What quantization should I use for DeepSeek-R1 on a NVIDIA GeForce RTX 4060?

Q4_K_M is the highest-quality quant that grades B or better on this hardware with llama.cpp at 8K context; the per-quant table on this page grades all 4 available quants.

Can an NVIDIA GeForce RTX 4060 run DeepSeek-R1?

No. DeepSeek-R1 needs 405.6GB at Q4_K_M with llama.cpp and 8K context; the NVIDIA GeForce RTX 4060 has 8GB of VRAM (7.50GB usable) — it does not fit, even with CPU offload.

NVIDIA GeForce RTX 4060 · DeepSeek-R1 · Q4_K_M

❌ Won't fit

betaMLA memory formula — beta, not yet calibrated

weights 404.4 GBKV cache 0.58 GBoverhead 0.58 GBpool 7.50 GBneeds 405.6 GB — over the pool

assumes llama.cpp · batch 1 · 8K context · fp16 KV cache

No quant of this model fits cleanly on this hardware with llama-cpp at this context — a smaller model or more memory is the real fix.

Rent a GPU that fits

This needs about 405.6 GB and NVIDIA GeForce RTX 4060 pools 7.50 GB. You can rent a GPU with enough VRAM by the hour instead of buying one.

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$299 MSRP · as of 2026-07-08

Also good for gaming — 1080p-class (estimated from FP32 compute, not a game benchmark).

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Adjust assumptions — runtime, context, GPU count →

Every quant, graded

NVIDIA GeForce RTX 4060 × DeepSeek-R1, llama.cpp, 8K context.

Quant	File size	Fit
Q8_0	713.3 GB	Won't fit
Q6_K	550.8 GB	Won't fit
Q5_K_M	475.4 GB	Won't fit
Q4_K_M	404.4 GB	Won't fit

DeepSeek-R1 on similar hardware

More models on the NVIDIA GeForce RTX 4060

Verdict computed from published specs and measured GGUF file sizes — see how the math works.