Files
cortex/cortex.example.toml
rob thijssen 3f94c50817 chore: move default ports out of common-collision ranges
Previous defaults collided with well-trodden infra services and with
the Linux ephemeral port range:

- cortex API     8000 — common dev-server default (Django, minio UI)
- cortex metrics 9100 — Prometheus node_exporter default
- neuron API     9090 — Cockpit default on Fedora, Prometheus self

Move to helexa-themed palindromic ports, all below Linux's
32768-60999 ephemeral range and not registered to any well-known
service:

- cortex API     31313
- cortex metrics 31314
- neuron API     13131

Updated places:
- cortex.example.toml, neuron.example.toml defaults
- default impls in cortex-core and neuron config
- cortex-cli --endpoint default for the status subcommand
- doc comments citing example URLs
- README.md and CLAUDE.md snippets

Consumers already on the old ports need a one-line edit in their
/etc/cortex/cortex.toml or /etc/neuron/neuron.toml to match;
firewall rules and prometheus scrape configs will also need
updating.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-16 17:45:25 +03:00

46 lines
1.2 KiB
TOML

# cortex.example.toml — example configuration
#
# Copy to cortex.toml and adjust for your environment.
#
# Environment variable overrides use CORTEX_ prefix with __ separators:
# CORTEX_GATEWAY__LISTEN=0.0.0.0:31313
[gateway]
listen = "0.0.0.0:31313"
metrics_listen = "0.0.0.0:31314"
[eviction]
strategy = "lru"
# Restart mistralrs after this many load/unload cycles to defragment VRAM.
# Set to 0 to disable.
defrag_after_cycles = 50
# -- Nodes ---------------------------------------------------------------
# Each [[nodes]] entry declares a mistral.rs instance in the fleet.
# Models are discovered by polling the node's /v1/models endpoint.
# Pinned models are never evicted.
[[nodes]]
name = "gpu-large"
endpoint = "http://gpu-large.internal:8080"
vram_mb = 49152 # e.g. 2x RTX 4090 (48 GB combined)
pinned = [
"your-org/large-model",
]
[[nodes]]
name = "gpu-medium"
endpoint = "http://gpu-medium.internal:8080"
vram_mb = 24576 # e.g. RTX 4090 (24 GB)
pinned = [
"your-org/medium-model",
]
[[nodes]]
name = "gpu-small"
endpoint = "http://gpu-small.internal:8080"
vram_mb = 12288 # e.g. RTX 3060 (12 GB)
pinned = [
"your-org/embedding-model",
]