Rename VM1 configs: default hyperstack-vm1.toml, Nemotron in -nemotron

Move the former hyperstack-vm1-coder.toml to hyperstack-vm1.toml as the
standard VM1 profile (Qwen3-Coder-Next on single GPU). Preserve the
dual-H100 Nemotron-3-Super stack as hyperstack-vm1-nemotron.toml. Point
create-both at hyperstack-vm1.toml and refresh README for current defaults.

Made-with: Cursor

1 files changed, 0 insertions, 188 deletions

diff --git a/hyperstack-vm1-coder.toml b/hyperstack-vm1-coder.toml
deleted file mode 100644
index cd127dd..0000000
--- a/hyperstack-vm1-coder.toml
+++ /dev/null
@@ -1,188 +0,0 @@
-[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"