DeepSeek R1 Distill Qwen 14B

Overview

DeepSeek's R1 reasoning distilled into Qwen 14B under MIT. AIME24 69.7 and MATH-500 93.9 — beats o1-mini on most reasoning benchmarks.

When to pick this model

• Math, coding, and STEM reasoning on a single 24GB GPU
• Local alternative to o1-mini-class APIs
• Workloads needing MIT-licensed reasoning
• Agentic planners that benefit from explicit CoT

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	9 GB
Q5_K_M	11 GB
Q8_0	16 GB
FP16 (no quantization)	28 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

In practice, DeepSeek R1 Distill Qwen 14B needs a 12 GB card at Q4_K_M (9 GB). Stepping up to Q8_0 nearly doubles the footprint to 16 GB, and unquantized FP16 weights take 28 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, DeepSeek R1 Distill Qwen 14B needs roughly 16 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 6 tokens/sec on entry-level GPUs, on the order of 20 tokens/sec on a mid-range card, and up to 55 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches DeepSeek R1 Distill Qwen 14B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

GPU memory	Example cards	Best fit for DeepSeek R1 Distill Qwen 14B
8 GB	RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB	Does not fit — needs 9 GB at Q4_K_M
12 GB	RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop	Q5_K_M (11 GB used)
16 GB	RTX 5080, RTX 4080 Super, Radeon RX 9070 XT	Q8_0 (16 GB used)
24 GB	RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop	Q8_0 (16 GB used)
32 GB	RTX 5090	FP16 (28 GB used)

Which GPU should you buy to run DeepSeek R1 Distill Qwen 14B?

To run DeepSeek R1 Distill Qwen 14B locally at Q4, you need ~9 GB of VRAM. The best value for this is a RTX 5070 (12 GB VRAM).

Published benchmark scores

Benchmark	Score
AIME 2024	69.7
MATH-500	93.9
GPQA	59.1

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

To put DeepSeek R1 Distill Qwen 14B in context: its AIME 2024 score of 69.7 ranks #6 of the 8 catalog models with a published AIME 2024 result (catalog median 71.7); its MATH-500 score of 93.9 ranks #4 of the 8 catalog models with a published MATH-500 result (catalog median 93.3). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.

Strengths

• AIME24 69.7 and MATH-500 93.9
• Beats o1-mini on multiple reasoning benchmarks
• MIT license — no usage restrictions
• 131k context

Limitations

• Verbose CoT inflates token costs
• Slower than non-reasoning 14B for simple queries
• No vision or tool-use specialization

Typical workloads

In our catalog grid, DeepSeek R1 Distill Qwen 14B is filed under Workstation Reasoning, Math, Science — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks.

The 128k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The MIT license is permissive, so shipping it inside a commercial product raises no special legal questions.

Architecture & training

Architecture: Dense Qwen 2.5 14B · SFT on R1 traces

Training: Distillation of R1 671B.

Quick start

ollama run deepseek-r1:14b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

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Frequently asked questions

How much VRAM does DeepSeek R1 Distill Qwen 14B need?

At the recommended Q4_K_M quantization, DeepSeek R1 Distill Qwen 14B needs about 9 GB of VRAM. Q8_0 takes 16 GB, and unquantized FP16 weights take 28 GB.

Can DeepSeek R1 Distill Qwen 14B run without a GPU?

Yes — with roughly 16 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does DeepSeek R1 Distill Qwen 14B support?

DeepSeek R1 Distill Qwen 14B supports a 128k-token context window (131,072 tokens).

Can I use DeepSeek R1 Distill Qwen 14B commercially?

Yes. DeepSeek R1 Distill Qwen 14B is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.

How fast is DeepSeek R1 Distill Qwen 14B on consumer hardware?

Our compatibility engine estimates on the order of 20 tokens/sec on a mid-range GPU and up to 55 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of DeepSeek R1 Distill Qwen 14B should I download first?

Start with Q4_K_M (9 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q8_0.