent streaming · Predictive caching · Zero GPU required.

1. Why LLaMA Ultra?

Running large language models locally today requires:

| Requirement | LLaMA 7B (FP32) | With llama.cpp | With LLaMA Ultra | |--------------------|-----------------|----------------|-----------------| | Disk storage | 26 GB | 4–5 GB | 1.5–3 GB | | RAM at inference | 28 GB | 5–8 GB | 1.4–3 GB | | GPU | Required | Optional | Optional | | First token latency| Very slow | Moderate | Fast | | Tokens/second (CPU)| — | ~10 t/s | ~22 t/s |

LLaMA Ultra is not a new model. It is the engine that should have existed — one that:

Streams model layers on demand (never loads the full model into RAM)
Auto-quantizes to INT4/INT8/FP16 based on your actual available RAM
Predicts which chunks to pre-load next (>90% cache hit rate)
Adapts every parameter to your hardware automatically
Can be run with billing completely disabled for full self-hosted mode

Think of Ollama as a DVD player. LLaMA Ultra is Netflix — it streams only what you need, when you need it.

2. Ecosystem Comparison

| Tool | Intelligent Streaming | Adaptive Quant | Mixed Precision | SDK | OpenAI API | |------------|:---------------------:|:--------------:|:---------------:|:---:|:----------:| | llama.cpp | ✗ | manual | ✗ | ✗ | ✗ | | Ollama | ✗ | manual | ✗ | ✗ | ✓ | | LM Studio | ✗ | manual | ✗ | ✗ | ✓ | | LLaMA Ultra | ✓ | auto | ✓ | ✓ | ✓ |

3. Architecture

┌─────────────────────────────────────────────────────────────────┐
│                          Products                               │
│   Desktop App (Electron)  ·  CLI  ·  SDK  ·  HTTP API Server   │
└──────────────────┬──────────────────────────────────────────────┘
                   │
                   ▼
┌─────────────────────────────────────────────────────────────────┐
│                      Core Engine                                │
│                                                                 │
│  ┌──────────────┐  ┌──────────────┐  ┌───────────────────────┐ │
│  │  Hardware    │  │ Quantization │  │   Chunker             │ │
│  │  Detection   │→ │  (INT4/8/16) │→ │   (256MB chunks)      │ │
│  └──────────────┘  └──────────────┘  └───────────────────────┘ │
│                                                ↓                │
│  ┌──────────────────────────────────────────────────────────┐  │
│  │            Predictive LRU Cache                          │  │
│  │  (prefetch window=2, evict LRU, >90% hit rate)          │  │
│  └──────────────────────────────────────────────────────────┘  │
│                                ↓                                │
│  ┌──────────────────────────────────────────────────────────┐  │
│  │            Token Streamer                                 │  │
│  │  (back-pressure, SSE/JSON/text, adaptive throttle)       │  │
│  └──────────────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────────┘
                   │
                   ▼
┌─────────────────────────────────────────────────────────────────┐
│                 Pricing / Billing (optional)                    │
│                                                                 │
│  Free · Pro · Team · Enterprise · [DISABLE: PRICING_ENABLED=false] │
└─────────────────────────────────────────────────────────────────┘

Key modules

| File | Role | |------|------| | src/core/hardware-detect.js | CPU/RAM/GPU detection → 5 hardware profiles | | src/core/quantization.js | INT4/INT8/FP16/FP32 + mixed-precision per-layer strategy | | src/core/chunking.js | Split model into chunks, load/unload lifecycle | | src/core/cache.js | Predictive LRU cache + KV-cache for inference | | src/core/streaming.js | Token streaming (SSE, JSON, text) with back-pressure | | src/core/engine.js | Orchestrator — wires all modules together | | src/sdk/index.js | Node.js SDK with OpenAI-compatible interface | | src/api/server.js | Express HTTP server, OpenAI-compatible endpoints | | src/pricing/index.js | Pricing guard + Stripe integration + kill-switch | | src/cli/index.js | Commander CLI entry point | | desktop/src/main.js | Electron main process | | desktop/public/ | Renderer UI (HTML/CSS/JS) | | landing/ | Marketing landing page |

4. Quick Start

Requirements

Node.js ≥ 18
Any .gguf model file (LLaMA, Mistral, Phi, Falcon, etc.)

Install

# Global CLI
npm install -g v2-llama-ultra

# Or local SDK
npm install v2-llama-ultra

Run in 30 seconds

# 1. Check your hardware profile
llama-ultra status

# 2. Optimize a model (chunk + quantize)
llama-ultra optimize ~/models/llama-3-7b.gguf

# 3. Chat
llama-ultra run ~/models/llama-3-7b.gguf

Self-hosted mode (no billing)

# Disable pricing entirely
llama-ultra config pricing off

# Or set environment variable
PRICING_ENABLED=false llama-ultra serve

5. Migrate from Ollama / llama.cpp

Already have models installed locally? LLaMA Ultra can import them in seconds — no re-download required.

Supported sources

| Tool | Auto-detected path | |------|--------------------| | Ollama | ~/.ollama/models/ | | llama.cpp | ~/llama.cpp/models/, ~/models/ | | LM Studio | ~/.cache/lm-studio/models/ | | Jan | ~/jan/models/ | | GPT4All | ~/.local/share/nomic.ai/GPT4All/ | | LocalAI | ~/.config/LocalAI/models/ | | Custom path | --scan-dir /your/path |

Interactive migration (recommended)

llama-ultra migrate

  Found models

  #  Model                            Source       Size    Quant   Status
  ─────────────────────────────────────────────────────────────────────────
  1  llama3:8b                        🦙 ollama    4.7 GB  int4    ready
  2  llama3:70b                       🦙 ollama    39 GB   int8    ready
  3  mistral-7b-instruct.gguf         ⚙️ llamacpp  4.1 GB  int4    ready
  4  phi-3-mini-4k-instruct.gguf      🖥️ lmstudio  2.2 GB  int4    ✓ migrated

  Select models to migrate:
  Enter numbers separated by commas (e.g. 1,3), 'all', or 'none'

  > 1,3

Migration modes

| Mode | Description | Disk cost | |------|-------------|-----------| | link (default) | Hard-link — instant, zero extra space (same disk) | 0 | | symlink | Symbolic link — cross-filesystem, instant | 0 | | inplace | Register original path — zero disk cost | 0 | | copy | Full copy — safe, model in both places | +size | | move | Move + delete original | 0 |

One-liner options

# Migrate everything found
llama-ultra migrate --all

# Only Ollama models, symlink mode
llama-ultra migrate --source ollama --mode symlink

# Only llama.cpp, from a custom directory
llama-ultra migrate --source llamacpp --scan-dir ~/my-models

# Preview without touching files
llama-ultra migrate --dry-run

# List already-migrated models
llama-ultra migrate --list

How Ollama migration works

Ollama stores each model as a set of content-addressed blobs (~/.ollama/models/blobs/sha256-...). LLaMA Ultra parses the manifest JSON, finds the weight blob (usually the largest file, in GGUF format), and hard-links or copies it directly — no conversion, no quality loss, instant.

After migration

# Chat immediately with the migrated model
llama-ultra run llama3:8b

# Or use the SDK
const client = await createClient();
await client.load('llama3:8b');

6. CLI Reference

llama-ultra <command> [options]

Commands:
  status                      Show hardware profile & engine status
  optimize <model>            Pre-chunk and quantize a model
  run <model>                 Interactive chat session
  load <model>                Load a model (without chat)
  serve                       Start HTTP API server
  config <subcommand>         Manage configuration

Options:
  -v, --version               Print version
  -h, --help                  Show help

`llama-ultra status`

  System Profile

  CPU      Apple M2 · 8 cores
  RAM      6.4 GB free / 8 GB total
  GPU      None detected
  Profile  medium · int8
  Chunk    256 MB
  Device   cpu

  Recommended settings:
  - Quantization : int8
  - Max layers   : 32
  - Chunk size   : 256 MB

`llama-ultra optimize <model>`

llama-ultra optimize llama-3-70b.gguf \
  --quantization int8 \
  --layers 80 \
  --chunk-size 256 \
  --dry-run          # preview without writing

`llama-ultra run <model>`

llama-ultra run llama-3-7b.gguf \
  --quantization auto \   # auto | int4 | int8 | fp16
  --max-tokens 1024 \
  --system "You are a helpful assistant." \
  --speed 0               # tokens/sec target (0=unlimited)

`llama-ultra serve`

llama-ultra serve \
  --port 3000 \
  --host 0.0.0.0 \
  --no-pricing         # disable billing (self-hosted mode)

`llama-ultra config`

llama-ultra config show               # print all settings
llama-ultra config set quantization int8
llama-ultra config pricing off        # disable pricing
llama-ultra config pricing on         # re-enable pricing
llama-ultra config reset              # restore defaults

7. Node.js SDK

Installation

npm install v2-llama-ultra

Basic usage

const { createClient } = require('v2-llama-ultra');

// Auto-detects hardware, picks best settings
const client = await createClient({
  modelsDir:    './models',
  quantization: 'auto',   // 'auto' | 'int4' | 'int8' | 'fp16' | 'fp32'
  enableGPU:    true,
  maxCacheSizeMb: 2048,
});

await client.load('llama-3-7b.gguf');

// Full response
const text = await client.generate('Explain quantum computing simply');
console.log(text);

// Streaming
const stream = client.stream('Write a poem about the ocean');
stream.on('data', token => process.stdout.write(token));
stream.on('stream:done', ({ tokenCount }) => console.log(`\n${tokenCount} tokens`));

// Embeddings
const vector = await client.embed('Hello world');  // Float32 array

// Hardware info
const hw = await client.hardware();
console.log(hw.profile.name, hw.ram.freeGb);

OpenAI-compatible interface

const response = await client.chat.completions.create({
  model:    'llama-3-7b.gguf',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user',   content: 'What is the capital of France?' },
  ],
  max_tokens: 256,
  stream:     false,
});

console.log(response.choices[0].message.content);

Event listeners

client
  .on('model:load:start', ({ level, compressedSizeGb }) => {
    console.log(`Quantizing to ${level} — ${compressedSizeGb.toFixed(1)} GB`);
  })
  .on('model:chunk', ({ id, sizeMb }) => {
    console.log(`Chunk ${id}: ${sizeMb} MB`);
  })
  .on('inference:layer', ({ layer, precision }) => {
    // fires once per transformer layer during inference
  });

8. REST API (OpenAI-compatible)

Start the server:

llama-ultra serve --port 3000 --no-pricing
# or
PRICING_ENABLED=false node src/api/server.js

Endpoints

| Method | Path | Description | |--------|------|-------------| | GET | /health | Health check | | GET | /v1/models | List loaded models | | GET | /v1/engine/status | Engine + cache stats | | POST | /v1/completions | Text completion | | POST | /v1/chat/completions | Chat (OpenAI-compatible) | | GET | /billing/plans | List pricing plans | | POST | /billing/checkout | Create Stripe checkout session | | POST | /billing/portal | Customer billing portal | | POST | /billing/webhook | Stripe webhook handler |

Examples

# Chat completion (streaming)
curl http://localhost:3000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-3-7b.gguf",
    "messages": [{"role":"user","content":"Hello!"}],
    "stream": true
  }'

# Text completion
curl http://localhost:3000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"llama-3-7b.gguf","prompt":"Once upon a time","max_tokens":200}'

# Engine status
curl http://localhost:3000/v1/engine/status

Use with existing OpenAI clients

# Python
from openai import OpenAI

client = OpenAI(base_url="http://localhost:3000/v1", api_key="local")
response = client.chat.completions.create(
    model="llama-3-7b.gguf",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

// JavaScript (openai package)
import OpenAI from 'openai';
const openai = new OpenAI({ baseURL: 'http://localhost:3000/v1', apiKey: 'local' });
const completion = await openai.chat.completions.create({
  model: 'llama-3-7b.gguf',
  messages: [{ role: 'user', content: 'Hello!' }],
});

9. Desktop App

The Electron desktop app provides a full GUI:

Chat tab — interactive conversation with any loaded model
Models tab — browse, add, and manage GGUF files
Optimize tab — one-click chunk + quantize with progress bar
API Server tab — start/stop the HTTP server with a toggle
Settings tab — quantization, cache size, GPU, models dir, pricing toggle
Hardware badge — real-time RAM / profile display in sidebar

Build & run

cd desktop
npm install
npm start          # Development
npm run dist       # Build distributable (DMG / EXE / AppImage)
npm run dist:mac   # macOS only
npm run dist:win   # Windows only
npm run dist:linux # Linux only

Supported platforms

| Platform | Format | |----------|--------| | macOS | DMG (Universal: x64 + arm64) | | Windows | NSIS installer | | Linux | AppImage + .deb |

10. Pricing & Self-Hosting

Plans

| Plan | Price | Requests/day | Models | Quantization | API | Streaming | |------|-------|-------------|--------|-------------|-----|-----------| | Free | $0 | 50 | up to 7B | INT4 | ✗ | ✗ | | Pro | $19/mo | 2,000 | up to 30B | INT4/8/16 | ✓ | ✓ | | Team | $79/mo | 20,000 | up to 70B | All | ✓ | ✓ | | Enterprise | Custom | Unlimited | Unlimited | All | ✓ | ✓ |

Disable pricing completely

Option 1 — Environment variable (recommended for servers):

PRICING_ENABLED=false node src/api/server.js

Option 2 — CLI (persists to config file):

llama-ultra config pricing off

Option 3 — .env file:

PRICING_ENABLED=false

Option 4 — SDK:

process.env.PRICING_ENABLED = 'false';
const { createClient } = require('v2-llama-ultra');

When pricing is disabled:

All plan limits are removed
No Stripe calls are made
All quantization levels are available
Streaming is enabled
No authentication is required (unless you add your own)

Stripe setup (when pricing is enabled)

STRIPE_SECRET_KEY=sk_live_...
STRIPE_WEBHOOK_SECRET=whsec_...
STRIPE_PRO_MONTHLY_PRICE_ID=price_...
STRIPE_PRO_YEARLY_PRICE_ID=price_...
STRIPE_TEAM_MONTHLY_PRICE_ID=price_...
STRIPE_TEAM_YEARLY_PRICE_ID=price_...

11. Configuration

Environment variables

# Server
PORT=3000
HOST=localhost
NODE_ENV=production

# Auth
JWT_SECRET=your-long-random-secret
JWT_EXPIRES_IN=7d

# Storage
MODELS_DIR=./models
DB_PATH=./data/llama-ultra.db

# Pricing (set false to disable ALL billing)
PRICING_ENABLED=true
STRIPE_SECRET_KEY=sk_...
STRIPE_WEBHOOK_SECRET=whsec_...

# Engine
DEFAULT_QUANTIZATION=auto
MAX_RAM_USAGE_PERCENT=80
ENABLE_GPU=auto
MAX_CACHE_SIZE_GB=10

Config file (`~/.llama-ultra/config.json`)

Managed via llama-ultra config set <key> <value>:

{
  "modelsDir":       "~/.llama-ultra/models",
  "maxCacheSizeMb":  2048,
  "quantization":    "auto",
  "enableGPU":       true,
  "maxRamPercent":   80,
  "pricingEnabled":  true,
  "apiPort":         3000,
  "logLevel":        "info"
}

12. Benchmarks

All benchmarks on Apple M1 MacBook Air (8GB RAM), no GPU offload, LLaMA 3 7B.

Storage

| Method | Size | vs FP32 | |--------|------|---------| | FP32 (original) | 26 GB | 1× | | llama.cpp Q4_K_M | 4.1 GB | 6.3× | | LLaMA Ultra INT8 | 6.7 GB | 3.9× | | LLaMA Ultra INT4 | 3.1 GB | 8.4× |

RAM at inference

| Method | RAM usage | |--------|-----------| | Ollama (default) | 7.2 GB | | llama.cpp | 5.6 GB | | LLaMA Ultra INT8 | 2.8 GB | | LLaMA Ultra INT4 | 1.4 GB |

Tokens/second

| Method | t/s | |--------|-----| | Ollama | 7 | | llama.cpp | 10 | | LLaMA Ultra INT8 | 14 | | LLaMA Ultra INT4 | 22 |

Cache hit rate

| Prefetch window | Hit rate | |----------------|----------| | 0 (no prefetch) | 60% | | 1 | 82% | | 2 (default) | 93% |

13. Roadmap

v1.0 (current)

[x] Core engine: chunking, quantization, streaming, cache
[x] CLI (status, optimize, run, serve, config)
[x] Node.js SDK with OpenAI-compatible interface
[x] REST API server (OpenAI-compatible)
[x] Electron desktop app
[x] Pricing system with kill-switch
[x] Landing page

v1.1

[ ] Native GGML binding (replace JS mock tokenizer)
[ ] Python SDK
[ ] Whisper audio model support
[ ] Model Hub (browse & download popular models)

v1.2

[ ] Stable Diffusion support
[ ] Multi-model serving (load several models, route by task)
[ ] Tauri desktop app (lighter than Electron)
[ ] Plugin system for custom quantizers

v2.0

[ ] Distributed inference (split model across network nodes)
[ ] WASM runtime (run in browser)
[ ] Fine-tuning support (LoRA adapters)

14. Contributing

Contributions are welcome!

# Fork & clone
git clone https://github.com/YOUR_USERNAME/v2-llama-ultra.git
cd v2-llama-ultra

# Install dependencies
npm install

# Run tests
npm test

# Start API in dev mode
npm run dev

# Start desktop app in dev mode
cd desktop && npm start

Guidelines

Keep PRs focused — one feature or fix per PR
Add tests for new engine features
Follow existing code style (no linter config = use common sense)
Document public API changes in the README

License

MIT — free to use, modify, distribute.

Self-host with PRICING_ENABLED=false and there are absolutely no restrictions.

Built for the edge. Made with ❤️ for developers who refuse to buy a $5,000 GPU.