Models › Llama 3.1 8B Instruct › RTX 4060 Ti 16GB
Can I run Llama 3.1 8B Instruct on RTX 4060 Ti 16GB?
Yes
Yes. Llama 3.1 8B Instruct fits fully on the RTX 4060 Ti 16GB at Q8_0 (about 10.6 GB of 16 GB usable, 8k context). ESTIMATE
Verdict by quantization (8k context, f16 KV, 32 GB RAM for offload)
| Quant | Needs | RTX 4060 Ti 16GB (16 GB) |
|---|---|---|
| Q3_K_M | 5.9 GB | ✅ Fits |
| IQ4_XS | 6.2 GB | ✅ Fits |
| Q4_K_M | 6.8 GB | ✅ Fits |
| MXFP4 | 6.6 GB | ✅ Fits |
| Q5_K_M | 7.7 GB | ✅ Fits |
| Q6_K | 8.6 GB | ✅ Fits |
| Q8_0 | 10.6 GB | ✅ Fits |
| F16 | 18.5 GB | 🐢 Needs RAM offload |
Measured speed MEASURED
| Quant | Generation (tok/s) | Prompt (tok/s) | Source |
|---|---|---|---|
| Q4_K_M | 48.87 | — | www.localscore.ai (2026-10-20) |
Memory figures are estimates from the model's parameter count and config.json (Hugging Face). How we calculate. Last verified with data from 2026-10-20.
FAQ
Can I run Llama 3.1 8B Instruct on the RTX 4060 Ti 16GB?
Yes. Llama 3.1 8B Instruct fits fully on the RTX 4060 Ti 16GB at Q8_0 (about 10.6 GB of 16 GB usable, 8k context).
How much VRAM does Llama 3.1 8B Instruct need?
About 6.8 GB at Q4_K_M and 10.6 GB at Q8_0 with 8k context (estimate).
Llama 3.1 8B Instruct on other hardware: RTX 3060 12GB · RTX 5060 Ti 16GB · RTX 4070 Ti Super · RTX 3090 · RTX 4090 · RTX 5090 · Radeon RX 7900 XTX · Apple M4 Pro (48 GB) · Apple M4 Max (128 GB)
Other models on RTX 4060 Ti 16GB: gpt-oss 20B (MoE) · Qwen3.6 27B · Qwen3.6 35B A3B (MoE) · Qwen3.8 27B · Gemma 4 26B A4B (MoE) · Gemma 4 12B · Qwen3.5 9B
See all models for 16 GB VRAM.
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