Can the RTX 3060 12GB Run Llama 3.1 8B?
Runs
Yes. Llama 3.1 8B at Q4_K_M is a 4.9 GB download (Hugging Face file listing for bartowski/Meta-Llama-3.1-8B-Instruct-GGUF), and the RTX 3060 12GB has 12 GB of VRAM, leaving about 7.1 GB for the KV cache and runtime. Published runs put generation at a median of 52.2 tokens per second across 5 cited measurements. Fastest cited run: tyolab.com.
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Published runs for Llama 3.1 8B on the RTX 3060 12GB
| Generation | Quant | Runtime | Context | Source | Date |
|---|---|---|---|---|---|
| 64.5 tok/s | q4_K_M | ollama | — | tyolab.comlabelled "llama3.1:8b" | 2026-05-11 |
| 55 tok/s | q4_K_M | llama.cpp | — | GitHub Gist (the-crypt-keeper)labelled "llama3.1:8b" | date not given |
| 52.2 tok/s | q4_K_M | llama.cpp | — | LocalScore (Mozilla Builders)labelled "llama3.1:8b" | date not given |
| 51.3 tok/s | q4_K_M | llamafile (LocalScore) | — | LocalScore (Mozilla Builders)labelled "llama3.1:8b" | date not given |
| 42 tok/s | q4_K_M | llama.cpp | — | ModelFit.iolabelled "Llama 3.1 8B Instruct" | 2025-01-01 |
More about this pair
How much VRAM does Llama 3.1 8B need?
The Q4_K_M file is 4.9 GB. Add roughly 1.5 GB for the runtime and an ~8K-token context, so about 6.4 GB of VRAM holds it comfortably. Longer contexts grow the KV cache and need more.
What speed does Llama 3.1 8B reach on the RTX 3060 12GB?
The fastest published run is 64.5 tokens per second (tyolab.com, q4_K_M, ollama); the median of 5 cited runs is 52.2 tok/s. Generation reads every weight once per token, so the RTX 3060 12GB's 360 GB/s of memory bandwidth divided by the 4.92 GB file puts the theoretical ceiling near 73 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
Llama 3.1 8B on other GPUs
- RTX 4060 — Runs, 40 tok/s median
- RTX 3060 Ti — Runs, 57.3 tok/s (1 run)
- Intel Arc B580 — Runs, 40 tok/s median
- RTX 4070 SUPER — Runs, 53.4 tok/s median
- RTX 4060 Ti 16GB — Runs, 49.2 tok/s median
- RTX 5060 Ti 16GB — Runs, 65.1 tok/s median
- Intel Arc A770 16GB — Runs, 38.7 tok/s (1 run)
- Radeon RX 9070 XT — Runs
- RTX 4070 Ti SUPER — Runs, 53.9 tok/s (1 run)
- RTX 4080 — Runs, 101 tok/s median
- RTX 5070 Ti — Runs, 95.3 tok/s median
- RTX 5080 — Runs, 88.5 tok/s median
- Tesla P40 24GB — Runs, 50.6 tok/s median
- RTX 3090 — Runs, 92 tok/s median
- Radeon RX 7900 XTX — Runs, 48.3 tok/s median
- RTX 4090 — Runs, 126 tok/s median
- RTX 5090 — Runs, 180 tok/s median
- RTX A6000 48GB — Runs
- RTX PRO 5000 Blackwell 48GB — Runs, 150 tok/s median
- Instinct MI210 64GB — Runs
- RTX PRO 6000 Blackwell 96GB — Runs, 138 tok/s (1 run)
Other models on the RTX 3060 12GB
- Llama 3.2 1B — Runs, 185 tok/s median
- Llama 3.2 3B — Runs, 126 tok/s median
- Mistral 7B — Runs
- Qwen2.5 7B — Runs
- DeepSeek-R1-Distill-Llama 8B — Runs
- Qwen3 8B — Runs, 48.6 tok/s median
- Gemma 2 9B — Runs
- Gemma 3 12B — Runs
- Qwen2.5 14B — Runs, 26.5 tok/s median
- DeepSeek-R1-Distill-Qwen 14B — Runs, 29.4 tok/s (1 run)
- Qwen3 14B — Runs, 31.2 tok/s median
- Phi-4 14B — Runs
- gpt-oss-20b — Offload
- Gemma 3 27B — Offload
- Gemma 2 27B — Offload
- Qwen3 30B-A3B — Offload
- Qwen3 32B — Offload
- Qwen2.5 32B — Offload
- DeepSeek-R1-Distill-Qwen 32B — Offload
- Qwen3 32B (Q6_K) — Offload
- Qwen2.5 32B (Q6_K) — Offload
- DeepSeek-R1-Distill-Qwen 32B (Q6_K) — Offload
- Gemma 3 27B (Q8_0) — Offload
- Llama 3.1 70B — Offload
- Llama 3.3 70B — Offload
- DeepSeek-R1-Distill-Llama 70B — Offload
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.