Best local LLMs for RTX 5060 Ti 16GB
GeForce RTX 5060 Ti 16GB. Memory-fit math with KV cache, speeds labelled by how they were obtained, and commands you can paste.
Best for Coding · 16k context
| Model | Quant | Fit | Memory | Speed (generation) |
|---|---|---|---|---|
| Qwen3.5 9B | Q8_0 | fits fully | 10.1 GB | ~46 tok/s measured src |
| gpt-oss 20B | Q8_0 | fits fully | 12.5 GB | ~125 tok/s measured src |
| Qwen3-Coder 30B-A3B | Q4_K_M | MoE offload | 20.2 GB | ~71 tok/s estimated |
| Laguna XS 2.1 | Q4_K_M | MoE offload | 21.3 GB | ~72 tok/s estimated |
| Nemotron 3 Nano 30B-A3B | Q4_K_M | MoE offload | 24.8 GB | ~41 tok/s estimated |
llama-server -hf unsloth/Qwen3.5-9B-GGUF:Q8_0 -c 16384 -fa on --jinja -ngl 99
OLLAMA_CONTEXT_LENGTH=16384 ollama serve ollama run hf.co/unsloth/Qwen3.5-9B-GGUF:Q8_0
Context length is an Ollama server setting: quit the Ollama app or stop the service first, or set it in its environment (launchctl on Mac, systemctl on Linux).
Best for Chat / general · 16k context
| Model | Quant | Fit | Memory | Speed (generation) |
|---|---|---|---|---|
| Gemma 4 12B | Q8_0 | fits fully | 13.3 GB | ~31 tok/s measured src |
| Qwen3.5 9B | Q8_0 | fits fully | 10.1 GB | ~46 tok/s measured src |
| Ornith 1.5 9B | Q8_0 | fits fully | 10.1 GB | ~46 tok/s scaled from measured src |
| gpt-oss 20B | Q8_0 | fits fully | 12.5 GB | ~125 tok/s measured src |
| Gemma 4 E4B | Q8_0 | fits fully | 8.5 GB | ~52 tok/s scaled from measured src |
llama-server -hf unsloth/gemma-4-12b-it-GGUF:Q8_0 -c 16384 -fa on --jinja -ngl 99
OLLAMA_CONTEXT_LENGTH=16384 ollama serve ollama run hf.co/unsloth/gemma-4-12b-it-GGUF:Q8_0
Context length is an Ollama server setting: quit the Ollama app or stop the service first, or set it in its environment (launchctl on Mac, systemctl on Linux).
Best for Vision · 16k context
| Model | Quant | Fit | Memory | Speed (generation) |
|---|---|---|---|---|
| Gemma 4 12B | Q8_0 | fits fully | 13.3 GB | ~31 tok/s measured src |
| Qwen3.5 9B | Q8_0 | fits fully | 10.1 GB | ~46 tok/s measured src |
| Qwen3-VL 8B | Q8_0 | fits fully | 11.1 GB | ~49 tok/s measured src |
| Qwen3.5 4B | Q8_0 | fits fully | 5.1 GB | ~80 tok/s measured src |
| Qwen3.6 35B-A3B | UD-Q4_K_M | MoE offload | 22.5 GB | ~66 tok/s estimated |
llama-server -hf unsloth/gemma-4-12b-it-GGUF:Q8_0 -c 16384 -fa on --jinja -ngl 99
OLLAMA_CONTEXT_LENGTH=16384 ollama serve ollama run hf.co/unsloth/gemma-4-12b-it-GGUF:Q8_0
Context length is an Ollama server setting: quit the Ollama app or stop the service first, or set it in its environment (launchctl on Mac, systemctl on Linux).
All 23 models that fit, fastest first · 16k context
Click a column header to sort. 10 of 23 speeds here are measured or scaled from measured; the rest are bandwidth estimates. Filter by use case, speed source, fit, license and more in the interactive advisor →
| Model | Params | Quant | Fit | Memory | Tok/s | Source |
|---|---|---|---|---|---|---|
| LFM2.5 24B-A2B | 23.97B MoE | Q4_K_M | fits fully | 14.9 GB | ~180 | scaled from measured |
| gpt-oss 20B | 20.91B MoE | Q8_0 | fits fully | 12.5 GB | ~125 | measured |
| GLM-4.7-Flash | 31.22B MoE | Q4_K_M | MoE offload | 19.2 GB | ~92 | estimated |
| Nemotron 3 Nano 4B | 3.97B | Q8_0 | fits fully | 4.6 GB | ~84 | scaled from measured |
| Gemma 4 26B-A4B | 25.81B MoE | UD-Q4_K_M | MoE offload | 17.5 GB | ~82 | estimated |
| Qwen3.5 4B | 4.66B | Q8_0 | fits fully | 5.1 GB | ~80 | measured |
| Laguna XS 2.1 | 33.44B MoE | Q4_K_M | MoE offload | 21.3 GB | ~72 | estimated |
| Qwen3-Coder 30B-A3B | 30.53B MoE | Q4_K_M | MoE offload | 20.2 GB | ~71 | estimated |
| Ornith 1.5 35B-A3B | 35.95B MoE | Q4_K_M | MoE offload | 22.3 GB | ~69 | estimated |
| Qwen3.6 35B-A3B | 35.95B MoE | UD-Q4_K_M | MoE offload | 22.5 GB | ~66 | estimated |
| DeepSeek R1 Distill Qwen 7B | 7.62B | Q8_0 | fits fully | 9.0 GB | ~53 | scaled from measured |
| Gemma 4 E4B | 8B | Q8_0 | fits fully | 8.5 GB | ~52 | scaled from measured |
| Qwen3-VL 8B | 8.77B | Q8_0 | fits fully | 11.1 GB | ~49 | measured |
| Qwen3.5 9B | 9.65B | Q8_0 | fits fully | 10.1 GB | ~46 | measured |
| Ornith 1.5 9B | 9.65B | Q8_0 | fits fully | 10.1 GB | ~46 | scaled from measured |
| Nemotron 3.5 Lightning 30B-A3B | 31.58B MoE | UD-Q4_K_M | MoE offload | 25.4 GB | ~45 | estimated |
| Nemotron 3 Nano 30B-A3B | 31.58B MoE | Q4_K_M | MoE offload | 24.8 GB | ~41 | estimated |
| Gemma 4 12B | 11.96B | Q8_0 | fits fully | 13.3 GB | ~31 | measured |
| Mistral Small 3.2 24B | 24.01B | Q4_K_M | partial offload | 17.0 GB | ~16 | estimated |
| Qwen3.8 27B | 27.78B | UD-Q4_K_M | partial offload | 17.7 GB | ~12 | estimated |
| Qwen3.6 27B | 27.78B | Q4_K_M | partial offload | 18.1 GB | ~11 | estimated |
| Gemma 4 31B | 31.27B | Q4_K_M | partial offload | 20.5 GB | ~8 | estimated |
| DeepSeek R1 Distill Qwen 32B | 32.76B | Q4_K_M | partial offload | 24.1 GB | ~6 | estimated |
Long context on this hardware · up to 1M tokens
Largest context whose KV cache still fits next to the weights (same placement as the 16k result, KV types f16 / q8_0 / q4_0). Models are capped at their verified maximum; extended means beyond the native maximum using YaRN, only listed where the model card documents it. Long context needs flash attention (-fa on, already in the commands) and a lot of memory; a quantized KV cache saves memory but can cost accuracy. Speed at depth is an estimate (penalty fitted to measured depth runs up to 128k).
Try it in the advisor: 128k · 256k · 512k · 1M
| Model | Native max | Max ctx f16 | Max ctx q8_0 | Max ctx q4_0 | Est. tok/s at max f16 ctx |
|---|---|---|---|---|---|
| Qwen3.6 35B-A3B UD-Q4_K_M | 256k | 512k extended | 1M extended | 1M extended | ~12 estimated |
| Nemotron 3 Nano 4B Q8_0 | 256k | 256k | 256k | 256k | ~48 estimated |
| Gemma 4 26B-A4B UD-Q4_K_M | 256k | 256k | 256k | 256k | ~31 estimated |
| Qwen3.5 4B Q8_0 | 256k | 256k | 512k extended | 1M extended | ~33 estimated |
| Laguna XS 2.1 Q4_K_M | 256k | 256k | 256k | 256k | ~13 estimated |
| Ornith 1.5 35B-A3B Q4_K_M | 256k | 256k | 256k | 256k | ~22 estimated |
| Nemotron 3.5 Lightning 30B-A3B UD-Q4_K_M | 256k | 256k | 256k | 256k | ~30 estimated |
| Nemotron 3 Nano 30B-A3B Q4_K_M | 256k | 256k | 256k | 256k | ~29 estimated |
| Qwen3.8 27B UD-Q4_K_M | 256k | 256k | 512k extended | 1M extended | ~7 estimated |
| Qwen3.6 27B Q4_K_M | 256k | 256k | 512k extended | 1M extended | ~6 estimated |
| GLM-4.7-Flash Q4_K_M | 198k | 128k | 128k | 128k | ~23 estimated |
| Qwen3-Coder 30B-A3B Q4_K_M | 256k | 128k | 256k | 512k extended | ~12 estimated |
| Gemma 4 E4B Q8_0 | 128k | 128k | 128k | 128k | ~44 estimated |
| Qwen3.5 9B Q8_0 | 256k | 128k | 256k | 512k extended | ~34 estimated |
| Ornith 1.5 9B Q8_0 | 256k | 128k | 256k | 256k | ~34 estimated |
| Mistral Small 3.2 24B Q4_K_M | 128k | 128k | 128k | 128k | ~7 estimated |
| Gemma 4 31B Q4_K_M | 256k | 128k | 256k | 256k | ~5 estimated |
| gpt-oss 20B Q8_0 | 128k | 64k | 128k | 128k | ~80 estimated |
| DeepSeek R1 Distill Qwen 7B Q8_0 | 128k | 64k | 128k | 128k | ~39 estimated |
| Gemma 4 12B Q8_0 | 256k | 64k | 128k | 256k | ~29 estimated |
| DeepSeek R1 Distill Qwen 32B Q4_K_M | 128k | 64k | 128k | 128k | ~4 estimated |
| Qwen3-VL 8B Q8_0 | 256k | 32k | 64k | 128k | ~35 estimated |
| LFM2.5 24B-A2B Q4_K_M | 128k | 16k | 32k | 64k | ~155 estimated |
FAQ
What is the best local LLM for RTX 5060 Ti 16GB?
Of the 10 models that fit fully at 16k context, LFM2.5 24B-A2B Q4_K_M is the fastest at ~180 tok/s (scaled from measured); the largest is LFM2.5 24B-A2B Q4_K_M.
How much memory can RTX 5060 Ti 16GB use for a model?
About 15.0 GB usable for the model and KV cache, with 448 GB/s memory bandwidth.
Can RTX 5060 Ti 16GB run long contexts?
See the long-context table above: it lists the largest context whose KV cache fits next to the weights for every model, up to 1M tokens where the model supports it.
Open the interactive advisor with this hardware →
Speeds marked measured are medians of real runs (short-context generation) from these sources; scaled from measured means a measurement of a same-size or same-model-different-quant run, scaled by bytes read; estimated comes from memory bandwidth (spec source: https://www.techpowerup.com/gpu-specs/geforce-rtx-5060-ti-16-gb.c4292) × an efficiency factor of 0.91 calibrated to measured runs on this hardware. 11 measured data points on file for this hardware · submit your benchmark. System RAM here is an example (32 GB, ddr5-5600). Model data last verified 2026-10-20. Ranking = fits and fast enough first, then larger; not a quality benchmark.
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