Which local LLMs fit on an RTX 5080 (16 GB GDDR7)?
The RTX 5080 has 16 GB GDDR7 of discrete VRAM. After a 2 GiB reserve for the OS and display, about 14 GiB is usable for a model. At an 8K context the largest model in our list that fits is DeepSeek-V2-Lite-Chat at Q4_K_M (about 12.2 GiB). Qwen3.8-27B at UD-IQ3_XXS is tight (about 11 GiB–13.3 GiB).
These are estimates from the model's config and file sizes, not measurements. How we calculate
Which models fit at 8K
Each model uses the highest-precision file that fits, one chat at a time.
Fits (43)
| Model | Quant | Total |
|---|---|---|
| Phi-4-reasoning | GGUF Q5_K_M (Unsloth) | 11.6 GiB–12.2 GiB |
| DeepSeek-V2-Lite-Chat | GGUF Q4_K_M (mradermacher) | 10.0 GiB–12.2 GiB |
| Qwen2.5-14B-Instruct | GGUF Q5_K_M (Qwen) | 11.4 GiB–12.1 GiB |
| Qwen2.5-Coder-14B-Instruct | GGUF Q5_K_M (Qwen) | 11.4 GiB–12.1 GiB |
| OpenMath-Nemotron-14B | GGUF Q5_K_M (mradermacher) | 11.4 GiB–12.1 GiB |
| phi-4 | GGUF Q5_K_M (Unsloth) | 11.4 GiB–12.1 GiB |
| Qwen3-14B | GGUF Q5_K_M (Qwen) | 11.2 GiB–11.8 GiB |
| Mistral-Nemo-Instruct-2407 | GGUF Q6_K (Unsloth) | 10.7 GiB–11.4 GiB |
| GLM-4-9B-0414 | GGUF Q8_0 (Unsloth) | 9.8 GiB–10.3 GiB |
| granite-3.3-8b-instruct | GGUF Q8_0 (IBM) | 9.5 GiB–10.1 GiB |
| Qwen3-VL-8B-Instruct | GGUF Q8_0 (Qwen) | 9.4 GiB–10.0 GiB |
| Qwen3-8B-Base | GGUF Q8_0 (mradermacher) | 9.4 GiB–10.0 GiB |
| Qwen3-8B | GGUF Q8_0 | 9.4 GiB–10.0 GiB |
| Qwen2.5-Math-7B-Instruct | GGUF Q8_0 (bartowski) | 8.1 GiB–10.0 GiB |
| DeepSeek-R1-Distill-Llama-8B | GGUF Q8_0 (Unsloth) | 9.1 GiB–9.7 GiB |
| Gemma 3 12B IT | GGUF Q4_0 (Google QAT) | 8.5 GiB–9.0 GiB |
| Mistral-7B-Instruct-v0.3 | GGUF Q8_0 (bartowski) | 8.3 GiB–8.9 GiB |
| Qwen2.5-Coder-7B | GGUF Q8_0 (mradermacher) | 8.1 GiB–8.7 GiB |
| Qwen2.5-Coder-7B-Instruct | GGUF Q8_0 (Qwen) | 8.1 GiB–8.7 GiB |
| Qwen2.5-7B-Instruct | GGUF Q8_0 | 8.1 GiB–8.7 GiB |
| DeepSeek-R1-Distill-Qwen-7B | GGUF Q8_0 (Unsloth) | 8.1 GiB–8.7 GiB |
| OpenMath-Nemotron-7B | GGUF Q8_0 (mradermacher) | 8.1 GiB–8.7 GiB |
| Qwen2.5-VL-7B-Instruct | GGUF Q8_0 (Unsloth) | 8.1 GiB–8.7 GiB |
| Qwen2-7B-Instruct | GGUF Q8_0 (Qwen) | 8.1 GiB–8.7 GiB |
| Phi-3.5-mini-instruct | GGUF Q8_0 (bartowski) | 6.9 GiB–7.6 GiB |
| Llama 3.2 3B Instruct | Official BF16 safetensors | 7.0 GiB–7.5 GiB |
| Qwen3-VL-4B-Instruct | GGUF Q8_0 (Qwen) | 5.2 GiB–5.8 GiB |
| Qwen3-4B-Instruct-2507 | GGUF Q8_0 (Unsloth) | 5.2 GiB–5.8 GiB |
| Qwen3-4B | GGUF Q8_0 | 5.2 GiB–5.8 GiB |
| Phi-4-mini-instruct | GGUF Q8_0 (Unsloth) | 4.9 GiB–5.5 GiB |
| Qwen2.5-Coder-3B-Instruct | GGUF Q8_0 (Qwen) | 3.8 GiB–4.2 GiB |
| Qwen2.5-3B-Instruct | GGUF Q8_0 | 3.8 GiB–4.2 GiB |
| Qwen2.5-VL-3B-Instruct | GGUF Q8_0 (Unsloth) | 3.5 GiB–3.9 GiB |
| SmolLM2-1.7B-Instruct | GGUF Q8_0 (Unsloth) | 3.3 GiB–3.8 GiB |
| Gemma 3 4B IT | GGUF Q4_0 (Google QAT) | 3.3 GiB–3.8 GiB |
| Qwen3-1.7B-Base | GGUF Q8_0 (mradermacher) | 2.7 GiB–3.2 GiB |
| Qwen3-1.7B | GGUF Q8_0 | 2.7 GiB–3.2 GiB |
| Llama 3.2 1B Instruct | Official BF16 safetensors | 2.7 GiB–3.1 GiB |
| Qwen2.5-Coder-1.5B-Instruct | GGUF Q8_0 (Qwen) | 2.1 GiB–2.5 GiB |
| Qwen2.5-1.5B-Instruct | GGUF Q8_0 (Qwen) | 2.1 GiB–2.5 GiB |
| Qwen3-0.6B | GGUF Q8_0 (Qwen) | 1.6 GiB–2.0 GiB |
| Gemma 3 1B IT | GGUF Q4_0 (Google QAT) | 1.1 GiB–1.5 GiB |
| Qwen2.5-0.5B-Instruct | GGUF Q8_0 (Qwen) | 0.8 GiB–1.2 GiB |
Tight fit (2)
| Model | Quant | Total |
|---|---|---|
| gpt-oss-20b | MXFP4 safetensors | 13.1 GiB–13.8 GiB |
| Qwen3.8-27B | GGUF UD-IQ3_XXS (Unsloth) | 11.0 GiB–13.3 GiB |
Won't fit (41)
- Mistral-Small-24B-Instruct-2501 · Q4_K_M · 15.5 GiB
- Llama 3.1 8B Instruct · BF16 · 16.8 GiB
- Gemma 3 27B IT · Q4_0 · 18 GiB
- Qwen3-30B-A3B · Q4_K_M · 18.9 GiB
- Qwen3-30B-A3B-Instruct-2507 · Q4_K_M · 18.9 GiB
- Qwen3-VL-30B-A3B-Instruct · Q4_K_M · 18.9 GiB
- Qwen3-32B · Q4_K_M · 21.4 GiB
- OpenMath-Nemotron-32B · Q4_K_M · 21.5 GiB
- Qwen2.5-VL-32B-Instruct · Q4_K_M · 21.5 GiB
- DeepSeek-R1-Distill-Qwen-32B · Q4_K_M · 21.5 GiB
- QwQ-32B · Q4_K_M · 21.5 GiB
- Qwen2.5-Coder-32B-Instruct · Q4_K_M · 21.5 GiB
- Qwen2.5-32B-Instruct · Q4_K_M · 21.5 GiB
- Drama 3 · Unofficial int4 W4A16 · 27 GiB
- Mixtral-8x7B-Instruct-v0.1 · Q4_K_M · 28.6 GiB
- Llama-3.1-Nemotron-70B-Instruct-HF · Q4_K_M · 43.6 GiB
- Qwen2.5-72B-Instruct · Q4_K_M · 45 GiB
- Qwen3-Next-80B-A3B-Instruct · Q4_K_M · 47.3 GiB
- gpt-oss-120b · MXFP4 · 62.8 GiB
- GLM-4.5-Air · Q4_K_M · 71.4 GiB
- Qwen3.8-Flash-Next · Q4_K_M · 131.5 GiB
- MiniMax-M2 · Q4_K_M · 134 GiB
- Qwen3-235B-A22B · Q4_K_M · 137.1 GiB
- Qwen3-235B-A22B-Instruct-2507 · Q4_K_M · 137.1 GiB
- Llama 3.3 70B Instruct · BF16 · 137.2 GiB
- DeepSeek-V4-Flash-0731 · FP8 · 183.7 GiB
- GLM-4.6 · Q4_K_M · 208.4 GiB
- GLM-4.5 · Q4_K_M · 209.2 GiB
- MiniMax-M3 · Q4_K_M · 249.7 GiB
- GLM-5.3-Flash · Q8_0 · 373.6 GiB
- DeepSeek-V3 · Q4_K_M · 443.1 GiB
- DeepSeek-R1 · Q4_K_M · 443.1 GiB
- DeepSeek-V3-0324 · Q4_K_M · 443.1 GiB
- DeepSeek-R1-0528 · Q4_K_M · 443.7 GiB
- DeepSeek-V3.1 · Q4_K_M · 444.2 GiB
- Muse Spark Open · NVFP4 · 508.9 GiB
- DeepSeek-V4.1-Flash · FP8 · 558.8 GiB
- Ling-3.1-flash · FP8 · 628.3 GiB
- Kimi-K2-Instruct · Q4_K_M · 679.5 GiB
- Kimi-K2-Thinking · Q4_K_M · 680 GiB
- GLM-5.3 · Q8_0 · 877 GiB
Detailed checks
Memory
- NVIDIAGeForce RTX 5080 16GB desktop
- Memory kindDiscrete VRAM
- Marketed16 GB GDDR7
- Gross16.00 GiB (17,179,869,184 bytes)
- Default reserve2.00 GiB (2,147,483,648 bytes)
- Net budget14.00 GiB (15,032,385,536 bytes)
The reserve is an adjustable assumption. On a headless machine you can lower it in the calculator.
Two of these cards do not add up to one bigger card. Each copy of a model has to fit on one card unless the engine splits it with tensor parallelism.
Related pages
Sources
- NVIDIA GeForce compare — 50 Series — Standard Memory Config 16 GB GDDR7
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