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Can the RTX 3060 12GB Run Qwen3 8B?

Runs

Yes. Qwen3 8B at Q4_K_M is a 5 GB download (Hugging Face file listing for unsloth/Qwen3-8B-GGUF), and the RTX 3060 12GB has 12 GB of VRAM, leaving about 7 GB for the KV cache and runtime. Published runs put generation at a median of 48.6 tokens per second across 4 cited measurements. Fastest cited run: tyolab.com.

Model file5 GBQ4_K_M
VRAM12 GBRTX 3060 12GB
Headroom7 GBafter weights
Median speed48.6 tok/s4 cited runs
Bandwidth ceiling~72 tok/s360 GB/s ÷ 5.03 GB

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Published runs for Qwen3 8B on the RTX 3060 12GB

GenerationQuantRuntimeContextSourceDate
59.5 tok/s q4_K_M llama.cpp — tyolab.comlabelled "qwen3:8b" 2026-05-11
55.2 tok/s q4_K_XL llama.cpp 4K Hardware Cornerlabelled "qwen3:8b" date not given
42 tok/s q4_K_M llama.cpp 16K Ajit Singh / Hardware-Cornerlabelled "qwen3:8b" 2026-04-07
31.9 tok/s q4_K_XL llama.cpp 32K singhajit.com (Hardware Corner)labelled "qwen3:8b" 2025-06-01

More about this pair

How much VRAM does Qwen3 8B need?

The Q4_K_M file is 5 GB. Add roughly 1.5 GB for the runtime and an ~8K-token context, so about 6.5 GB of VRAM holds it comfortably. Longer contexts grow the KV cache and need more.

What speed does Qwen3 8B reach on the RTX 3060 12GB?

The fastest published run is 59.5 tokens per second (tyolab.com, q4_K_M, llama.cpp); the median of 4 cited runs is 48.6 tok/s. Generation reads every weight once per token, so the RTX 3060 12GB's 360 GB/s of memory bandwidth divided by the 5.03 GB file puts the theoretical ceiling near 72 tokens per second. That is an upper bound, not a speed estimate: kernel, launch and compute overhead keep real runs below it, and SpecPicks rejects any published figure above it.

Check another pair

See the full matrix → · All RTX 3060 12GB benchmarks →

Qwen3 8B on other GPUs

Other models on the RTX 3060 12GB

How the checks work

The fit is the Q4_K_M file size against the card's VRAM, with 1.5 GB kept for the runtime and an ~8K context. A published run counts only if the pair fits, the run is Q4-class, its source does not call it an estimate, it is under the bandwidth ceiling (memory bandwidth ÷ GB read per token: the whole file for a dense model, only the active experts' weights for a mixture-of-experts model such as gpt-oss-20b or Qwen3 30B-A3B, which is why those can post figures above bandwidth ÷ file size), it is not slower than the same card on a model twice its size, a single run is not slower than the same card on a larger dense model, and it is not one figure pasted across many cards. The whole matrix is downloadable as a CSV under CC BY 4.0.

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This page is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.