DeepSeek-R1-Distill-Llama-8B

Running DeepSeek-R1-Distill-Llama-8B locally

At Q4_K_M and 4K of context, DeepSeek-R1-Distill-Llama-8B needs about 5.6 GB — 4.6 GB of weights, 0.50 GB of KV cache and a 0.5 GB activation buffer. The smallest card in this index that clears that comfortably is the RTX 5060 Ti 8G at 8 GB, and 61 of the 61 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.

What longer context costs

Going from 4K to 32K adds about 3.50 GB, taking the total to 9.5 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 128K; holding all of it at Q4_K_M would need about 23 GB.

Which build to download

This index tracks 3 formats for it — GGUF, EXL2, AWQ — across 4 levels. Q5_K_M carries the lowest published perplexity loss at 1.2%. The fastest level measured here is EXL2 4.65bpw at 210 tok/s on an RTX 4090, batch 1. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 and CUDA only.

Common questions

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

About 5.6 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 9.5 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.

Will DeepSeek-R1-Distill-Llama-8B run on a 8GB GPU?

Yes — at Q4_K_M and 4K context it needs about 5.6 GB, which leaves 2.4 GB spare on a RTX 5060 Ti 8G. That is the smallest card in this index that clears it comfortably; 61 of 61 do.

Which quantization of DeepSeek-R1-Distill-Llama-8B should I use?

Q5_K_M has the lowest published quality loss (1.2%), and Q4_K_M is the level most people run. All 4 levels in the index are Q4_K_M, Q5_K_M, EXL2 4.65bpw, AWQ INT4.