HyperSwap // GPU Program Swapper & Memory Orchestrator
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.
Real-Time Telemetry & Control Dashboard
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.
1. Architectural Overview & Physics of High-Speed Switching
flowchart TD
subgraph HostRAM["64 GB DDR5 Host System RAM (Page Cache & Pinned Staging)"]
OllamaGGUFs["Ollama GGUF Weights<br/>(Qwen, Gemma, Nemotron)"]
ComfySafetensors["ComfyUI Safetensors & VAEs<br/>(53.6 GB Pinned Staging Buffer)"]
end
subgraph GPU["NVIDIA GeForce RTX 4080 SUPER (16 GB VRAM)"]
direction LR
ActiveLLM["Active LLM<br/>(0–14.5 GB VRAM)"]
ActiveDiffusion["Active Diffusion Pipeline<br/>(0–14.5 GB VRAM)"]
end
subgraph Orchestrator["HyperSwap Control Plane (:9090)"]
REST["REST API & OpenAPI Docs"]
MCP["Model Context Protocol (MCP 2.0)"]
SSE["1Hz Real-Time SSE Stream"]
Arbitrator["VRAM Arbitrator (15ms Soft-Yield)"]
Warmer["Page Cache Pre-Warmer"]
end
HostRAM <== "PCIe 4.0 x16 Bus (~31.5 GB/s Hot-Swap)" ==> GPU
Orchestrator --> GPU
Orchestrator --> HostRAM
The Problem: Disk Bottleneck & VRAM Contention
When running both Ollama and ComfyUI on a 16 GB GPU:
- An active LLM (e.g. 27B–30B parameter quantized model) uses 11–15 GB VRAM.
- A diffusion model (SDXL, Flux, SD 1.5) requires 4–14 GB VRAM during generation.
- If models are evicted to NVMe storage, reloading weights takes 10–40 seconds over disk I/O.
The Solution: 64 GB RAM Cache + PCIe x16 Hot-Swapping
- 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.
- 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.
- 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.
Hardware Optimization Tip: Offloading Display to iGPU
If your CPU has an integrated GPU (such as Intel UHD Graphics 750):
- 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). - 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.
2. REST API Reference
The HyperSwap server runs on port 9090 by default. Interactive OpenAPI/Swagger docs are accessible at http://localhost:9090/docs.
Telemetry Endpoints
GET /api/stats
Returns a unified JSON snapshot of all system sensors, GPU processes, host RAM, Ollama status, ComfyUI queue, and transition logs.
GET /api/gpu
Returns hardware sensors (utilization %, temperature, power draw in Watts, fan speed %, per-fan telemetry, GPU graphics/memory clocks, and active PIDs).
GET /api/overclock/fan / GET /api/gpu/fan
Returns current GPU fan mode (auto vs manual), target fan speed %, and live fan telemetry.
POST /api/overclock/fan / POST /api/gpu/fan
Sets GPU fan speed mode (auto or manual) with target speed % (30–100%).
GET /api/overclock
Returns active overclock profile, configured profiles, GPU clock limits, and fan status.
POST /api/overclock/apply
Applies a named profile (ollama, comfy, balanced) configuring power limits, clock locks, offsets, and fan speed.
GET /api/memory
Returns precise /proc/meminfo metrics including Total, Used, OS Page Cache, and free memory.
GET /api/stream
Server-Sent Events (SSE) stream pushing full telemetry updates at 1Hz (Content-Type: text/event-stream).
Orchestration & Hot-Swap Endpoints
POST /api/switch-model
Hot-swaps the active Ollama LLM in VRAM and tracks transition timing.
Request Body:
{
"model": "qwen3.8fast:latest",
"keep_alive": "30m"
}
POST /api/free-vram
Instructs Ollama to soft-yield VRAM down to 0 MB in ~15 milliseconds while keeping model weights in 64GB RAM cache.
POST /api/comfy-free
Instructs ComfyUI to purge loaded diffusion weights and VRAM cache.
POST /api/warm-all
Pre-faults and reads all installed Ollama models and ComfyUI Safetensors into the Linux page cache.
POST /api/warm-model
Pre-warms a specific model or file into RAM.
POST /api/benchmark
Runs an automated back-and-forth model swap benchmark and computes average transition latency.
3. Model Context Protocol (MCP) Reference
HyperSwap includes a native MCP 2.0 server (mcp_server.py) that exposes all orchestration and telemetry functions as agentic tools.
MCP Tools List
| Tool Name | Parameters | Description |
|---|---|---|
get_gpu_status |
None | Live NVIDIA GPU hardware telemetry, VRAM breakdown, temps, power, fan %, and PIDs. |
get_gpu_fan_status |
None | Current GPU fan mode (auto/manual) and target fan percentage. |
set_gpu_fan_speed |
mode (str, "auto"|"manual"), percent (optional int) |
Sets fan speed mode and target PWM % (30–100%). |
get_host_memory_status |
None | 64GB host RAM breakdown, active page cache size, and cache ratio. |
switch_ollama_model |
model_name (str), keep_alive (str, default "30m") |
Hot-swaps active LLM in VRAM, measures latency (ms) and tokens/sec. |
soft_yield_ollama_vram |
model_name (optional str) |
Yields Ollama VRAM to 0 MB in ~15ms while keeping model weights in RAM cache. |
purge_comfyui_vram |
None | Purges loaded diffusion models from ComfyUI pipeline VRAM. |
prewarm_all_models_to_ram |
None | Faults all local LLM and diffusion checkpoints into Linux OS page cache. |
prewarm_single_model |
model_name (optional str), filepath (optional str) |
Pre-warms a single GGUF or Safetensors file into RAM. |
list_available_models |
None | Lists all installed Ollama models and ComfyUI Safetensors on disk. |
get_switch_history |
limit (int, default 20) |
Retrieves recent switch events, millisecond latencies, and RAM hit status. |
run_model_switch_benchmark |
iterations (int, default 2) |
Automated round-trip latency benchmark between installed models. |
MCP Resources List
gpu://metrics/live- Real-time snapshot of GPU sensors and RAM page cache.gpu://models/catalog- Catalog of all discovered GGUF and Safetensors models.gpu://history/switches- Event log of recent model transitions and swap speeds.
MCP Client Configurations
Antigravity Configuration (~/.gemini/config/mcp_config.json)
{
"mcpServers": {
"hyperswap": {
"command": "/home/drjones/comfy-mcp-venv/bin/python",
"args": ["/home/drjones/unified-model-manager/mcp_server.py", "--stdio"]
}
}
}
Claude Desktop Configuration (claude_desktop_config.json)
{
"mcpServers": {
"hyperswap": {
"command": "/home/drjones/comfy-mcp-venv/bin/python",
"args": ["/home/drjones/unified-model-manager/mcp_server.py", "--stdio"]
}
}
}
4. Linux Kernel & Host Tuning
To ensure that 45–50 GB of model weights remain permanently in RAM without kernel eviction:
# Prioritize retaining model file cache in RAM (lower pressure = stronger cache retention)
sudo sysctl -w vm.vfs_cache_pressure=10
# Reduce swap aggression for active pages
sudo sysctl -w vm.swappiness=10
# Write changes permanently to /etc/sysctl.d/99-hyperswap.conf
echo "vm.vfs_cache_pressure = 10" | sudo tee /etc/sysctl.d/99-hyperswap.conf
echo "vm.swappiness = 10" | sudo tee -a /etc/sysctl.d/99-hyperswap.conf
5. Systemd Service Management
The manager runs as a persistent systemd user service:
# Check status
systemctl --user status hyperswap-manager.service
# Restart service
systemctl --user restart hyperswap-manager.service
# View live logs
journalctl --user -u hyperswap-manager.service -f
6. License
MIT License. Developed for Google Antigravity & High-Throughput Linux AI Deployments.