cortex/CLAUDE.md

CLAUDE.md — cortex

Project overview

cortex is a Rust reverse-proxy that sits in front of multiple mistral.rs inference nodes and presents a unified OpenAI + Anthropic compatible API surface. It handles model routing, lifecycle management (load/unload/evict), request translation, and metrics collection.

Repository layout

cortex/
├── Cargo.toml              # workspace root
├── cortex.toml      # example gateway config
├── README.md
├── CLAUDE.md               # ← you are here
├── crates/
│   ├── cortex-core/            # shared types, config, envelopes
│   │   └── src/
│   │       ├── lib.rs
│   │       ├── config.rs       # figment-based config structs
│   │       ├── node.rs         # NodeState, ModelStatus
│   │       ├── openai.rs       # OpenAI request/response types
│   │       ├── anthropic.rs    # Anthropic request/response types
│   │       ├── translate.rs    # OpenAI <-> Anthropic translation
│   │       └── metrics.rs      # RequestMetrics, histogram helpers
│   ├── cortex-gateway/         # the HTTP proxy server
│   │   └── src/
│   │       ├── lib.rs
│   │       ├── state.rs        # CortexState: Arc<RwLock<...>>
│   │       ├── router.rs       # model -> node routing logic
│   │       ├── proxy.rs        # streaming HTTP proxy to backends
│   │       ├── evictor.rs      # LRU/priority eviction logic
│   │       ├── poller.rs       # background task polling node status
│   │       ├── handlers.rs     # axum handlers (chat, completions, models, etc.)
│   │       └── metrics.rs      # prometheus exporter endpoint
│   ├── cortex-agent/           # per-node sidecar (future: defrag, restart)
│   │   └── src/
│   │       ├── lib.rs
│   │       └── agent.rs        # local node management
│   └── cortex-cli/             # CLI entrypoint
│       └── src/
│           └── main.rs
└── tests/                  # integration tests (future)

Key design decisions

mistral.rs HTTP API for model lifecycle

mistral.rs (v0.8+) supports dynamic model loading/unloading at runtime:

• POST /v1/models/unload {"model_id": "..."} — frees VRAM, preserves config
• POST /v1/models/reload {"model_id": "..."} — explicitly reload
• POST /v1/models/status {"model_id": "..."} — loaded/unloaded/reloading
• GET /v1/models — lists all models with status field
• Lazy loading: requests to unloaded models trigger automatic reload

The gateway does NOT manage systemd units for model swaps. It calls these HTTP endpoints directly. The only systemd interaction is for full-process restarts after VRAM fragmentation accumulates (defrag_after_cycles).

Streaming proxy

Chat completions are proxied as SSE streams. The gateway must:

1. Parse the inbound request to extract the model name
2. Route to the correct backend node
3. Stream the response back, capturing token timing for metrics
4. NOT buffer the full response — true streaming passthrough

Anthropic translation

When a request arrives at /v1/messages (Anthropic format), the gateway translates it to OpenAI format before proxying to mistral.rs, then translates the response back. This is stateless envelope transformation.

Eviction

The evictor runs as a background task. Before loading a model on a node where VRAM is tight:

1. Check if the model is already loaded elsewhere → route there instead
2. Find the LRU model on the target node (excluding pinned models)
3. Call /v1/models/unload on that model
4. The incoming request's lazy-load triggers the new model load

Metrics

Per-request: model, node, prompt_tokens, completion_tokens, total_tokens, tok_per_sec, time_to_first_token_ms, total_latency_ms. Exposed as Prometheus histograms/counters on a separate port.

Tech stack

• Rust 2024 edition — workspace with 4 crates
• Axum 0.8 — HTTP framework (same as mistral.rs itself)
• reqwest — HTTP client for proxying to backends
• figment — config loading (TOML + env vars)
• tokio — async runtime
• metrics + metrics-exporter-prometheus — observability
• tracing — structured logging

Build commands

cargo build --release           # build all crates
cargo run -p cortex-cli -- serve    # run the gateway
cargo test                      # run all tests
cargo clippy --workspace        # lint

CI

Gitea Actions runs on every push to any branch. All three checks must pass before merging:

cargo fmt --check --all                    # formatting
cargo clippy --workspace -- -D warnings   # lint (warnings are errors)
cargo test --workspace                     # tests

Run these locally before pushing. cargo fmt --all fixes formatting automatically. Clippy warnings must be resolved, not suppressed with #[allow(...)] unless there is a clear rationale.

Environment

• Targets Fedora 43 (systemd, SELinux enforcing)
• Nodes communicate over a private network (e.g. WireGuard mesh)
  • One or more GPU nodes running mistral.rs on port 8080
  • Optionally a metrics-only node (no GPU) for Prometheus/Grafana
• Each node runs mistralrs serve on port 8080
• Gateway listens on port 8000 (API) and 9100 (metrics)
• TLS terminated at gateway or via nginx; internal traffic is plaintext over WireGuard

Conventions

• Error handling: anyhow for binaries, thiserror for library crates
• No unwrap() in library code; expect() only with clear rationale
• All public types derive Debug, Clone, Serialize, Deserialize where sensible
• Config structs use figment with TOML as primary source, env vars as override
• Prefer Arc<RwLock<...>> for shared fleet state; minimize lock duration
• SSE streaming uses tokio_stream + eventsource-stream for parsing
• Log at info for request routing, debug for proxy details, warn for eviction and node health, error for proxy failures

mistral.rs API gotchas

These are sharp edges Claude Code will hit when implementing the proxy. Read before touching proxy.rs or handlers.rs.

Model name validation

mistral.rs validates that the model field in every request matches the model that was actually loaded. If the names don't match, the request is rejected outright. The special model name "default" bypasses this validation entirely.

Implication for cortex: The gateway must ensure the model field in the proxied request body matches what mistral.rs expects. Two strategies:

1. Passthrough — the client uses the exact HuggingFace model ID (e.g. Qwen/Qwen3-Coder-30B-A3B-Instruct) and cortex routes based on that. This is the simplest approach and should be the default.
2. Rewrite to "default" — if cortex introduces its own model aliases, it must rewrite the model field to "default" before proxying. This is a future feature, not phase 1.

Lazy loading latency

When a request hits an unloaded model, mistral.rs automatically reloads it before processing. This can take 10-60+ seconds for large models. The gateway must:

• Set a generous HTTP client timeout (already 300s in the scaffold).
• Mark the request as cold_start: true in metrics.
• Not retry or time out prematurely — the upstream is busy loading, not dead.

SSE stream format

mistral.rs streams use standard OpenAI SSE format:

data: {"id":"...","choices":[{"delta":{"content":"token"},...}]}\n\n
data: [DONE]\n\n

The proxy must forward these chunks verbatim. Do not attempt to parse or re-serialize each chunk — that adds latency and risks breaking the stream. Parse only for metrics extraction (token counts from the final usage object, timing from chunk arrival).

Multi-model mode

mistralrs serve can load multiple models when started with a selector config or multiple --text-model / --vision-model flags. The /v1/models response lists all of them with a status field. When sending requests, the model field must match one of the listed model IDs — "default" only works if you don't care which model handles it.

Unload preserves config

POST /v1/models/unload frees VRAM but keeps the model's config in memory. A subsequent request to that model (or explicit reload) will reload from disk/HF cache — not re-download. This is fast relative to initial download but still involves loading weights into VRAM.

Implementation plan

Each phase is a branch → PR. CI must pass (fmt, clippy, test) before merge. Phases are sequential — each builds on the previous.

Phase 1: Compile and proxy a basic request ✅

Completed. 6 integration tests in cortex-gateway/tests/proxy_basic.rs: chat completion proxy, health endpoint, list models, model not found, no healthy nodes, missing model field. Test helpers in tests/common/mod.rs provide spawn_mock_backend() and spawn_gateway() using axum as the mock mistral.rs backend.

Phase 2: Streaming SSE passthrough ✅

Completed. The existing Body::from_stream(bytes_stream()) proxy works for SSE out of the box. 2 integration tests in cortex-gateway/tests/streaming.rs:

• test_streaming_sse_passthrough — 5 chunks with 50ms delays, verifies incremental delivery (time spread between first and last chunk)
• test_streaming_done_terminator — verifies data: [DONE] is forwarded

Phase 3: Poller + live /v1/models ✅

Completed. Extracted poll_once() from poll_loop() for testability. 4 tests in cortex-gateway/tests/poller.rs:

• test_poller_discovers_models — 2 models (loaded + unloaded) discovered with correct status
• test_poller_updates_gateway_models_endpoint — /v1/models reflects polled state with node attribution
• test_poller_marks_unreachable_node_unhealthy — unreachable node flipped to unhealthy
• test_poller_removes_stale_models — model removed from upstream is pruned from state

Phase 4: Eviction ✅

Completed. Added last_accessed tracking in handlers (touch_model called after routing). 5 tests in cortex-gateway/tests/eviction.rs:

• test_evict_lru_model — older model evicted, unload call verified on mock
• test_eviction_skips_pinned_models — pinned model protected, newer model evicted instead
• test_eviction_nothing_to_evict — all models pinned, returns None
• test_eviction_increments_lifecycle_cycles — counter incremented after eviction
• test_last_accessed_updated_on_request — last_accessed set after proxied request

Router-triggered eviction (automatic eviction on VRAM pressure during request routing) deferred — requires per-model VRAM tracking which is not yet populated. The evict_lru_on_node function is callable and tested for when that integration is added.

Phase 5: Anthropic translation ✅

Completed. Non-streaming Anthropic round-trip implemented: handler buffers upstream OpenAI response, translates via openai_to_anthropic, returns Anthropic-format JSON. 5 tests in cortex-gateway/tests/anthropic.rs:

• test_anthropic_to_openai_round_trip — full request/response translation with stop_reason mapping ("stop" → "end_turn") and usage field names
• test_anthropic_with_system_prompt — system field translated to system message
• test_anthropic_with_content_blocks — array content blocks handled
• test_anthropic_model_not_found — 404 for unknown model
• test_anthropic_invalid_request — 400 for malformed request

Streaming Anthropic SSE translation (OpenAI SSE → Anthropic SSE event types) deferred as a follow-up.

Phase 6: Metrics instrumentation ✅

Completed. Added proxy_with_metrics helper in handlers that wraps every proxy call with timing and counters. All three handler paths (chat completions, completions, Anthropic messages) instrumented.

Metrics emitted per request (with model and node labels):

• cortex_requests_total — incremented on every proxy attempt
• cortex_request_duration_seconds — histogram of successful request latency
• cortex_request_errors_total — incremented on proxy failures
• cortex_cold_starts_total — incremented when routing to an unloaded model

Added install_test_recorder() for testing without the HTTP listener. 1 test in cortex-gateway/tests/metrics.rs verifies counters and histograms appear after a proxied request.

Token-level metrics (tok/s, TTFT) deferred — requires parsing the response body or final SSE chunk, which is Phase 6b work.

2026-04-15 addendum

Phases 1–6 complete. The gateway proxies requests (streaming and non-streaming), routes by model name to the correct node, polls node /v1/models for live state, evicts LRU models with pinning, translates Anthropic ↔ OpenAI envelopes, and emits Prometheus metrics. CI is green.

Phase 7 onward introduces neuron — the per-node daemon that replaces the placeholder cortex-agent crate — along with hardware discovery, a harness abstraction (so cortex is not permanently wedded to mistral.rs), and a model catalogue for placement decisions.

Architecture: cortex + neuron

cortex is the control plane. It exposes the unified API, routes requests, manages model lifecycle across the fleet, and collects metrics.

neuron is the node plane. One instance runs on every GPU host. It:

• Discovers local hardware (GPU count, types, VRAM, CUDA compute capability, driver version) and reports it to cortex.
• Manages harnesses — inference engines like mistral.rs, llama.cpp, or ComfyUI. Each harness is a trait implementation. neuron starts, stops, health-checks, and proxies to whichever harness is serving a given model.
• Manages model lifecycle — load, unload, status — abstracting the differences between harnesses (mistral.rs has HTTP lifecycle endpoints; llama.cpp may need process management).
• Reports runtime state — per-device VRAM usage, GPU utilisation, temperature, loaded models with actual VRAM consumption.

cortex never shells out to nvidia-smi, never touches systemd units, and never talks directly to a harness. It talks only to neurons.

                    ┌─────────────────────┐
                    │      cortex         │
                    │  (cortex-gateway)   │
                    │  Router · Evictor   │
                    │  Metrics · Translate│
                    └──┬──────┬────────┬──┘
                       │      │        │
            ┌──────────▼┐  ┌──▼─────┐  ┌▼──────────┐
            │  neuron   │  │ neuron │  │  neuron   │
            │  beast    │  │ benjy  │  │ quadbrat  │
            │           │  │        │  │           │
            │ harness:  │  │harness:│  │ harness:  │
            │ mistralrs │  │mistral │  │ mistralrs │
            │ (+ comfy) │  │rs      │  │           │
            └───────────┘  └────────┘  └───────────┘

The Harness trait

Defined in cortex-core so both cortex and neuron share the type definitions. neuron provides the runtime implementations.

/// What an inference harness must do, from neuron's perspective.
#[async_trait]
pub trait Harness: Send + Sync {
    /// Human-readable name (e.g. "mistralrs", "llamacpp", "comfyui").
    fn name(&self) -> &str;

    /// Start the harness process if it is not already running.
    async fn start(&self, config: &HarnessConfig) -> Result<()>;

    /// Stop the harness process gracefully.
    async fn stop(&self) -> Result<()>;

    /// Health check. Returns the harness process status.
    async fn health(&self) -> HarnessHealth;

    /// List models the harness knows about (loaded + unloaded).
    async fn list_models(&self) -> Result<Vec<ModelInfo>>;

    /// Load a model with the given spec (quant, TP, device assignment).
    async fn load_model(&self, spec: &ModelSpec) -> Result<()>;

    /// Unload a model, freeing device memory.
    async fn unload_model(&self, model_id: &str) -> Result<()>;

    /// Return the URL where inference requests for this model should
    /// be sent. None if the model is not loaded.
    async fn inference_endpoint(&self, model_id: &str) -> Option<String>;
}

The mistral.rs implementation wraps the HTTP API:

• list_models → GET /v1/models
• load_model → POST /v1/models/reload
• unload_model → POST /v1/models/unload
• inference_endpoint → returns the base URL (the model name routes internally within mistral.rs)
• start/stop → manage the mistralrs.service systemd unit

A future llama.cpp implementation would manage per-model llama-server processes (one process per loaded model, each on its own port).

neuron API

neuron exposes an HTTP API on port 9090 that cortex polls and calls.

GET  /discovery
     → {
         hostname, os, kernel,
         cuda_version, driver_version,
         devices: [{ index, name, vram_total_mb, compute_capability }],
         harnesses: ["mistralrs", ...]
       }

GET  /health
     → {
         uptime_secs,
         devices: [{ index, vram_used_mb, vram_free_mb, utilization_pct, temp_c }]
       }

GET  /models
     → [{ id, harness, status, devices: [int], vram_used_mb }]

POST /models/load
     ← { model_id, harness, quant, tensor_parallel, devices: [int] }
     → { status: "loaded" | "loading" }

POST /models/unload
     ← { model_id }
     → { status: "unloaded" }

GET  /models/{model_id}/endpoint
     → { url: "http://localhost:8080" }

cortex never constructs a harness-specific URL. It asks neuron for the inference endpoint and proxies there.

Discovery replaces static device config

cortex.toml no longer contains device types, VRAM sizes, or CUDA architectures. That information comes from neuron's /discovery endpoint. cortex.toml shrinks to:

[gateway]
listen = "0.0.0.0:8000"
metrics_listen = "0.0.0.0:9100"

[eviction]
strategy = "lru"
defrag_after_cycles = 50

[[neurons]]
name = "beast"
endpoint = "http://beast.hanzalova.internal:9090"

[[neurons]]
name = "benjy"
endpoint = "http://benjy.kosherinata.internal:9090"

[[neurons]]
name = "quadbrat"
endpoint = "http://quadbrat.hanzalova.internal:9090"

On startup and periodically, cortex calls GET /discovery and GET /health on each neuron to build its topology map. The router uses this topology — not config — to make placement decisions.

Model catalogue

Model serving profiles live in a separate file (models.toml) because they describe how to serve a model, not where. cortex matches these profiles against the discovered topology to determine valid placements.

[[models]]
id = "Qwen/Qwen3-Coder-30B-A3B-Instruct"
harness = "mistralrs"
quant = "Q4_K_M"
vram_mb = 19000
min_devices = 2
min_device_vram_mb = 10000
pinned_on = ["beast"]       # optional: never evict from these neurons

[[models]]
id = "Qwen/Qwen3-VL-8B"
harness = "mistralrs"
quant = "Q8_0"
vram_mb = 10000
min_devices = 1

[[models]]
id = "Qwen/Qwen2.5-Coder-14B-Instruct"
harness = "mistralrs"
quant = "Q6_K"
vram_mb = 12000
min_devices = 1
pinned_on = ["benjy"]

The router consults the catalogue to answer: "model X needs 2 devices with ≥10GB each; beast has 2× RTX 5090 at 32GB each; that's a valid placement." This replaces the current per-node pinned list in config and the hardcoded vram_mb per node.

Revised repository layout

cortex/
├── Cargo.toml
├── cortex.toml                 # gateway config (neurons only)
├── models.toml                 # model catalogue
├── README.md
├── CLAUDE.md
├── crates/
│   ├── cortex-core/            # shared types
│   │   └── src/
│   │       ├── lib.rs
│   │       ├── config.rs       # GatewayConfig, NeuronEndpoint
│   │       ├── catalogue.rs    # ModelProfile, placement matching
│   │       ├── discovery.rs    # DeviceInfo, DiscoveryResponse
│   │       ├── harness.rs      # Harness trait, HarnessConfig, HarnessHealth
│   │       ├── node.rs         # NodeState, ModelEntry, ModelStatus
│   │       ├── openai.rs       # OpenAI envelope types
│   │       ├── anthropic.rs    # Anthropic envelope types
│   │       ├── translate.rs    # OpenAI <-> Anthropic translation
│   │       └── metrics.rs      # RequestMetrics
│   ├── cortex-gateway/         # control plane (existing, modified)
│   │   └── src/
│   │       ├── lib.rs
│   │       ├── state.rs        # CortexState (updated: discovery topology)
│   │       ├── router.rs       # updated: catalogue + discovery placement
│   │       ├── proxy.rs        # streaming proxy (unchanged)
│   │       ├── evictor.rs      # updated: talks to neuron, not mistralrs
│   │       ├── poller.rs       # updated: polls neuron, not mistralrs
│   │       ├── handlers.rs     # axum handlers (unchanged API surface)
│   │       └── metrics.rs      # prometheus exporter (unchanged)
│   ├── neuron/                 # node plane (replaces cortex-agent)
│   │   └── src/
│   │       ├── main.rs         # binary entrypoint, axum server on :9090
│   │       ├── discovery.rs    # nvidia-smi, device enumeration
│   │       ├── health.rs       # runtime GPU polling
│   │       ├── api.rs          # HTTP handlers for /discovery, /models, etc.
│   │       ├── harness/
│   │       │   ├── mod.rs      # Harness trait re-export, registry
│   │       │   ├── mistralrs.rs  # mistral.rs HTTP API wrapper
│   │       │   └── llamacpp.rs   # stub for future llama.cpp support
│   │       └── models.rs       # local model lifecycle orchestration
│   └── cortex-cli/             # CLI entrypoint (unchanged)
│       └── src/
│           └── main.rs
└── tests/

The cortex-agent crate is deleted. Its replacement is neuron/.

Implementation plan (phases 7+)

Phases 1–6 are merged and passing CI. Each subsequent phase is a branch → PR. CI (fmt, clippy, test) must pass before merge.

Phase 7: neuron scaffold and discovery ✅

Completed. Deleted cortex-agent, created crates/neuron/ (binary: cortex-neuron). Added shared types to cortex-core: discovery.rs (DeviceInfo, DiscoveryResponse, DeviceHealth, HealthResponse) and harness.rs (Harness async trait, HarnessConfig, ModelSpec, ModelInfo).

neuron discovers GPUs via nvidia-smi, caches health readings, and serves GET /discovery and GET /health. Pure parsing functions separated from command execution for testability. 9 unit tests for nvidia-smi CSV parsing, 3 integration tests for the HTTP endpoints.

Phase 8: neuron harness — mistral.rs implementation

Goal: neuron can manage mistral.rs: start/stop the process, list models, load/unload models, and report the inference endpoint.

Steps:

1. In crates/neuron/src/harness/mistralrs.rs:
   • Implement the Harness trait.
   • start() — invoke systemctl start mistralrs.service (or a configured unit name). Wait for the health endpoint to respond.
   • stop() — systemctl stop mistralrs.service.
   • health() — GET {mistralrs_endpoint}/health.
   • list_models() — GET {mistralrs_endpoint}/v1/models, parse the response including the status field.
   • load_model() — POST {mistralrs_endpoint}/v1/models/reload.
   • unload_model() — POST {mistralrs_endpoint}/v1/models/unload.
   • inference_endpoint() — return mistralrs_endpoint (mistral.rs routes internally by model name in the request body).
2. In crates/neuron/src/harness/mod.rs:
   • A HarnessRegistry that maps harness name → Box<dyn Harness>.
   • On neuron startup, register the mistralrs harness (configured with the local mistralrs endpoint, e.g. http://localhost:8080).
3. Add neuron API endpoints:
   • GET /models — aggregate across all registered harnesses.
   • POST /models/load — dispatch to the correct harness.
   • POST /models/unload — dispatch to the correct harness.
   • GET /models/{model_id}/endpoint — ask the harness.
4. neuron config (neuron.toml):

   port = 9090
   
   [[harnesses]]
   name = "mistralrs"
   endpoint = "http://localhost:8080"
   systemd_unit = "mistralrs.service"
   

5. Tests:
   • Mock HTTP server standing in for mistral.rs. Test that the harness implementation correctly translates list/load/unload calls.
   • Integration test: start neuron with mock mistralrs backend, call GET /models, assert it returns models from the mock.

Done when: neuron manages a (mock) mistral.rs instance. All API endpoints return correct data. Tests pass.

Phase 9: cortex talks to neurons

Goal: cortex-gateway's poller, router, and evictor talk to neuron instead of directly to mistral.rs. Discovery replaces static config.

Steps:

1. Update cortex-core/src/config.rs:
   • Replace NodeConfig { endpoint, vram_mb, pinned } with NeuronEndpoint { name, endpoint }.
   • Add ModelCatalogue loaded from models.toml.
   • Remove per-node vram_mb and pinned fields (these come from discovery and the catalogue respectively).
2. Add cortex-core/src/catalogue.rs:
   • ModelProfile { id, harness, quant, vram_mb, min_devices, min_device_vram_mb, pinned_on }.
   • fn find_valid_placements(profile, discovered_nodes) -> Vec<PlacementOption> that matches a model profile against discovered topologies.
3. Update cortex-gateway/src/state.rs:
   • CortexState holds discovered topology per neuron (devices, VRAM, harnesses) alongside the existing model status map.
4. Update cortex-gateway/src/poller.rs:
   • Poll GET {neuron}/discovery on startup and every 60s (topology changes rarely).
   • Poll GET {neuron}/health every 10s (VRAM usage, utilisation).
   • Poll GET {neuron}/models every 10s (model status).
   • Merge all three into CortexState.
5. Update cortex-gateway/src/router.rs:
   • resolve() now consults the model catalogue to determine valid placements, then picks the best node (loaded > unloaded-on-capable-node).
   • For models needing TP=2, only nodes with ≥2 devices are candidates.
6. Update cortex-gateway/src/evictor.rs:
   • evict_lru_on_node() calls POST {neuron}/models/unload instead of calling mistral.rs directly.
   • Eviction respects pinned_on from the catalogue.
7. Update cortex-gateway/src/proxy.rs:
   • Before proxying, ask neuron for the inference endpoint: GET {neuron}/models/{model_id}/endpoint. This decouples cortex from knowing which port or harness is serving the model.
8. Tests:
   • Update existing integration tests to use a mock neuron (mock /discovery, /health, /models, /models/load, etc.) instead of a mock mistralrs.
   • New test: model catalogue placement — profile requires TP=2, assert it only routes to a node with ≥2 discovered devices.
   • New test: eviction calls neuron's unload endpoint, not mistralrs.

Done when: cortex has zero direct references to mistral.rs endpoints. All existing tests are updated and pass. New placement tests pass. cortex.toml only contains neuron endpoints. models.toml drives placement and pinning.

Phase 10: neuron packaging (RPM)

Goal: neuron and cortex are installable via dnf from the grenade COPR repo.

Steps:

1. neuron.spec — RPM spec file for the neuron binary. Install to /usr/libexec/cortex/neuron. Systemd unit cortex-neuron.service. Config at /etc/cortex/neuron.toml.
2. Update cortex.spec — ensure the cortex binary, config, and models.toml are packaged correctly.
3. Gitea Actions CI job: on tag push, build SRPM, submit to COPR.
4. Document the install path:

   dnf copr enable grenade/cortex
   # on the gateway host:
   dnf install cortex
   # on each GPU node:
   dnf install cortex-neuron
   

Done when: dnf install cortex-neuron on a Fedora 43 host drops the binary, config, and systemd unit. systemctl start cortex-neuron runs discovery and serves /discovery.

Phase 11: llama.cpp harness stub

Goal: Prove the harness abstraction works with a second engine.

Steps:

1. crates/neuron/src/harness/llamacpp.rs — implement the Harness trait for llama.cpp's llama-server.
   • start() — launch llama-server with the correct model path, --port, --n-gpu-layers, --tensor-split args. Track the child process.
   • stop() — send SIGTERM to the child process.
   • list_models() — llama-server serves one model per process, so return a single-element list.
   • load_model() — start a new llama-server process for this model.
   • unload_model() — stop the process.
   • inference_endpoint() — return http://localhost:{assigned_port}.
2. Port allocation: neuron assigns ports from a range (e.g. 8100-8199) to llama-server instances.
3. Register in HarnessRegistry when configured:

   [[harnesses]]
   name = "llamacpp"
   binary = "/usr/local/bin/llama-server"
   port_range = [8100, 8199]
   

4. Tests: mock llama-server (simple HTTP server returning canned responses), test load/unload/endpoint lifecycle.

Done when: A model with harness = "llamacpp" in models.toml can be loaded and served through cortex. Tests pass with mock llama-server.

Phase 12 (lower priority): mistral.rs COPR packaging

Goal: Fedora RPMs for mistral.rs built against specific CUDA versions.

Steps:

1. mistralrs-cuda.spec — RPM spec that clones a pinned mistral.rs git tag, builds with --features cuda, links against the system CUDA toolkit. Produces mistralrs-cuda13-server (CUDA 13.x / sm_120) and mistralrs-cuda12-server (CUDA 12.x / sm_89). Install binary to /usr/local/bin/mistralrs.
2. COPR build config: enable the NVIDIA CUDA repo as a build dependency. Pin the CUDA toolkit version in BuildRequires.
3. Gitea Actions or manual workflow: bump the mistral.rs tag in the spec, trigger COPR rebuild.
4. neuron's mistralrs harness config references which binary/package provides the mistral.rs binary. neuron could warn at startup if the installed mistral.rs CUDA version doesn't match the discovered driver.

Done when: dnf install mistralrs-cuda13-server on beast provides a working mistralrs binary built for Blackwell GPUs. dnf install mistralrs-cuda12-server on benjy provides one built for Ada GPUs.

This is a separate repo/spec — not part of the cortex workspace — but tightly coupled operationally. Track it as a sibling project.