DeepSeek R1 Distills: Reasoning on Local Hardware

DeepSeek R1 distills is a useful local-model lesson because it makes one trade-off visible: math puzzles, reasoning demos, comparing small and mid-size local reasoning models, and teaching token-budget trade-offs. The point is not to crown a permanent winner. The point is to learn how to match a model family to hardware, task, license, and risk.

Current source signal

Build the small version

Run one reasoning prompt on a small distill and a larger distill. Record answer correctness, reasoning length, latency, and memory use.

1. 1Pick one exact model file or runtime tag from the current model card.
2. 2Run three short prompts: one easy, one task-specific, and one likely failure case.
3. 3Record load time, response speed, memory pressure, answer quality, and one surprising failure.
4. 4Write a one-paragraph recommendation: use it, do not use it, or use it only for a narrow job.

reasoning_eval: prompt: multi_step_problem models: - small_distill - larger_distill score: final_answer: correct_or_wrong reasoning: useful_or_noisy latency_seconds: number tokens_generated: number

The big idea: remember reasoning ladder. Local model work is product design under constraints, not just downloading the model with the loudest leaderboard score.