DeepSeek-R1 Distill Qwen 14B
DeepSeek-R1 Distill Qwen 14B loads in 11 GB at Q4_K_M, comfortably from 24 GB of memory, Macs included, starting at the Mac Mini M6 16GB. Below is the memory math we ran for it, the smallest card that fits, and the fastest machine we track.
How much memory does DeepSeek-R1 Distill Qwen 14B need?
DeepSeek-R1 Distill Qwen 14B is a 14B dense model built for reasoning, quality. The weights alone take 11 GB at Q4_K_M; add context and the OS, and it belongs in the 24 GB memory class. Every number below comes from the ModelFit engine: speeds are estimates, labeled est., because we do not benchmark hardware ourselves.
KV = fp16 estimate (q8_0 cache roughly halves it). "Comfortable" = weights + KV within the engine's tiered budget (~70-85% of memory).
Which machines run DeepSeek-R1 Distill Qwen 14B?
Verdicts come from the same engine that powers our fit checker: memory capacity first, bandwidth for speed. How we compute these numbers · Hugging Face model card
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Frequently asked questions
How much memory does DeepSeek-R1 Distill Qwen 14B need?
11 GB for the Q4_K_M weights, plus ~3.0 GB of KV-cache at 16k context, about 14.0 GB total. Comfortable from 24 GB of VRAM or unified memory.
Does DeepSeek-R1 Distill Qwen 14B run on a Mac?
Yes, from the Mac Mini M6 16GB (~15 tok/s est.). Unified memory means the RAM budget is the only limit.
What is the smallest GPU for DeepSeek-R1 Distill Qwen 14B?
The NVIDIA GeForce RTX 4060 Ti (16 GB VRAM) is the smallest tracked card that runs DeepSeek-R1 Distill Qwen 14B comfortably, ~21 tok/s est.
Which DeepSeek-R1 Distill Qwen 14B quant should I pick?
The tracked Q4_K_M build at 11 GB is the one we recommend for most machines. Every published build, ranked by real GGUF file size rather than a formula, is on the quant comparison page linked below.
Cite this page
ModelFit: DeepSeek-R1 Distill Qwen 14B, specs, memory math and hardware verdicts. https://modelfit.io/models/deepseek-r1-distill-qwen-14b/ (dataset updated 2026-10-13, CC BY 4.0).