Proxmox Homelab Scripts

Utilities for creating and operating local Proxmox LXCs and VMs.

Layout

• pro-v620/: scripts and notes for the Radeon Pro V620 (current GPU — the host now has two).
• rx-6700-xt/: scripts and notes for the Radeon RX 6700 XT (prior GPU — the V620 replaced it; kept for reference).
• bench-runner/: disposable LXC for OpenAI-compatible LLM benchmarks.
• hermes/: persistent LXC running NousResearch's Hermes Agent (the agent that consumes the LLM runtime's API).
• host-net/: host-side networking that runs on the Proxmox host itself (not in an LXC). host-net/wifi-nat/ turns the host into a WiFi-uplink NAT gateway so it can run with no ethernet.

Each GPU folder should own its own model/runtime assumptions. LLM containers tend to need GPU-specific environment variables, memory sizing, context settings, and runtime flags, so the scripts are intentionally explicit instead of trying to be a universal model launcher.

VMID Convention

Containers are allocated VMIDs by role:

Range	Purpose
100-119	Infra / services
120-139	AI/LLM containers
140-159	Databases
200+	Test / temporary

Current containers: CT 120 (the AI/LLM runtime — pinned to one of two Radeon Pro V620s (GPU 1; GPU 2 left idle/free); provisioned by pro-v620/create-lxc-llamacpp-qwen3.6-35b-a3b.sh), CT 121 (hermes, the Hermes Agent that consumes CT 120's API) and CT 200 (bench-runner, disposable). Each creation script defaults its VMID to the matching range and accepts a VMID= override.

Current Scripts

Benchmark Runner LXC

Creates a small unprivileged Debian LXC that runs the benchmark suite against a local LLM runtime's OpenAI-compatible API (LM Studio or llama.cpp — both expose the same /v1 endpoint, so the suite treats them identically).

See bench-runner/README.md.

Run it directly on the Proxmox host without cloning the repo:

bash -c "$(wget -qLO - https://raw.githubusercontent.com/marchah/Proxmox/main/bench-runner/create-lxc-bench-runner.sh)"

Pro V620 LLM Runtime LXC (current)

Creates a privileged Ubuntu LXC serving a high-parameter Qwen model on the Radeon Pro V620 (Navi 21 / gfx1030, 32 GB) via Vulkan:

• GPU: two Radeon Pro V620s (32 GB each — replaced the 12 GiB RX 6700 XT), one in PCIe-1 (0000:2d:00.0) and one in PCIe-3 (0000:06:00.0). The ~26.6 GB model fits one card, so CT 120 is pinned to GPU 1 alone and GPU 2 is left idle/free for a future service. Both cooled by a single NF-F12 shroud (one fan curve tracking the hotter card) and undervolted −100 mV. See pro-v620/README.md.
• Model: unsloth/Qwen3.6-35B-A3B-GGUF / Qwen3.6-35B-A3B-UD-Q5_K_XL.gguf (MoE, 35B total / ~3B active — fast, fits 32 GB at Q5)
• Engine: create-lxc-llamacpp-qwen3.6-35b-a3b.sh — llama.cpp's llama-server

Defaults to CT 120, serves an OpenAI-compatible API on 0.0.0.0:1234 under the id qwen3.6-35b-a3b. Chosen for agent use (MoE keeps per-step latency low). See pro-v620/README.md for benchmarks, the model bake-off, and tuning.

Run directly on the Proxmox host without cloning the repo:

bash -c "$(wget -qLO - https://raw.githubusercontent.com/marchah/Proxmox/main/pro-v620/create-lxc-llamacpp-qwen3.6-35b-a3b.sh)"

Hermes Agent LXC

Creates a persistent unprivileged Debian LXC running NousResearch's Hermes Agent — the homelab's agent, pointed at the CT 120 runtime's OpenAI-compatible API (no Nous Portal login). A single hermes gateway run service serves both the messaging gateway and Hermes's own OpenAI-compatible API server on 0.0.0.0:8642. Defaults to CT 121, full browser tools, starts on boot. See hermes/README.md.

Run directly on the Proxmox host without cloning the repo:

bash -c "$(wget -qLO - https://raw.githubusercontent.com/marchah/Proxmox/main/hermes/create-lxc-hermes-agent.sh)"

Host WiFi-NAT Gateway (runs on the host, not in an LXC)

host-net/wifi-nat/install.sh lets the Proxmox host run with no ethernet: the onboard WiFi (wlo1) becomes the routed WAN and vmbr0 becomes an internal NAT'd LAN (10.10.10.0/24) that the LXCs sit behind (dnsmasq DHCP/DNS + nftables masquerade + port-forwards). It's staged and reversible, with an auto-rollback guarding the risky cutover. See host-net/wifi-nat/README.md.

RX 6700 XT LLM Runtime LXC (prior GPU)

The V620 above replaced this card. These scripts are kept for reference.

Creates a privileged Ubuntu LXC serving the same model on the RX 6700 XT via Vulkan, with a choice of inference engine:

• GPU: Radeon RX 6700 XT
• Model: unsloth/Qwen3.5-9B-GGUF / Qwen3.5-9B-Q4_K_M.gguf
• Engine — pick one script:
  • create-lxc-lmstudio-qwen3.5-9b.sh — LM Studio's lms CLI
  • create-lxc-llamacpp-qwen3.5-9b.sh — llama.cpp's llama-server

Both default to CT 120 and serve an OpenAI-compatible API on 0.0.0.0:1234, so they are mutually exclusive — run one at a time (only one can use the 12 GiB GPU). See rx-6700-xt/README.md.

Recommended: llama.cpp with --parallel 4 (the script default). For Qwen3.5-9B-Q4_K_M at 64 k context on this GPU:

Metric	LM Studio	llama.cpp
Single-stream	53 tok/s	56 tok/s
Concurrent aggregate (4 slots)	~47 tok/s	80 tok/s
Cold prefill ≥ 6 k tokens	garbage	correct (to 32 k)

Single-user speed is a wash (~56 tok/s, ~0.2 s first token), but llama.cpp roughly doubles concurrent throughput and — unlike LM Studio — never corrupts long cold prompts. Both are GPU-bound (~99 % util, ~7 GiB / 12 GiB VRAM). Full data and methodology: rx-6700-xt/README.md#recommendation.

Run directly on the Proxmox host without cloning the repo:

# LM Studio
bash -c "$(wget -qLO - https://raw.githubusercontent.com/marchah/Proxmox/main/rx-6700-xt/create-lxc-lmstudio-qwen3.5-9b.sh)"
# llama.cpp
bash -c "$(wget -qLO - https://raw.githubusercontent.com/marchah/Proxmox/main/rx-6700-xt/create-lxc-llamacpp-qwen3.5-9b.sh)"

Notes

• Run scripts on the Proxmox host as root.
• Review each GPU folder README before running its scripts.
• Keep downloaded model files and generated benchmark results out of git.