Can the RTX 5070 Ti Run Qwen2.5 32B?
Offload
Not entirely. Qwen2.5 32B at Q4_K_M is a 19.9 GB download (Hugging Face file listing for bartowski/Qwen2.5-32B-Instruct-GGUF), about 3.9 GB more than the RTX 5070 Ti'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 Tesla P40 24GB (24 GB).
Buy the RTX 5070 Ti
GIGABYTE NVIDIA GeForce RTX 5070 Ti SFF 16G Graphics Card, 16GB 256-bit GDDR7, PCIe 5.0…Price moves too often to quote here — check the current one.
Published runs for Qwen2.5 32B on the RTX 5070 Ti
No published run for this pair has passed the checks yet.
More about this pair
How much VRAM does Qwen2.5 32B need?
The Q4_K_M file is 19.9 GB. Add roughly 1.5 GB for the runtime and an ~8K-token context, so about 21.4 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 5070 Ti?
No published run of Qwen2.5 32B on the RTX 5070 Ti has been cited yet. Generation reads every weight once per token, so the RTX 5070 Ti's 896 GB/s of memory bandwidth divided by the 19.85 GB file puts the theoretical ceiling near 45 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
- RTX 5060 Ti 16GB — Offload
- Intel Arc A770 16GB — Offload
- Radeon RX 9070 XT — Offload
- RTX 4070 Ti SUPER — Offload
- RTX 4080 — Offload
- RTX 5080 — Offload
- Tesla P40 24GB — Runs
- RTX 3090 — Runs, 23 tok/s median
- Radeon RX 7900 XTX — Runs, 28 tok/s (1 run)
- RTX 4090 — Runs, 34.4 tok/s (1 run)
- RTX 5090 — Runs, 54 tok/s median
- RTX A6000 48GB — Runs, 26.1 tok/s (1 run)
- RTX PRO 5000 Blackwell 48GB — Runs, 27.8 tok/s (1 run)
- Instinct MI210 64GB — Runs
- RTX PRO 6000 Blackwell 96GB — Runs, 56 tok/s (1 run)
Other models on the RTX 5070 Ti
- Llama 3.2 1B — Runs, 192 tok/s median
- Llama 3.2 3B — Runs
- Mistral 7B — Runs, 112 tok/s (1 run)
- Qwen2.5 7B — Runs
- Llama 3.1 8B — Runs, 95.3 tok/s median
- DeepSeek-R1-Distill-Llama 8B — Runs
- Qwen3 8B — Runs, 91.9 tok/s median
- Gemma 2 9B — Runs
- Gemma 3 12B — Runs
- Qwen2.5 14B — Runs, 55.1 tok/s median
- DeepSeek-R1-Distill-Qwen 14B — Runs
- Qwen3 14B — Runs, 58 tok/s median
- Phi-4 14B — Runs
- gpt-oss-20b — Runs, 156 tok/s (1 run)
- Gemma 3 27B — Offload
- Gemma 2 27B — Offload
- Qwen3 30B-A3B — Offload
- Qwen3 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.
As an Amazon Associate, SpecPicks earns from qualifying purchases. As an eBay Partner Network member, SpecPicks may earn a commission on eBay links.
This page is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.