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Text model · DeepSeek-R1-Distill

DeepSeek-R1-Distill-Llama 8B: RAM and VRAM requirements

DeepSeek-R1-Distill-Llama 8B needs about 6.4 GB to run at Q4_K_M (the Q4_K_M GGUF file is ~4.92 GB to download; KV cache and overhead add the rest), or about 10 GB at Q8_0. The lightest hardware that runs it is Nvidia GeForce RTX 3060 (12GB).

DeepSeek-R1-Distill family · 8B params · released Jan 2025 · 79.3M Ollama pulls.

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License MIT · Commercial OK ↓ 401.9K/mo ♥ 872 on HuggingFace
Q4_K_M GGUF
4.92 GB
Q8_0 GGUF
8.54 GB
Memory @ Q4 (4k)
~6.4 GB
Context
128 k

Q4_K_M and Q8_0 are GGUF download (file) sizes. Memory @ Q4 includes the KV cache and overhead, so it runs larger than the file on disk.

Will it run on your device?

DeepSeek-R1-Distill-Llama 8B runs on 33 of 40 tracked devices at Q4_K_M.

33 run well 0 tight fit 7 too small
Fit check Q4_K_M
Yes, it runs fast
needs 6.4 GB usable 10.5 GB
See the full breakdown
$ollama run deepseek-r1:8b

Memory breakdown

Weights (Q4_K_M)4.92 GB
+
KV cache (4k)0.7 GB
+
Overhead0.8 GB
=
Total6.4 GB

How context length changes it

4k context ~6.4 GB 32k context ~11.1 GB 128k context ~27.4 GB

Longer context grows the KV cache quickly: DeepSeek-R1-Distill-Llama 8B needs ~6.4 GB at 4k but ~27.4 GB at 128k, which can push it past a device that fits it at a short context.

Speed drops too: every token re-reads the KV cache, so at 128k context DeepSeek-R1-Distill-Llama 8B generates at roughly ~18% of its short-context speed. How this is estimated.

Quantization sizes

GGUF quantson disk
Quantization Size on disk
Q2_K 3.4 GB est
Q3_K_M 3.9 GB est
Q4_K_M (default) 4.92 GB
Q5_K_M 5.7 GB est
Q6_K 6.6 GB est
Q8_0 8.54 GB
FP16 16 GB est

Lower quant = smaller and faster, slightly lower quality. Q4_K_M is the common default.

Run it

Ollama
$ ollama run deepseek-r1:8b
llama.cpp
$ llama-cli -hf bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M
LM Studio
$ lms get bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF

Which devices can run DeepSeek-R1-Distill-Llama 8B?

Apple Silicon Macs

RAM-only laptops

iPhone & iPad

Android

NVIDIA GPUs

AMD GPUs

Same job, different size

Pick by what fits your memory: step down to free up VRAM, or step up if you have headroom.

Next size down Qwen2.5 Coder 7B 7B Next size up DeepSeek-R1-0528-Qwen3-8B 8.19B

Similar models

Head-to-head

FAQ

How much VRAM or RAM does DeepSeek-R1-Distill-Llama 8B need?

At Q4_K_M, DeepSeek-R1-Distill-Llama 8B needs about 6.4 GB (weights ~4.92 GB + KV cache + overhead) at a 4k context. At Q8_0 budget ~10 GB.

What is the Q4_K_M GGUF file size of DeepSeek-R1-Distill-Llama 8B?

The Q4_K_M GGUF file is about 4.92 GB to download, and the Q8_0 GGUF is about 8.54 GB. That is the weights file on disk; to run it you also need room for the KV cache and overhead, so budget ~6.4 GB of memory at Q4_K_M.

Can DeepSeek-R1-Distill-Llama 8B run on a laptop?

Yes, DeepSeek-R1-Distill-Llama 8B fits on a 16 GB machine at Q4_K_M and runs on Apple Silicon or a 12 GB+ GPU comfortably.

Can I use DeepSeek-R1-Distill-Llama 8B commercially?

Yes. DeepSeek-R1-Distill-Llama 8B is licensed MIT, which permits commercial use.

Understand the numbers

Short guides to the ideas behind DeepSeek-R1-Distill-Llama 8B's memory and quant figures.

Distilled from DeepSeek-R1 onto Llama-3.1-8B base. Released 2025-01-20. Q4_K_M=4.92GB, Q8_0=8.54GB from bartowski HF repo. Ollama shows 5.2GB for the 8b tag. Context 128K.

Sources

Last validated 2026-08-03. Memory figures are estimates. See methodology.