DeepSeek-R1-Distill-Qwen-14B
14BDeepSeek
R1 reasoning distilled into 14B. Huge community interest; excellent chain-of-thought.
131K
Max Context
3
Quant Variants
EXL2 4.65bpw
Best Quality
98.0%
Accuracy Retained
Quantization Variants
Per-quant VRAM, quality loss, and inference speed on RTX 4090
Measured = site benchmarks · Estimated = formula · Community = public reports
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Running DeepSeek-R1-Distill-Qwen-14B locally
At Q4_K_M and 4K of context, DeepSeek-R1-Distill-Qwen-14B needs about 10.1 GB — 8.5 GB of weights, 0.75 GB of KV cache and a 0.9 GB activation buffer. The smallest card in this index that clears that comfortably is the RTX 5070 at 12 GB, and 55 of the 63 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.
What longer context costs
Going from 4K to 32K adds about 5.25 GB, taking the total to 15.9 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 128K; holding all of it at Q4_K_M would need about 36 GB.
Which build to download
This index tracks 3 formats for it — GGUF, EXL2, AWQ — across 3 levels. EXL2 4.65bpw carries the lowest published perplexity loss at 2.0%. The fastest level measured here is EXL2 4.65bpw at 128 tok/s on an RTX 4090, batch 1. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 and CUDA only.
Common questions
- How much VRAM does DeepSeek-R1-Distill-Qwen-14B need?
- About 10.1 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 15.9 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
- Will DeepSeek-R1-Distill-Qwen-14B run on a 12GB GPU?
- Yes — at Q4_K_M and 4K context it needs about 10.1 GB, which leaves 1.9 GB spare on a RTX 5070. That is the smallest card in this index that clears it comfortably; 55 of 63 do.
- Which quantization of DeepSeek-R1-Distill-Qwen-14B should I use?
- EXL2 4.65bpw has the lowest published quality loss (2.0%), and Q4_K_M is the level most people run. All 3 levels in the index are Q4_K_M, EXL2 4.65bpw, AWQ INT4.
Where this model fits
Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.
- Runs comfortably on
- RTX 507012GB · +1.9RTX 4070 Ti12GB · +1.9RTX 4070 Super12GB · +1.9RTX 407012GB · +1.9RTX 3080 Ti12GB · +1.9
- Tight but possible
- RTX 3080 10G10GB
- Just misses
- RTX 3070+2.1GB overMac M3 8G+4.1GB over
- Compared here
- GGUF vs AWQGGUF vs EXL2AWQ vs EXL2
- Try it yourself
- Size it in the calculatorGenerate the run command
How to actually run this
Deployment guides for this model and this class of hardware.
This page's figures change when the model or the runtime does.Last updated 2026-09-21 RSS → /feed.xml