Which local LLMs fit on an RTX 4070 Ti SUPER (16 GB GDDR6X)?
The RTX 4070 Ti SUPER has 16 GB GDDR6X of discrete VRAM. After a 1 GiB reserve for the OS and display, about 15 GiB is usable for a model. At an 8K context the largest model in our list that fits is Qwen3.8-27B at UD-IQ3_XXS (about 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 (44)
| Model | Quant | Total |
|---|---|---|
| Qwen3-14B | GGUF Q6_K (Qwen) | 12.7 GiB–13.4 GiB |
| Qwen3.8-27B | GGUF UD-IQ3_XXS (Unsloth) | 11.0 GiB–13.3 GiB |
| 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 |
| 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 |
|---|---|---|
| Mistral-Small-24B-Instruct-2501 | GGUF Q4_K_M (Unsloth) | 14.7 GiB–15.5 GiB |
| gpt-oss-20b | MXFP4 safetensors | 13.1 GiB–13.8 GiB |
Won't fit (40)
- 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 4070 Ti SUPER 16GB desktop
- Memory kindDiscrete VRAM
- Marketed16 GB GDDR6X
- Gross16.00 GiB (17,179,869,184 bytes)
- Default reserve1.00 GiB (1,073,741,824 bytes)
- Net budget15.00 GiB (16,106,127,360 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
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