Expanded node agent llama-server parameters coverage.

configs/node_agent.example.yaml

@@ -22,11 +22,12 @@ models:
     path: "/models/SomePlannerModel.Q5_K_M.gguf"
     roles: ["planner"]
     default_ctx: 8192
+    # Common llama-server options can be configured as structured fields:
+    ctx_size: 8192
+    gpu_layers: 60
+    threads: 8
+    batch_size: 1024
+    flash_attn: true
+    # Keep server_args for less common passthrough flags.
     server_args:
-      # Examples (llama.cpp flags differ by build/version):
-      # c: 8192
-      # n_gpu_layers: 60
-      # threads: 8
-      # parallel: 1
-      # keep: true
-      c: 8192
+      parallel: 1

docs/NODE_AGENT.md

@@ -22,6 +22,41 @@ models:
 - `path`: exact GGUF file to load
 - `roles`: role labels this model can satisfy when the node registers with a gateway
 
+## Common llama-server options as structured config
+
+For common runtime tuning, the node agent supports structured model fields instead of requiring everything to go
+through raw `server_args`.
+
+Supported structured fields:
+- `ctx_size`
+- `batch_size`
+- `ubatch_size`
+- `threads`
+- `threads_batch`
+- `gpu_layers`
+- `main_gpu`
+- `tensor_split`
+- `flash_attn`
+- `alias`
+
+Example:
+
+```yaml
+models:
+  - model_id: "planner-gguf"
+    path: "/models/SomePlannerModel.Q5_K_M.gguf"
+    roles: ["planner"]
+    ctx_size: 8192
+    gpu_layers: 60
+    threads: 8
+    batch_size: 1024
+    flash_attn: true
+    server_args:
+      parallel: 1
+```
+
+`server_args` is still supported as an escape hatch for less common `llama-server` flags.
+
 ## Persistent server model
 
 For each GPU device, the node agent starts a dedicated `llama-server` process, pinned via

src/rolemesh_node_agent/adapters/cuda.py

@@ -12,6 +12,7 @@ from dataclasses import dataclass
 from typing import Any, Dict, List
 
 from .base import DeviceMetrics, DeviceRef, RuntimeAdapter
+from ..config import ModelEntry
 
 
 def _find_free_port() -> int:
@@ -117,6 +118,31 @@ class CudaAdapter(RuntimeAdapter):
             metrics.append(m)
         return metrics
 
+    def build_server_args(self, model: ModelEntry) -> Dict[str, Any]:
+        args: Dict[str, Any] = {"ctx-size": model.ctx_size or model.default_ctx}
+
+        if model.batch_size is not None:
+            args["batch-size"] = model.batch_size
+        if model.ubatch_size is not None:
+            args["ubatch-size"] = model.ubatch_size
+        if model.threads is not None:
+            args["threads"] = model.threads
+        if model.threads_batch is not None:
+            args["threads-batch"] = model.threads_batch
+        if model.gpu_layers is not None:
+            args["gpu-layers"] = model.gpu_layers
+        if model.main_gpu is not None:
+            args["main-gpu"] = model.main_gpu
+        if model.tensor_split:
+            args["tensor-split"] = ",".join(f"{v:g}" for v in model.tensor_split)
+        if model.flash_attn is not None:
+            args["flash-attn"] = "on" if model.flash_attn else "off"
+        if model.alias:
+            args["alias"] = model.alias
+
+        args.update(model.server_args or {})
+        return args
+
     async def ensure_server(self, device: DeviceRef, *, model_path: str, model_id: str, server_args: Dict[str, Any]) -> str:
         if device.backend != "cuda" or device.kind != "gpu":
             raise ValueError("CudaAdapter can only manage cuda gpu devices.")

src/rolemesh_node_agent/config.py

@@ -11,6 +11,16 @@ class ModelEntry(BaseModel):
     path: Path
     roles: List[str] = Field(default_factory=list)
     default_ctx: int = 8192
+    ctx_size: Optional[int] = None
+    batch_size: Optional[int] = None
+    ubatch_size: Optional[int] = None
+    threads: Optional[int] = None
+    threads_batch: Optional[int] = None
+    gpu_layers: Optional[int] = None
+    main_gpu: Optional[int] = None
+    tensor_split: Optional[List[float]] = None
+    flash_attn: Optional[bool] = None
+    alias: Optional[str] = None
     server_args: Dict[str, Any] = Field(default_factory=dict)
 
 

src/rolemesh_node_agent/main.py

@@ -175,7 +175,7 @@ def create_app(cfg: NodeAgentConfig) -> FastAPI:
                     device,
                     model_path=str(model_entry.path),
                     model_id=model_entry.model_id,
-                    server_args=model_entry.server_args,
+                    server_args=app.state.cuda.build_server_args(model_entry),
                 )
             except ServerStartupError as e:
                 return _error(str(e), code="server_startup_error", status_code=503)

tests/test_node_agent.py

@@ -6,6 +6,7 @@ from pathlib import Path
 import httpx
 
 from rolemesh_node_agent.adapters.base import DeviceMetrics, DeviceRef
+from rolemesh_node_agent.adapters.cuda import CudaAdapter
 from rolemesh_node_agent.config import ModelEntry, NodeAgentConfig
 from rolemesh_node_agent.main import _merge_scheduler_metrics, _select_device
 from rolemesh_node_agent.scheduler import AdmissionError, DeviceQueue
@@ -266,6 +267,42 @@ def test_chat_completions_uses_selected_device_not_first_device(tmp_path):
     asyncio.run(app.state.http.aclose())
 
 
+def test_cuda_adapter_build_server_args_from_structured_fields(tmp_path):
+    model_path = tmp_path / "model.gguf"
+    model_path.write_bytes(b"GGUF")
+    adapter = CudaAdapter()
+    model = ModelEntry(
+        model_id="planner-gguf",
+        path=model_path,
+        roles=["planner"],
+        ctx_size=4096,
+        batch_size=1024,
+        ubatch_size=256,
+        threads=8,
+        threads_batch=4,
+        gpu_layers=999,
+        main_gpu=1,
+        tensor_split=[3, 1],
+        flash_attn=True,
+        alias="planner",
+        server_args={"parallel": 2},
+    )
+
+    args = adapter.build_server_args(model)
+
+    assert args["ctx-size"] == 4096
+    assert args["batch-size"] == 1024
+    assert args["ubatch-size"] == 256
+    assert args["threads"] == 8
+    assert args["threads-batch"] == 4
+    assert args["gpu-layers"] == 999
+    assert args["main-gpu"] == 1
+    assert args["tensor-split"] == "3,1"
+    assert args["flash-attn"] == "on"
+    assert args["alias"] == "planner"
+    assert args["parallel"] == 2
+
+
 def test_merge_scheduler_metrics_overlays_queue_state():
     device = DeviceRef(kind="gpu", backend="cuda", id="gpu:0")
     metric = DeviceMetrics(device=device)