Can the RTX 5060 Ti 16GB Run Qwen2.5 32B at Q6_K?
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Not entirely. Qwen2.5 32B at Q6_K is a 26.9 GB download (Hugging Face file listing for bartowski/Qwen2.5-32B-Instruct-GGUF), about 10.9 GB more than the RTX 5060 Ti 16GB's 16 GB, so the remainder has to be offloaded to system RAM. Generation speed is then set by system-memory and PCIe bandwidth rather than by the GPU, so no tokens-per-second figure is quoted for this pair. The smallest card in this matrix that holds it entirely is the RTX 5090 (32 GB).
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Published runs for Qwen2.5 32B on the RTX 5060 Ti 16GB
No published run for this pair has passed the checks yet.
More about this pair
How much VRAM does Qwen2.5 32B need?
The Q6_K file is 26.9 GB. Add roughly 1.5 GB for the runtime and an ~8K-token context, so about 28.4 GB of VRAM holds it comfortably. Longer contexts grow the KV cache and need more.
What speed does Qwen2.5 32B reach on the RTX 5060 Ti 16GB?
No published run of Qwen2.5 32B on the RTX 5060 Ti 16GB has been cited yet. Generation reads every weight once per token, so the RTX 5060 Ti 16GB's 448 GB/s of memory bandwidth divided by the 26.89 GB file puts the theoretical ceiling near 17 tokens per second once the whole model is resident. That is an upper bound, not a speed estimate: kernel, launch and compute overhead keep real runs below it.
Check another pair
Qwen2.5 32B on other GPUs
- RTX 4060 — Offload
- RTX 3060 Ti — Offload
- RTX 3060 12GB — Offload
- Intel Arc B580 — Offload
- RTX 4070 SUPER — Offload
- RTX 4060 Ti 16GB — Offload
- Intel Arc A770 16GB — Offload
- Radeon RX 9070 XT — Offload
- RTX 4070 Ti SUPER — Offload
- RTX 4080 — Offload
- RTX 5070 Ti — Offload
- RTX 5080 — Offload
- RTX 3090 — Offload
- Radeon RX 7900 XTX — Offload
- RTX 4090 — Offload
- RTX 5090 — Runs
- RTX A6000 48GB — Runs
- RTX PRO 6000 Blackwell 96GB — Runs
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 (1 run)
- Qwen2.5 7B — Runs
- Llama 3.1 8B — Runs, 65.1 tok/s median
- 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, 112 tok/s (1 run)
- 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 — Offload
- DeepSeek-R1-Distill-Qwen 32B — Offload
- Gemma 3 27B — 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 Q6_K 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.