DeepSeek-R1 Distill Llama 8B · 11 GPUs measured first-party · llama.cpp Q4_K_M · Updated July 2026

What GPU Do You Need for DeepSeek-R1 Distill Llama 8B?

DeepSeek-R1 Distill Llama 8B is the odd one out in the distill family, same R1 reasoning training, but poured into a Llama 3.1 base instead of Qwen. Measured on 11 GPUs (llama.cpp, Q4_K_M): 284 tok/s on the B300, ~6GB peak VRAM, statistically inseparable from its Qwen-based 7B sibling on speed.

Benchmarked weights: bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF

Why the base model matters more than the benchmark. On our charts this model and Distill-Qwen 7B are twins, 284 vs 285 tok/s at the top, identical floors. The difference is underneath: a Llama base brings different training data, different instincts, different failure modes. That's not a footnote, it's the feature. If you're building the multi-model consensus setups we keep advocating, an ensemble of Qwen-based models shares blind spots; swapping this Llama-based distill into the rotation buys you genuine diversity for free.

Practical notes from the runs. ~6GB peak means 8GB cards fit it with context room. The H200's 265 tok/s at 131W (2.03 tok/W) is again the efficiency pick, the Blackwell cards buy their extra 7% with more than double the power. On modest hardware it stays honest: 89 tok/s on an A10G-class card is quick enough that even long reasoning traces resolve in seconds.

Our verdict

DeepSeek-R1 Distill Llama 8B: 284 tok/s peak, ~6GB floor, speed-identical to the Qwen-based distill, behaviorally distinct because of the Llama base. That distinctness is the reason to run it: it's the diversity member of a reasoning ensemble.

FAQ

What's different about the Llama-based distill?

The reasoning training is the same R1 recipe; the base model isn't. A Llama 3.1 foundation means different pretraining and different failure modes than the Qwen-based distills. Which is exactly what you want when models cross-check each other.

What GPU does it need?

8GB and up, ~6GB measured peak at Q4_K_M. Same class as the 7B distill and the 8B Dolphins: this whole tier fits mainstream cards.

Should I run this or Distill-Qwen 7B?

For a single model, either, they measured within 1 tok/s of each other. For an ensemble, both: one Qwen-based, one Llama-based, and let the disagreements point you at the hard cases.

How fast is it on affordable hardware?

50 tok/s on an L4, 36 on a T4, usable single-user speeds. On anything modern the reasoning traces stop being a wait: the H200 measured 265 tok/s at just 131W.

Does it work as a coding model?

It reasons about code well for its size, but coder-tuned models (Qwen2.5-Coder, Codestral) beat it at completion tasks. Use it where step-by-step deliberation matters more than raw code fluency.