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title: 'Filesystem Model Deployment Guide' sidebar_label: 'Deployment Guide' description: 'Operating guide for filesystem-loaded models in production: formats, device selection, memory footprint, and observability.' sidebar_position: 10 pagination_prev: null pagination_next: null tags:

• models
• observability

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Production operating guide for loading local language models from the filesystem (GGUF, safetensors, ONNX).

Authentication & Secrets

The Filesystem model provider has no authentication layer. Access control is enforced by the operating system:

• The Spice runtime process must have read permission on the model files.
• For containers, mount model files as read-only volumes.
• For Kubernetes, mount via PersistentVolumeClaim or a model-serving sidecar.

For sensitive models (proprietary weights, fine-tunes with PII training data), restrict filesystem ACLs to the Spice process user and encrypt the volume at rest.

Resilience Controls

The Filesystem model provider reads local files synchronously. There is no network layer, retry logic, or remote backoff. Failures surface as filesystem errors (ENOENT, EACCES, EIO).

Model loading happens once at startup. A missing or unreadable file fails the spicepod load; fix the underlying cause and restart.

Capacity & Sizing

Supported Formats

Format	Extension	Notes
GGUF	.gguf	Quantized / unquantized; loaded via the mistral local loader.
GGML (legacy)	.ggml	Legacy llama.cpp format.
Safetensors	.safetensors	Native tensor format; preferred over .bin for safety.
PyTorch	.bin / .pt / .pth	Legacy PyTorch checkpoints.
ONNX	.onnx	Supported via the tract runtime for classical ML models.

Device Selection

Local inference uses the first available backend in order:

1. CUDA (if compiled with CUDA support and a device is present)
2. Metal (if compiled with Metal support — macOS / Apple Silicon)
3. CPU fallback

Install the CUDA-enabled Spice build on GPU hosts for materially better throughput on models over a few billion parameters.

Memory Footprint

Model file size on disk is close to RAM / VRAM footprint at load. Quantized GGUF models (Q4, Q5, Q8) reduce footprint roughly proportional to their bit-width. For a 7B-parameter model:

• f16: ~14 GB
• Q8: ~7.5 GB
• Q5: ~5 GB
• Q4: ~4 GB

Add ~20–30% headroom for KV cache and working memory during inference.

Concurrency

The runtime rate limiter defaults to max_concurrency=1 for local models — local inference is compute-bound and benefits little from request-level parallelism on a single accelerator. Override via max_concurrency for multi-GPU / large-core CPU hosts.

Metrics

Shared LLM metrics apply (see the OpenAI Model Deployment Guide for the full metric list): llm_requests, llm_failures, llm_internal_request_duration_ms, llm_prompt_tokens_total, llm_completion_tokens_total.

See Component Metrics for enabling and exporting metrics.

Task History

Local inference operations emit ai_completion spans (and health spans for probes) in task history, mirroring the shared model spans. captured_output and token usage fields are logged.

Known Limitations

• Single-process loading: A model is loaded into the Spice process — it cannot be shared across process instances without a dedicated inference server.
• Format support depends on compile features: CUDA, Metal, and ONNX support are conditional on the Spice build flavor (default, CUDA, ONNX-enabled).
• No hot reload: Swapping the underlying model file requires a spicepod reload.
• No integrity check: The provider trusts the file on disk. Validate checksums out-of-band for supply-chain assurance.
• Model_type override: When the loader cannot auto-detect the architecture, model_type can force a known architecture.

Troubleshooting

Symptom	Likely cause	Resolution
No such file or directory	Path typo or missing mount.	Verify the file exists in the Spice process's filesystem (ls inside the container).
Permission denied	Spice user lacks read on the file.	Adjust ACLs or mount with appropriate UID/GID.
Model fails to load with unsupported architecture	Loader cannot infer architecture from filename.	Set model_type explicitly.
OOM on load	File size exceeds device memory.	Use a smaller / more quantized variant; move to CPU with more RAM; split across GPUs if supported.
Inference falls back to CPU unexpectedly	CUDA / Metal not available.	Use a CUDA-enabled Spice build on GPU hosts; for macOS, use the Apple Silicon build.
Slow first inference after startup	JIT / weight-quantization warmup.	Issue a warmup request at startup; subsequent calls are hot.