209 lines
8.4 KiB
Markdown
209 lines
8.4 KiB
Markdown
# HyperSwap // GPU Program Swapper & Memory Orchestrator
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[](https://fastapi.tiangolo.com)
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[](https://modelcontextprotocol.io)
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[](https://developer.nvidia.com/cuda-zone)
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[](https://ubuntu.com)
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**HyperSwap** is an ultra-low-latency VRAM arbitrator, RAM cache pre-warmer, and real-time telemetry dashboard designed specifically for Linux deployment machines that simultaneously host **Ollama LLM workloads** and **ComfyUI Diffusion pipelines** on a single GPU.
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---
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## Real-Time Telemetry & Control Dashboard
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*The HyperSwap live dashboard running on `:9090`, demonstrating real-time VRAM allocation tracking (Ollama 14.14 GB, ComfyUI 0.38 GB, Desktop 0.6 GB), 33.89 GB of models resident in 64GB host RAM cache, and sub-25ms model VRAM purges and soft-yields.*
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---
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## 1. Architectural Overview & Physics of High-Speed Switching
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```mermaid
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flowchart TD
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subgraph HostRAM["64 GB DDR5 Host System RAM (Page Cache & Pinned Staging)"]
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OllamaGGUFs["Ollama GGUF Weights<br/>(Qwen, Gemma, Nemotron)"]
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ComfySafetensors["ComfyUI Safetensors & VAEs<br/>(53.6 GB Pinned Staging Buffer)"]
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end
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subgraph GPU["NVIDIA GeForce RTX 4080 SUPER (16 GB VRAM)"]
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direction LR
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ActiveLLM["Active LLM<br/>(0–14.5 GB VRAM)"]
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ActiveDiffusion["Active Diffusion Pipeline<br/>(0–14.5 GB VRAM)"]
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end
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subgraph Orchestrator["HyperSwap Control Plane (:9090)"]
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REST["REST API & OpenAPI Docs"]
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MCP["Model Context Protocol (MCP 2.0)"]
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SSE["1Hz Real-Time SSE Stream"]
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Arbitrator["VRAM Arbitrator (15ms Soft-Yield)"]
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Warmer["Page Cache Pre-Warmer"]
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end
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HostRAM <== "PCIe 4.0 x16 Bus (~31.5 GB/s Hot-Swap)" ==> GPU
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Orchestrator --> GPU
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Orchestrator --> HostRAM
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```
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### The Problem: Disk Bottleneck & VRAM Contention
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When running both Ollama and ComfyUI on a 16 GB GPU:
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* An active LLM (e.g. 27B–30B parameter quantized model) uses **11–15 GB VRAM**.
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* A diffusion model (SDXL, Flux, SD 1.5) requires **4–14 GB VRAM** during generation.
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* If models are evicted to NVMe storage, reloading weights takes **10–40 seconds** over disk I/O.
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### The Solution: 64 GB RAM Cache + PCIe x16 Hot-Swapping
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* **Host RAM as Staging**: All active LLMs and diffusion checkpoints remain 100% resident in the 64 GB Linux OS Page Cache and pinned memory buffer.
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* **PCIe Bus Hot-Swap Speed**: Reloading from host RAM over the PCIe 4.0 x16 bus achieves **~31.5 GB/s** transfer bandwidth, bringing model swap times down to **hundreds of milliseconds**.
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* **15ms Soft-Yield**: When ComfyUI triggers an image generation, Ollama executes an instant soft-yield (`keep_alive: 0`), dropping VRAM allocation from 14.5 GB to 0 MB in **~15 milliseconds** without discarding model pages from system RAM.
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### Hardware Optimization Tip: Offloading Display to iGPU
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If your CPU has an integrated GPU (such as Intel UHD Graphics 750):
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* Plugging your display monitor into the motherboard's HDMI/DisplayPort offloads the desktop display server (`gnome-shell`, `firefox`, `Xwayland`) to the iGPU (shared system RAM).
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* This **reclaims ~0.7 to 1.5 GB of dedicated GDDR6X VRAM** on the RTX 4080 SUPER, giving AI models 100% dedicated access to the full **16.0 GB VRAM**.
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---
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## 2. REST API Reference
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The HyperSwap server runs on port `9090` by default. Interactive OpenAPI/Swagger docs are accessible at `http://localhost:9090/docs`.
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### Telemetry Endpoints
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#### `GET /api/stats`
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Returns a unified JSON snapshot of all system sensors, GPU processes, host RAM, Ollama status, ComfyUI queue, and transition logs.
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#### `GET /api/gpu`
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Returns hardware sensors (utilization %, temperature, power draw in Watts, fan %, GPU graphics/memory clocks, and active PIDs).
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#### `GET /api/memory`
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Returns precise `/proc/meminfo` metrics including Total, Used, OS Page Cache, and free memory.
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#### `GET /api/stream`
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Server-Sent Events (SSE) stream pushing full telemetry updates at 1Hz (`Content-Type: text/event-stream`).
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---
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### Orchestration & Hot-Swap Endpoints
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#### `POST /api/switch-model`
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Hot-swaps the active Ollama LLM in VRAM and tracks transition timing.
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**Request Body:**
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```json
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{
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"model": "qwen3.8fast:latest",
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"keep_alive": "30m"
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}
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```
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#### `POST /api/free-vram`
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Instructs Ollama to soft-yield VRAM down to 0 MB in ~15 milliseconds while keeping model weights in 64GB RAM cache.
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#### `POST /api/comfy-free`
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Instructs ComfyUI to purge loaded diffusion weights and VRAM cache.
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#### `POST /api/warm-all`
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Pre-faults and reads all installed Ollama models and ComfyUI Safetensors into the Linux page cache.
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#### `POST /api/warm-model`
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Pre-warms a specific model or file into RAM.
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#### `POST /api/benchmark`
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Runs an automated back-and-forth model swap benchmark and computes average transition latency.
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---
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## 3. Model Context Protocol (MCP) Reference
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HyperSwap includes a native **MCP 2.0 server** (`mcp_server.py`) that exposes all orchestration and telemetry functions as agentic tools.
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### MCP Tools List
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| Tool Name | Parameters | Description |
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| :--- | :--- | :--- |
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| **`get_gpu_status`** | *None* | Live NVIDIA GPU hardware telemetry, VRAM breakdown, temps, power, and PIDs. |
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| **`get_host_memory_status`** | *None* | 64GB host RAM breakdown, active page cache size, and cache ratio. |
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| **`switch_ollama_model`** | `model_name` (str), `keep_alive` (str, default "30m") | Hot-swaps active LLM in VRAM, measures latency (ms) and tokens/sec. |
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| **`soft_yield_ollama_vram`** | `model_name` (optional str) | Yields Ollama VRAM to 0 MB in ~15ms while keeping model weights in RAM cache. |
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| **`purge_comfyui_vram`** | *None* | Purges loaded diffusion models from ComfyUI pipeline VRAM. |
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| **`prewarm_all_models_to_ram`** | *None* | Faults all local LLM and diffusion checkpoints into Linux OS page cache. |
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| **`prewarm_single_model`** | `model_name` (optional str), `filepath` (optional str) | Pre-warms a single GGUF or Safetensors file into RAM. |
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| **`list_available_models`** | *None* | Lists all installed Ollama models and ComfyUI Safetensors on disk. |
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| **`get_switch_history`** | `limit` (int, default 20) | Retrieves recent switch events, millisecond latencies, and RAM hit status. |
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| **`run_model_switch_benchmark`**| `iterations` (int, default 2) | Automated round-trip latency benchmark between installed models. |
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### MCP Resources List
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* `gpu://metrics/live` - Real-time snapshot of GPU sensors and RAM page cache.
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* `gpu://models/catalog` - Catalog of all discovered GGUF and Safetensors models.
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* `gpu://history/switches` - Event log of recent model transitions and swap speeds.
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---
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### MCP Client Configurations
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#### Antigravity Configuration (`~/.gemini/config/mcp_config.json`)
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```json
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{
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"mcpServers": {
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"hyperswap": {
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"command": "/home/drjones/comfy-mcp-venv/bin/python",
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"args": ["/home/drjones/unified-model-manager/mcp_server.py", "--stdio"]
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}
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}
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}
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```
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#### Claude Desktop Configuration (`claude_desktop_config.json`)
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```json
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{
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"mcpServers": {
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"hyperswap": {
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"command": "/home/drjones/comfy-mcp-venv/bin/python",
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"args": ["/home/drjones/unified-model-manager/mcp_server.py", "--stdio"]
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}
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}
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}
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```
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---
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## 4. Linux Kernel & Host Tuning
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To ensure that 45–50 GB of model weights remain permanently in RAM without kernel eviction:
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```bash
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# Prioritize retaining model file cache in RAM (lower pressure = stronger cache retention)
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sudo sysctl -w vm.vfs_cache_pressure=10
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# Reduce swap aggression for active pages
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sudo sysctl -w vm.swappiness=10
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# Write changes permanently to /etc/sysctl.d/99-hyperswap.conf
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echo "vm.vfs_cache_pressure = 10" | sudo tee /etc/sysctl.d/99-hyperswap.conf
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echo "vm.swappiness = 10" | sudo tee -a /etc/sysctl.d/99-hyperswap.conf
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```
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---
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## 5. Systemd Service Management
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The manager runs as a persistent systemd user service:
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```bash
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# Check status
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systemctl --user status hyperswap-manager.service
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# Restart service
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systemctl --user restart hyperswap-manager.service
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# View live logs
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journalctl --user -u hyperswap-manager.service -f
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```
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---
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## 6. License
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MIT License. Developed for Google Antigravity & High-Throughput Linux AI Deployments.
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