Can RTX 4060 Run DeepSeek?

No, the RTX 4060 cannot run DeepSeek at a useful quality level. With only 8GB GDDR6 VRAM, the RTX 4060 is limited to the 1.5B distilled variant or an aggressively quantised 7B model with short context. For proper DeepSeek hosting that preserves the model’s reasoning capabilities, you need substantially more VRAM than this card offers.

The Short Answer

NO for practical DeepSeek use. Marginal YES for the 1.5B distilled model only.

The RTX 4060 has 8GB GDDR6 VRAM, which is an improvement over the 3050’s 6GB but still far short of what DeepSeek’s larger models require. The 7B distilled variant needs about 4.5GB in INT4 quantisation for weights alone, leaving around 3.5GB for KV cache and overhead. That gives you a context window of roughly 3072 tokens, which is usable but restrictive for the extended reasoning chains DeepSeek R1 is designed to produce.

The full DeepSeek R1 671B model or even the 32B distilled variant are completely out of the question. This card is not designed for large language model workloads.

VRAM Analysis

The 1.5B distilled model is the only variant that runs comfortably in FP16 on the RTX 4060, but it sacrifices significant reasoning quality compared to the larger variants. The 7B model in INT4 is the maximum you can squeeze in, and even then context length is limited. Review our DeepSeek VRAM requirements guide for detailed breakdowns.

Performance Benchmarks

The RTX 4060 delivers 38 tokens per second on the 1.5B model, which is fast but the model itself is too small for complex reasoning tasks. On the 7B variant at INT4, 15 tok/s is usable for interactive chat but the short context window undermines multi-step reasoning. See our benchmarks page for more comparisons.

Setup Guide

To run DeepSeek R1 7B distilled on the RTX 4060 with Ollama:

# Run the 7B distilled variant in Q4_K_M
ollama run deepseek-r1:7b-q4_K_M

To constrain context and avoid OOM errors:

# Custom Modelfile with strict VRAM management
cat <<EOF > Modelfile
FROM deepseek-r1:7b-q4_K_M
PARAMETER num_ctx 3072
PARAMETER num_gpu 99
EOF
ollama create deepseek-4060 -f Modelfile
ollama run deepseek-4060

For the 1.5B model which runs comfortably:

ollama run deepseek-r1:1.5b

Watch VRAM usage with nvidia-smi -l 1 during generation. The Ada Lovelace architecture in the RTX 4060 handles INT4 inference well, but the 8GB VRAM remains the hard limit.

Recommended Alternative

For DeepSeek workloads that matter, the RTX 3090 with 24GB VRAM is the sensible step up. It runs the 7B distilled model in full FP16 with 32K context at 35+ tok/s, and can handle the 14B variant in INT4 quantisation. The RTX 4060 Ti with 16GB is a middle ground that runs the 7B comfortably in INT8 with proper context length.

If you are comparing across the 4060 range, see whether the RTX 4060 Ti can run DeepSeek for a better option at a similar price tier. For image generation on this card, the RTX 4060 with Flux.1 is a more suitable workload. Our best GPU for LLM inference guide covers the full range of options for language models, and dedicated GPU servers give you the flexibility to choose the right hardware.