Can I Run / Qwen 2.5 Coder 7B / on NVIDIA RTX 2080 Ti
Can I Run Qwen 2.5 Coder 7B on a NVIDIA RTX 2080 Ti?
Yes
Runs at Q8_0 — near-lossless quality.
Model size
7.6B
GPU memory
11.0GB
Smallest quant
Q2_K
Best fit
Q8_0
11 quantizations fit your 11.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 9.1 GB | 10.6 GB | 8.1 GB | +1.9 GB |
| Q6_K | 7.3 GB | 8.8 GB | 6.3 GB | +3.7 GB |
| Q5_K_M | 6.4 GB | 7.9 GB | 5.4 GB | +4.6 GB |
| Q5_K_S | 6.2 GB | 7.7 GB | 5.3 GB | +4.8 GB |
| Q4_K_M | 5.6 GB | 7.1 GB | 4.7 GB | +5.4 GB |
| Q4_K_S | 5.3 GB | 6.8 GB | 4.5 GB | +5.7 GB |
| Q4_0 | 5.3 GB | 6.8 GB | 4.4 GB | +5.7 GB |
| Q3_K_L | 4.4 GB | 5.9 GB | 4.1 GB | +6.6 GB |
| Q3_K_M | 4.2 GB | 5.7 GB | 3.8 GB | +6.8 GB |
| Q3_K_S | 3.9 GB | 5.4 GB | 3.5 GB | +7.1 GB |
| Q2_K | 3.5 GB | 5.0 GB | 3.0 GB | +7.5 GB |
Try it in the cloud first
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Full model details
Qwen 2.5 Coder 7B →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
NVIDIA RTX 2080 Ti →
Top-ranked open-source models that fit in 11.0GB.
FAQ
Can the NVIDIA RTX 2080 Ti run Qwen 2.5 Coder 7B?
Yes. The NVIDIA RTX 2080 Ti's 11.0GB of VRAM is enough to run Qwen 2.5 Coder 7B at Q8_0 quantization (9.1GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 11.0GB. It uses about 9.1GB of memory and 10.6GB recommended for comfortable inference.
What if I need more headroom for context length?
KV cache memory grows with context length. The numbers above assume a baseline 2K-4K context. For long-context use (32K+), add another 2-6GB depending on the model architecture.