path: root/hyperstack-vm.toml

[auth]
api_key_file = "~/.hyperstack"

[hyperstack]
base_url = "https://infrahub-api.nexgencloud.com/v1"

[state]
file = ".hyperstack-vm-state.json"

[vm]
name_prefix = "hyperstack"
hostname = "hyperstack"
environment_name = "snonux-ollama"

# A100-80GB is the cost-first default for gpt-oss-120b 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 = ["gpt-oss-120b", "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"
# 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.
# Use --vllm / --no-vllm CLI flags to override install at runtime.
[vllm]
install = true
model = "openai/gpt-oss-120b"
# HuggingFace model cache on ephemeral NVMe (fast; survives reboots on most providers).
hug_cache_dir = "/ephemeral/hug"
container_name = "vllm_gpt_oss_120b"
# Hard architecture limit: max_position_embeddings=131072 in model config.json.
max_model_len = 131072
gpu_memory_utilization = 0.92
tensor_parallel_size = 1
# tool_call_parser="" disables --enable-auto-tool-choice; the llama3_json parser crashes
# on gpt-oss responses (vLLM 0.17.1 adds token_ids to responses, breaking the parser API).
tool_call_parser = ""

# Named model presets for 'ruby hyperstack.rb model switch <name>'.
# Each preset overrides the matching [vllm] field; unset fields fall back to [vllm] defaults.
# Switch examples:
#   ruby hyperstack.rb model switch qwen3-coder-next  # fast coding, 256k context
#   ruby hyperstack.rb model switch nemotron-super     # extended analysis, 131k context

[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 (no positional embeddings) so context can be set to
# 1M by just raising max_model_len; no YaRN needed. May OOM above 256K on A100 80GB.
# Requires trust_remote_code=true for the nemotron_h architecture.
# Note: cyankiwi AWQ has model_type="nemotron_nas" (underscore); vLLM keys on "nemotron-nas"
# (hyphen), so vLLM may not recognise it without trust_remote_code and latest vLLM.
# NVIDIA Nemotron-3-Super uses the same XML tool call format as Qwen3 XML:
# <tool_call><function=name><parameter=p>value</parameter></function></tool_call>
# qwen3_xml handles this format and is compatible with Nemotron's chat template.
[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
# nemotron_v3 reasoning parser exposes <think> tokens as reasoning_content in the API.
extra_vllm_args = ["--reasoning-parser", "nemotron_v3"]

# OpenAI GPT-OSS 20B — ultra-fast MoE (3.6B active / 20B total, MXFP4), ~14 GB on A100.
# Native MXFP4 quantization; vLLM auto-detects it (no --quantization flag needed).
# With only 14 GB weights, most of the 80 GB is available for KV cache (64K+ context).
# tool_call_parser = "" disables --enable-auto-tool-choice: the llama3_json parser crashes
# on gpt-oss responses (vLLM 0.17.1 adds token_ids to responses, breaking the parser API).
[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.
# 131072 is the absolute ceiling — exceeding it causes NaN or CUDA OOB errors.
# For sessions approaching this limit, start a fresh Pi conversation.
# tool_call_parser = "" disables --enable-auto-tool-choice (same reason as gpt-oss-20b).
[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.
# Official Qwen AWQ release; max_position_embeddings=32768 per model config.json.
[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.
# Note: model card warns of significant quality loss at 4-bit for this MoE architecture.
[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.
# Generates <think> reasoning tokens; --reasoning-parser deepseek_r1 exposes them in the API.
# tool_call_parser="" disables tool calling (reasoning models don't support it reliably).
[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.
# Native thinking mode; --reasoning-parser deepseek_r1 is compatible with Qwen3 thinking format.
# tool_call_parser="" disables tool calling (reasoning models don't support it reliably).
[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).
# Uses HF safetensors weights but Mistral tokenizer (tekken.json) and config (params.json).
# --load_format mistral is NOT used: AWQ weights are in standard HF safetensors format.
# --tokenizer_mode mistral and --config_format mistral handle the Mistral-native files.
[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"