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Inference & agents

Connect quality, latency and cost.

Serving requires an explicit service envelope. Agentic applications add tool calls, variable context and multiple model passes; a single device throughput claim cannot describe the entire workflow.

Measure both phases

Distinguish prompt processing from output generation. Record time to first token, output-token timing, concurrency and prompt/output lengths. Cache behaviour and batching should remain visible in any comparison.

Count accepted outcomes

Define what constitutes an acceptable answer or completed task. Rejected outputs and retries still consume resources. A cost-per-token figure is meaningful only with a stated workload, quality threshold and cost boundary.

Plan the full path

Include model loading, data access, CPU work, tool dependencies and operational recovery. Evaluate a representative traffic distribution before sizing production. Our inference tool is arithmetic using supplied measurements; it is not a serving simulator.

Prepare a comparable test

Keep the workload, software version, data path, service objective and cost boundary attached to the result. Changing these assumptions changes what a comparison means.

Official references: M1, C2. Checked 8 November 2026. Company descriptions remain attributed; announced plans retain their conditions.

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