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diff --git a/snippets/hyperstack/hyperstack-vm2.toml b/snippets/hyperstack/hyperstack-vm2.toml
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--- a/snippets/hyperstack/hyperstack-vm2.toml
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-[auth]
-api_key_file = "~/.hyperstack"
-
-[hyperstack]
-base_url = "https://infrahub-api.nexgencloud.com/v1"
-
-[state]
-# Separate state file for VM2 so vm1 and vm2 can be managed independently.
-file = ".hyperstack-vm2-state.json"
-
-[vm]
-name_prefix = "hyperstack2"
-hostname = "hyperstack2"
-environment_name = "snonux-ollama"
-
-# A100-80GB is the cost-first default for qwen3-coder-next inference.
-# Switch this to n3-H100x1 if you want safer throughput and compatibility headroom.
-flavor_name = "n3-A100x1"
-image_name = "Ubuntu Server 24.04 LTS R570 CUDA 12.8 with Docker"
-assign_floating_ip = true
-create_bootable_volume = false
-enable_port_randomization = false
-labels = ["qwen3-coder-next", "wireguard"]
-
-[ssh]
-username = "ubuntu"
-private_key_path = "~/.ssh/id_rsa"
-hyperstack_key_name = "earth"
-port = 22
-connect_timeout_sec = 10
-
-[network]
-wireguard_udp_port = 56710
-wireguard_subnet = "192.168.3.0/24"
-# VM2 gets the third server-side WireGuard IP (skipping .2 which is the earth client).
-# earth (client) is 192.168.3.2; VM1 is 192.168.3.1; VM2 is 192.168.3.3.
-wireguard_server_ip = "192.168.3.3"
-# Secure default: "auto" resolves your current public egress IP to /32 at runtime.
-# Override with explicit CIDRs if you deploy from multiple networks or want broader access.
-allowed_ssh_cidrs = ["auto"]
-allowed_wireguard_cidrs = ["auto"]
-# Port 11434 is shared by both Ollama and vLLM for firewall compatibility.
-ollama_port = 11434
-# Port 4000: LiteLLM Anthropic-API proxy (used with vLLM).
-litellm_port = 4000
-
-[bootstrap]
-enable_guest_bootstrap = true
-install_wireguard = true
-configure_ufw = true
-configure_ollama_host = false
-
-[ollama]
-# Disabled in favour of vLLM; set install = true to switch back to Ollama.
-install = false
-models_dir = "/ephemeral/ollama/models"
-listen_host = "0.0.0.0:11434"
-gpu_overhead_mb = 2000
-num_parallel = 1
-context_length = 32768
-pull_models = ["qwen3-coder-next"]
-
-# vLLM serves one model via Docker; LiteLLM translates Anthropic API → OpenAI.
-# VM2 defaults to qwen3-coder-next; use 'model switch' to load any other preset.
-[vllm]
-install = true
-model = "bullpoint/Qwen3-Coder-Next-AWQ-4bit"
-# HuggingFace model cache on ephemeral NVMe (fast; survives reboots on most providers).
-hug_cache_dir = "/ephemeral/hug"
-container_name = "vllm_qwen3"
-max_model_len = 262144
-gpu_memory_utilization = 0.92
-tensor_parallel_size = 1
-tool_call_parser = "qwen3_coder"
-# LiteLLM maps each entry to the vLLM model; add new Anthropic model IDs here.
-litellm_master_key = "sk-litellm-master"
-litellm_claude_model_names = [
-  "claude-sonnet-4-20250514",
-  "claude-opus-4-20250514",
-  "claude-opus-4-6-20260604",
-  "claude-haiku-3-5-20241022"
-]
-
-# Named model presets for 'ruby hyperstack.rb --config hyperstack-vm2.toml model switch <name>'.
-# Each preset overrides the matching [vllm] field; unset fields fall back to [vllm] defaults.
-
-[vllm.presets.qwen3-coder-next]
-model = "bullpoint/Qwen3-Coder-Next-AWQ-4bit"
-container_name = "vllm_qwen3"
-max_model_len = 262144
-gpu_memory_utilization = 0.92
-tensor_parallel_size = 1
-tool_call_parser = "qwen3_coder"
-
-# NVIDIA Nemotron-3-Super-120B-A12B AWQ 4-bit — hybrid Mamba+MoE (12B active / 120B total).
-# ~60 GB weights on A100 80GB. Uses NoPE so context can be set to 1M; no YaRN needed.
-# Requires trust_remote_code=true for the nemotron_h architecture.
-[vllm.presets.nemotron-super]
-model = "cyankiwi/NVIDIA-Nemotron-3-Super-120B-A12B-AWQ-4bit"
-container_name = "vllm_nemotron_super"
-max_model_len = 262144
-gpu_memory_utilization = 0.92
-tensor_parallel_size = 1
-tool_call_parser = "qwen3_xml"
-trust_remote_code = true
-extra_vllm_args = ["--reasoning-parser", "nemotron_v3"]
-
-# OpenAI GPT-OSS 20B — ultra-fast MoE (3.6B active / 20B total, MXFP4), ~14 GB on A100.
-[vllm.presets.gpt-oss-20b]
-model = "openai/gpt-oss-20b"
-container_name = "vllm_gpt_oss_20b"
-max_model_len = 65536
-gpu_memory_utilization = 0.92
-tensor_parallel_size = 1
-tool_call_parser = ""
-
-# OpenAI GPT-OSS 120B — powerful MoE (5.1B active / 117B total, MXFP4), ~65 GB on A100.
-# Hard architecture limit: max_position_embeddings=131072 in model config.json.
-[vllm.presets.gpt-oss-120b]
-model = "openai/gpt-oss-120b"
-container_name = "vllm_gpt_oss_120b"
-max_model_len = 131072
-gpu_memory_utilization = 0.92
-tensor_parallel_size = 1
-tool_call_parser = ""
-
-# Qwen2.5-Coder-32B-Instruct AWQ — best-in-class open coding model at 32B, ~18 GB on A100.
-[vllm.presets.qwen25-coder-32b]
-model = "Qwen/Qwen2.5-Coder-32B-Instruct-AWQ"
-container_name = "vllm_qwen25_coder32b"
-max_model_len = 32768
-gpu_memory_utilization = 0.92
-tensor_parallel_size = 1
-tool_call_parser = "hermes"
-
-# Qwen3-Coder-30B-A3B AWQ — Qwen3 generation coding MoE (3B active / 30B total), ~18 GB.
-[vllm.presets.qwen3-coder-30b]
-model = "QuantTrio/Qwen3-Coder-30B-A3B-Instruct-AWQ"
-container_name = "vllm_qwen3_coder30b"
-max_model_len = 65536
-gpu_memory_utilization = 0.92
-tensor_parallel_size = 1
-tool_call_parser = "qwen3_coder"
-
-# DeepSeek-R1-Distill-Qwen-32B AWQ — R1 reasoning distillation of Qwen 32B, ~18 GB on A100.
-[vllm.presets.deepseek-r1-32b]
-model = "casperhansen/deepseek-r1-distill-qwen-32b-awq"
-container_name = "vllm_deepseek_r1_32b"
-max_model_len = 32768
-gpu_memory_utilization = 0.92
-tensor_parallel_size = 1
-tool_call_parser = ""
-extra_vllm_args = ["--reasoning-parser", "deepseek_r1"]
-
-# Qwen3-32B AWQ — dense 32B reasoning model with extended context, ~18 GB on A100.
-[vllm.presets.qwen3-32b]
-model = "Qwen/Qwen3-32B-AWQ"
-container_name = "vllm_qwen3_32b"
-max_model_len = 32768
-gpu_memory_utilization = 0.92
-tensor_parallel_size = 1
-tool_call_parser = ""
-extra_vllm_args = ["--reasoning-parser", "deepseek_r1"]
-
-# Devstral-Small-2507 AWQ — Mistral's coding agent model (~15 GB on A100).
-[vllm.presets.devstral]
-model = "cyankiwi/Devstral-Small-2507-AWQ-4bit"
-container_name = "vllm_devstral"
-max_model_len = 32768
-gpu_memory_utilization = 0.92
-tensor_parallel_size = 1
-tool_call_parser = "mistral"
-extra_vllm_args = ["--tokenizer_mode", "mistral", "--config_format", "mistral"]
-
-[wireguard]
-auto_setup = true
-setup_script = "./wg1-setup.sh"
-
-[local_client]
-check_wg1_service = true
-interface_name = "wg1"
-config_path = "/etc/wireguard/wg1.conf"