Can the RTX 5060 Ti 16GB 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 5060 Ti 16GB has 16 GB of VRAM, leaving about 11.1 GB for the KV cache and runtime. Published runs put generation at a median of 59.2 tokens per second across 7 cited measurements. Fastest cited run: GPU Battle.

Model file4.9 GBQ4_K_M

VRAM16 GBRTX 5060 Ti 16GB

Headroom11.1 GBafter weights

Median speed59.2 tok/s7 cited runs

Bandwidth ceiling~91 tok/s448 GB/s ÷ 4.9 GB

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ASUS Dual GeForce RTX 5060 Ti 16GB GDDR7 OC Edition Gaming Graphics Card

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Published runs for Llama 3.1 8B on the RTX 5060 Ti 16GB

Generation	Quant	Runtime	Context	Source	Date
84.2 tok/s	q4_K_M	ollama	—	GPU Battlelabelled "llama3.1:8b"	2026-07-11
75.1 tok/s	q4_K_M	ollama	—	ComputingForGeekslabelled "llama3.1:8b"	2026-06-01
71 tok/s	q4_K_M	ollama	—	Runyard.devlabelled "llama3.1:8b"	2026-05-09
59.2 tok/s	q4_K_M	llama.cpp	—	LocalScore.ailabelled "llama3.1:8b"	2025-04-16
59 tok/s	q4_K_M	llama.cpp	—	LocalScore.ailabelled "llama3.1:8b"	2025-05-01
55 tok/s	q4_K_M	llama.cpp	—	InsiderLLMlabelled "llama3.1:8b"	2026-02-28
16.9 tok/s	Q4	ollama	—	LocalLLaMAlabelled "llama3.1:8b"	2026-04-20

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 is the fastest Llama 3.1 8B can generate on the RTX 5060 Ti 16GB?

Generation reads every weight once per token, so the RTX 5060 Ti 16GB's 448 GB/s of memory bandwidth divided by the 4.9 GB file sets a ceiling near 91 tokens per second. Real runs land below it; SpecPicks rejects any published figure above it.

Check another pair

See the full matrix → · All RTX 5060 Ti 16GB benchmarks →

Llama 3.1 8B on other GPUs

RTX 4060 — Runs, 41.7 tok/s median
RTX 3060 Ti — Runs, 57.3 tok/s median
RTX 3060 12GB — Runs, 53.6 tok/s median
Intel Arc B580 — Runs, 40 tok/s median
RTX 4070 SUPER — Runs, 53.4 tok/s median
RTX 4060 Ti 16GB — Runs, 48.2 tok/s median
Intel Arc A770 16GB — Runs, 38.7 tok/s median
Radeon RX 9070 XT — Runs
RTX 4070 Ti SUPER — Runs, 53.9 tok/s median
RTX 4080 — Runs, 101 tok/s median
RTX 5070 Ti — Runs, 95.3 tok/s median
RTX 5080 — Runs, 88.5 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, 150 tok/s median
RTX A6000 48GB — Runs
RTX PRO 6000 Blackwell 96GB — Runs, 138 tok/s median

Other models on the RTX 5060 Ti 16GB

Llama 3.2 1B — Runs, 210 tok/s median
Llama 3.2 3B — Runs
Mistral 7B — Runs, 90.2 tok/s median
Qwen2.5 7B — Runs
DeepSeek-R1-Distill-Llama 8B — Runs
Qwen3 8B — Runs, 51.2 tok/s median
Gemma 2 9B — Runs
Gemma 3 12B — Runs
Qwen2.5 14B — Runs, 32.8 tok/s median
DeepSeek-R1-Distill-Qwen 14B — Runs
Qwen3 14B — Runs, 33 tok/s median
Phi-4 14B — Runs
gpt-oss-20b — Runs
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
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 above, 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.