DeepSeek R1 Distill Llama 8B — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated January 20, 2025

Model library → DeepSeek R1 → DeepSeek R1 Distill Llama 8B

A highly efficient distilled version based on Llama 3. Incredible reasoning performance for its size.

DeepSeek R1 Distill Llama 8B needs about 6 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters8 Billion
Context window128,000
ArchitectureDense
ProviderDeepSeek
LicenceMIT
Specified atQ4_K_M
System RAM16 GB
Record updated2025-01-20

Licence

MIT — commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), at 8K context. Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K2.6 GB4.5 GB~155 tok/s (est.)Fits comfortably
Q3_K_M3.4 GB5.3 GB~134 tok/s (est.)Fits comfortably
Q4_K_M4.8 GB6.7 GB~107 tok/s (est.)Fits comfortably
Q5_K_M5.7 GB7.5 GB~96 tok/s (est.)Fits comfortably
Q6_K6.6 GB8.4 GB~86 tok/s (est.)Fits comfortably
Q8_08.5 GB10.4 GB~70 tok/s (est.)Fits comfortably
F1616.0 GB17.9 GB~41 tok/s (est.)Fits comfortably

Want the memory numbers alone, at every quantization level and your own context length? Use the DeepSeek R1 Distill Llama 8B VRAM calculator.

Buy This HardwareIntel Arc B570 10GB — 10 GB VRAM · 150 W board powerDeploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Recommended GPU

The cheapest catalogued GPU that runs DeepSeek R1 Distill Llama 8B is the Intel Arc B570 (10 GB).

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Intel Arc B570 10GB
10 GB VRAM · 150 W board power
2026 prices are volatile — check the current listing.
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How to Run DeepSeek R1 Distill Llama 8B

Install Ollama, then run:

ollama run deepseek-r1:8b

Weights on Hugging Face: deepseek-ai/DeepSeek-R1-Distill-Llama-8B.

Best for: reasoning, chat, logic.

Can I Run DeepSeek R1 Distill Llama 8B on My GPU?

Other DeepSeek R1 Sizes

DeepSeek R1 Distill Llama 8B — Frequently Asked Questions

How much VRAM does DeepSeek R1 Distill Llama 8B need?
About 6 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does DeepSeek R1 Distill Llama 8B run on an RTX 4090 (24 GB)?
Yes. DeepSeek R1 Distill Llama 8B needs about 6 GB at Q4_K_M, inside a 24 GB card, at an estimated 107 tokens/sec.
How do I run DeepSeek R1 Distill Llama 8B locally?
Install Ollama and run `ollama run deepseek-r1:8b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does DeepSeek R1 come in?
DeepSeek R1 Distill Llama 8B (6 GB), DeepSeek R1 Distill Qwen 32B (20 GB), DeepSeek R1 Distill Qwen 14B (9 GB), DeepSeek R1 (671B) (406 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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