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+[auth]
+api_key_file = "~/.hyperstack"
+
+[hyperstack]
+base_url = "https://infrahub-api.nexgencloud.com/v1"
+
+[state]
+# Separate state file for VM1 so vm1 and vm2 can be managed independently.
+file = ".hyperstack-vm1-state.json"
+
+[vm]
+name_prefix = "hyperstack1"
+hostname = "hyperstack1"
+environment_name = "snonux-ollama"
+
+# A100-80GB single GPU for qwen3-coder-next (default); other models available via presets.
+flavor_name = "n3-H100x1"
+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"
+# VM1 gets the first server-side WireGuard IP (gateway address + 0).
+# earth (client) is 192.168.3.2; VM1 is 192.168.3.1; VM2 is 192.168.3.3.
+wireguard_server_ip = "192.168.3.1"
+# 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
+
+[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", "qwen3-coder:30b", "gpt-oss:20b", "gpt-oss:120b", "nemotron-3-super"]
+
+# vLLM serves one model via Docker on the OpenAI-compatible API.
+# VM1 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"
+
+# Named model presets for 'ruby hyperstack.rb --config hyperstack-vm1-gptoss.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).
+# Single-GPU (A100-80GB) config: tensor_parallel_size=1, context capped at 32k to fit in VRAM.
+# Model weights occupy ~73.6 GiB of the 79.25 GiB A100; very little VRAM remains for KV cache.
+# enforce_eager=true disables CUDA graph capture, which avoids the large profiling-phase OOM.
+# gpu_memory_utilization=0.98 lets vLLM use nearly all available VRAM for KV blocks.
+# max_model_len reduced to 32768 to keep the KV cache footprint small enough to fit.
+[vllm.presets.nemotron-super]
+model = "cyankiwi/NVIDIA-Nemotron-3-Super-120B-A12B-AWQ-4bit"
+container_name = "vllm_nemotron_super"
+max_model_len = 32768
+gpu_memory_utilization = 0.98
+tensor_parallel_size = 1
+tool_call_parser = "qwen3_xml"
+trust_remote_code = true
+enable_prefix_caching = false
+extra_docker_env = ["VLLM_ALLOW_LONG_MAX_MODEL_LEN=1", "PYTORCH_ALLOC_CONF=expandable_segments:True"]
+extra_vllm_args = ["--reasoning-parser", "nemotron_v3", "--enforce-eager"]
+
+# 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 = ""
+extra_vllm_args = ["--reasoning-parser", "openai_gptoss"]
+
+# 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"]
+
+# Gemma 4 31B AWQ 4-bit — Google's dense 31B multimodal model (~19 GB weights on A100-80GB).
+# ~61 GB VRAM remaining for KV cache; supports up to ~32K context comfortably.
+# Uses vLLM's gemma4 tool-call parser for function calling support.
+[vllm.presets.gemma4-31b]
+model = "cyankiwi/gemma-4-31B-it-AWQ-4bit"
+container_name = "vllm_gemma4_31b"
+max_model_len = 32768
+gpu_memory_utilization = 0.92
+tensor_parallel_size = 1
+tool_call_parser = "gemma4"
+
+[wireguard]
+auto_setup = true
+setup_script = "./wg1-setup.sh"
+
+[local_client]
+check_wg1_service = true
+interface_name = "wg1"
+config_path = "/etc/wireguard/wg1.conf"