Can I Run DeepSeek-R1-Distill-Llama-8B on 8 GB system RAM?

作者： Jakub Rusinowski · 最后更新： 2025年1月20日

These figures are for DeepSeek-R1-Distill-Llama-8B, a distill of Llama-3.1-8B — not the full DeepSeek R1. The full DeepSeek R1 (671B) needs about 405 GB of weights at Q4_K_M and is a different model.

8 GB system RAM — what it gives a model

DeepSeek R1 on 8 GB system RAM: memory by quantization

Which DeepSeek R1 sizes fit

What to watch out for

• Only ~1.1 GB of headroom at Q3_K_M: a longer context or a second application can push this into swapping.
• Q3_K_M is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
• 4 larger variants of DeepSeek R1 do not fit and would need CPU offload or different hardware.
• These figures assume CPU-only inference. Any discrete GPU, even an 8 GB one, will be several times faster for models that fit in its VRAM.

Recommended setup

llama.cpp (CPU build) or Ollama — both run without a GPU

How these numbers are calculated

• Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
• 6.4 GB of the 8 GB is treated as usable for model weights (80% — the rest is the OS and running applications).
• DDR5-5600 dual channel at 89.6 GB/s peak. CPU decode is assumed to sustain 35% of that peak, because CPU inference is not purely bandwidth-bound — it also spends real time in compute and thread synchronisation. This figure is an assumption, not a fitted constant: no CPU measurement is in the calibration set.
• CPU-only inference: no GPU is assumed. A GPU of any size will beat these figures substantially.
• Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
• KV cache is computed from this model's published attention configuration.

FAQ

Can I run DeepSeek R1 on 8 GB system RAM?

Yes — DeepSeek R1 Distill Llama 8B at Q3_K_M needs about 5.3 GB of the 6.4 GB usable on 8 GB system RAM (~1.1 GB spare), at ~16.4 tok/s (estimated), with room for about 16,384 tokens of context.

Which quantization of DeepSeek R1 should I use on 8 GB system RAM?

Q3_K_M — it needs about 5.3 GB of the 6.4 GB available, downloads as roughly 3.4 GB, and runs at an estimated 16.4 tokens/sec with up to 16K of context.

What limits DeepSeek R1 on 8 GB system RAM?

Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.

Which runtime should I use?

llama.cpp (CPU build) or Ollama — both run without a GPU

Other Models on 8 GB system RAM

• EXAONE 3.5 on 8 GB system RAM
• Falcon 3 on 8 GB system RAM
• Gemma 3 on 8 GB system RAM
• Gemma 3n on 8 GB system RAM
• Gemma 4 on 8 GB system RAM

DeepSeek R1 on GPUs

• DeepSeek R1 on NVIDIA GeForce RTX 5090
• DeepSeek R1 on NVIDIA GeForce RTX 5080
• DeepSeek R1 on NVIDIA GeForce RTX 5070 Ti
• DeepSeek R1 on NVIDIA GeForce RTX 5070

What This Model Is Good At

• Best local LLMs for general assistant
• Best local LLMs for reasoning
• Best local LLMs for writing

Model & Tools

• DeepSeek R1 model page
• VRAM calculator
• Check your own hardware

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