Gemma 4 12B
Local OKExcellentA · 44
Best for: Chat, Coding, Multimodal·Pop: 80/100
Perf: est. 48 tok/s, typical error 15% · first token ~0.5s
Fits in 16 GB VRAM with room to spare. Best for chat, coding, multimodal on RTX 5060 Ti.
Best Local AI Models for RTX 5060 Ti (16GB)
The RTX 5060 Ti brings GDDR7 memory and Blackwell architecture to the budget segment. On 8B models it reaches est. 68 tok/s, typical error 5%, outperforming the older 4060 Ti by 58% while offering the same 16GB VRAM capacity for 14B models.
Quick answer
The best local LLM for the RTX 5060 Ti is Gemma 4 12B (est. 48 tok/s, typical error 15%) on its 16GB VRAM. It uses ~8GB of VRAM; the RTX 5060 Ti handles dense models up to ~14B at Q4. A 14B model at Q4 runs at est. 41 tok/s, typical error 15%. (ModelFit, 2026)
Sizing rule: a Q4 model needs about 0.6 GB of VRAM per billion parameters, and ModelFit budgets 90% of this card's 16GB for weights, context, and KV-cache. The per-size table below uses that same budget. Other strong fits: Mellum2.1 12B-A2.5B Thinking (12.1B, ~8.1GB) and Qwen3.5 9B Instruct (Q8) (9B, ~10.7GB). On this card Gemma 4 12B runs at est. 48 tok/s, typical error 15%. What it will not run: a 32B model does not fit in VRAM alone, so it spills to system RAM over PCIe and slows sharply.
$ollama run gemma4:12b
- Top pick
- Gemma 4 12B
- Decode speed
- est. 48 tok/s, typical error 15%
- VRAM needed
- ~8 GB
Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks. Typical error: the median gap between the ModelFit speed model and measured runs on hardware the model was not fitted on, for this backend and model size.
Cite this page: ModelFit, RTX 5060 Ti 16GB for Local LLMs: Runs 14B Q4 (est. 41 tok/s), https://modelfit.io/gpu/rtx-5060-ti/, updated October 2026, CC BY 4.0.
Specs
VRAM
16 GB GDDR7
Speed (8B Q4)
est. 68 tok/stypical error 5%
Bandwidth
448 GB/s
Architecture
Blackwell
Max model size
Up to ~14B dense
Compatibility
10 excellent, 0 workable
Table 1
RTX 5060 Ti estimated tokens/sec by model size
Q4_K_M · ModelFit estimate| Model size | Est. speed | Fit on 16GB |
|---|---|---|
| 7B | est. 78 tok/stypical error 5% | Fits in VRAM |
| 14B | est. 41 tok/stypical error 15% | Fits in VRAM |
| 20B MoE (3.6B active) | est. 91 tok/stypical error 15% | Fits in VRAM |
| 32B | est. 4 tok/srough estimate, offload not measured | CPU offload (slow) |
| 35B MoE (3B active) | est. 37 tok/srough estimate, offload not measured | CPU offload (slow) |
| 70B | est. 1 tok/srough estimate, offload not measured | CPU offload (slow) |
| 120B MoE (5.1B active) | est. 23 tok/srough estimate, offload not measured | CPU offload (slow) |
Fig. 1RTX 5060 Ti: estimated speed by model size
Decode speed, generic Q4_K_M model of each size at 4K context, 448 GB/s, typical error 5-15%
Fits in 16 GBCPU offload (slow)
7B78
14B41
20B MoE (3.6B active)91
32B4
35B MoE (3B active)37
70B1
120B MoE (5.1B active)23
tok/s est.ModelFit estimates, not measured benchmarks: decode speed of a generic Q4_K_M model of each size at 4K context on the RTX 5060 Ti's 448 GB/s bandwidth. MoE rows scale by active parameters (decode reads only the active experts), so a 35B MoE runs far faster than a dense 32B. "CPU offload" sizes exceed the 16GB VRAM; dense models slow to a crawl there, MoE models degrade less because hot experts stay GPU-resident. Offload rows are rough estimates: no measured run covers offload, so they carry no typical error. What typical error means.
Fig. 2
Context costs VRAM too.
Gemma 4 12B loads ~8 GB of weights; at 16k context the KV cache adds ~3.0 GB (still fits the ~14 GB usable VRAM), and at 64k it adds ~12.0 GB (exceeds the budget, use a smaller quant or a q8_0 KV cache).
Weights 8.0 GBKV cache · 16k context 3.0 GBHeadroom 3.4 GBRuntime reserve 1.6 GB
The model leaves about 3.4 GB of the usable VRAM budget free after weights and 16k context.
KV-cache figures assume an fp16 cache, the llama.cpp/Ollama default. Standard GQA models use a size-class estimate (8 KV heads x 128 head dim class); hybrid linear-attention models (Qwen3.5/3.6, Qwen3-Next) use the exact per-token cost from their published config, since only their sparse full-attention layers cache KV. A q8_0 KV cache roughly halves either figure. Estimates, not measurements.
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Where to buy the RTX 5060 Ti
Storage & accessories for your model library
- Internal NVMe SSD · 2TBA Gen4 M.2 drive keeps your whole GGUF and quant collection on fast local storage, loading models straight off NVMe.Check price on Amazon
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RTX 5060 Ti VRAM for AI: what actually fits?
16GB GDDR7 at 448 GB/s gives the 5060 Ti a significant advantage over the 4060 Ti. The same 14B models that run at est. 25 tok/s, typical error 15% on the older card now hit est. 41 tok/s. You can load DeepSeek-R1 14B or Qwen 2.5 14B with 5-6GB to spare for KV cache. GDDR7 also improves batch throughput, making the 5060 Ti viable for light multi-user serving.
Table 2
RTX 5060 Ti vs similar GPUs
| Hardware | Memory | Speed | Bandwidth |
|---|---|---|---|
| RTX 3060 | 12 GB | est. 50 tok/stypical error 5% | 360 GB/s |
| RTX 5070 | 12 GB | est. 98 tok/stypical error 5% | 672 GB/s |
| RTX 4060 Ti | 16 GB | est. 43 tok/stypical error 5% | 288 GB/s |
| RTX 5060 Ti | 16 GB | est. 68 tok/stypical error 5% | 448 GB/s |
Recommended models
10 modelsMellum2.1 12B-A2.5B Thinking
Local OKExcellentA · 44
Best for: Agentic coding, Small machines·Pop: 76/100
Perf: est. 132 tok/s, typical error 15% · first token ~0.1s
Fits in 16 GB VRAM with room to spare. Best for agentic coding, small machines on RTX 5060 Ti.
Qwen3.5 9B Instruct (Q8)
Local OKOKB · 26
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: est. 45 tok/s, typical error 5% · first token ~2.1s
Fits in 16 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 5060 Ti.
Gemma 4 E4B (Q8)
Local OKExcellentA · 48
Best for: On-device, Mobile, Chat·Pop: 82/100
Perf: est. 73 tok/s, typical error 5% · first token ~1.9s
Fits in 16 GB VRAM with room to spare. Best for on-device, mobile, chat on RTX 5060 Ti.
NVIDIA Nemotron 3.5 Bolt E4B
Local OKExcellentA · 45
Best for: Voice assistants, Tool calling, Chat·Pop: 75/100
Perf: est. 92 tok/s, typical error 5% · first token ~0.3s
Fits in 16 GB VRAM with room to spare. Best for voice assistants, tool calling, chat on RTX 5060 Ti.
Gemma 4 12B (Q8)
Local OKOKB · 11
Best for: Chat, Coding, Multimodal·Pop: 80/100
Perf: est. 30 tok/s, typical error 15% · first token ~3.0s
Fits in 16 GB VRAM with room to spare. Best for chat, coding, multimodal on RTX 5060 Ti.
Ministral 3 14B
Local OKExcellentA · 37
Best for: Chat, Reasoning, Vision·Pop: 74/100
Perf: est. 42 tok/s, typical error 15% · first token ~0.6s
Fits in 16 GB VRAM with room to spare. Best for chat, reasoning, vision on RTX 5060 Ti.
Qwen3.5 9B Instruct
Local OKExcellentA · 51
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: est. 73 tok/s, typical error 5% · first token ~0.4s
Fits in 16 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 5060 Ti.
LFM2 24B-A2B Instruct
Local OKOKB · 3
Best for: Local AI agents, privacy-first tool calling, MCP workflows·Pop: 80/100
Perf: est. 146 tok/s, typical error 15% · first token ~0.1s
Fits in 16 GB VRAM with room to spare. Best for local ai agents, privacy-first tool calling, mcp workflows on RTX 5060 Ti.
Ornith 1.0 9B
Local OKExcellentA · 61
Best for: Agentic coding on small machines·Pop: 76/100
Perf: est. 73 tok/s, typical error 5% · first token ~0.4s
Fits in 16 GB VRAM with room to spare. Best for agentic coding on small machines on RTX 5060 Ti.
Video and image models on the RTX 5060 Ti
Runs · Stated: the linked source says a GPU with that much VRAM runs the model. Runs · Published: a dated run on the named card with that much VRAM. unknown: no source yet. Exceeds VRAM: even the main weights are too large for 16 GB. Every VRAM tier: video · image. Ranked with reasons: video models for 16 GB of VRAM · image models for 16 GB of VRAM.
Video
| Model | Fits | Quality | App |
|---|---|---|---|
| HunyuanVideo-1.5Tencent Hunyuan Community License | Runs · Stated14 GB VRAM · official runner with model offloading · HunyuanVideo-1.5 model card | #38 of 48 in text to video4,292 votessnapshot 2026-09-22 | ComfyUI |
| LTX-2.3LTX-2 Community License Agreement | Runs · Stated16 GB VRAM · distilled with FP8 cast, previews only · LTX blog: hardware for AI video models | No independent score yet | Lightricks/LTX-2 ltx-pipelines, ComfyUI |
| LTX-2.5LTX-2.x Community License Agreement | Runs · Stated12 GB VRAM · LTX Desktop on Windows, weight streaming, 40 GB RAM, 540p · LTX Desktop compatibility matrix (Lightricks) | No independent score yet | Lightricks/LTX-2 ltx-pipelines, ComfyUI |
| MiniMax H3MiniMax H3 Community License Agreement | Runs · PublishedRTX 5060 · 16 GB VRAM · ComfyUI int8 convrot with int4 text encoder, 1024x768, 10 s in 24 min · ComfyUI issue #15274 | #8 of 48 in text to video11,417 votessnapshot 2026-09-22 | stable-diffusion.cpp, ComfyUI |
| Wan2.1Apache License 2.0 | Runs · Stated13 GB VRAM · T2V 14B in Diffusers, bf16 with group offloading to system RAM · Diffusers docs: Wan pipeline | No independent score yet | ComfyUI |
| Wan2.2 A14BApache License 2.0 | Runs · PublishedRTX 5060 Ti · 16 GB VRAM · ComfyUI fp16 I2V with 4-step LoRAs, 64 GB system RAM · ComfyUI issue #10662 | #42 of 48 in text to video10,458 votessnapshot 2026-09-22 | ComfyUI |
| Wan2.2 TI2V 5BApache License 2.0 | Runs · Stated8 GB VRAM · ComfyUI native offloading · ComfyUI docs: Wan2.2Complete: Comfy-Org repackaged FP16 with FP8 scaled UMT518.1 GB download | No independent score yet | ComfyUI |
| Mochi 1 previewApache License 2.0 | unknownComplete: calcuis GGUF Q3_K_M with Q4_0 T5 and FP8 VAE7.7 GB download | #48 of 48 in text to video5,893 votessnapshot 2026-09-22 | Diffusers, ComfyUI |
Quality: blind human votes from Arena (arena.ai), CC BY 4.0, snapshot 2026-09-22. Scores are for the Arena checkpoint, not your local quantized build.
Image
| Model | Fits | Quality | App |
|---|---|---|---|
| FLUX 3 Image KleinFLUX Kommunity License v1.0 | Runs · Stated8 GB VRAM · official ComfyUI int8 files · FLUX 3 Image Klein README (Black Forest Labs) | No independent score yet | Diffusers |
| FLUX.1 [dev]FLUX.1 [dev] Non-Commercial License v1.1.1 | Runs · Stated10 GB VRAM · FP4 on RTX 50 (Blackwell) · Black Forest Labs blog: FLUX with NVIDIA | #78 of 82 in text to image49,221 votessnapshot 2026-10-07 | Diffusers, ComfyUI |
| FLUX.2 [klein]Apache 2.0 (4B), FLUX Non-Commercial License (9B) | Runs · Stated13 GB VRAM · Klein 4B · FLUX.2 [klein] 4B model cardComplete: Klein 4B BF1616.0 GB download | No independent score yet | Diffusers, stable-diffusion.cpp |
| Krea 2 TurboKrea 2 Community License Agreement v.1 | Runs · PublishedRTX 4070 · 12 GB VRAM · ComfyUI fp8, dynamic VRAM paging, 8 steps · ComfyUI issue #15365 | No independent score yet | Diffusers, stable-diffusion.cpp |
| Qwen-Image-2.1Qwen Research License Agreement | Runs · PublishedRTX 5080 · 16 GB VRAM · stable-diffusion.cpp Q8_0, 1024x1024 · stable-diffusion.cpp issue #2041 | #19 of 82 in text to image11,441 votessnapshot 2026-10-07 | Diffusers, stable-diffusion.cpp |
| SDXL Base 1.0CreativeML Open RAIL++-M License | Runs · Stated8 GB VRAM · SDXL 1.0, no app named · SDXL 1.0 announcement (Stability AI) | No independent score yet | ComfyUI, stable-diffusion.cpp |
| Stable Diffusion 3.5 LargeStability AI Community License | Runs · Stated11 GB VRAM · FP8 TensorRT build on RTX 40 and 50 · Stability AI: SD3.5 TensorRT models | #81 of 82 in text to image23,396 votessnapshot 2026-10-07 | ComfyUI |
| Z-Image-TurboApache 2.0 | Runs · Stated16 GB VRAM · stated for consumer GPUs · Z-Image-Turbo model card | #59 of 82 in text to image24,808 votessnapshot 2026-10-07 | Diffusers, stable-diffusion.cpp |
| MOSS-ImageApache 2.0 | unknownComplete: UD-Q4_K_XL GGUF13.2 GB download | No independent score yet | stable-diffusion.cpp |
| FLUX.2 [dev]FLUX Non-Commercial License | Exceeds VRAMComplete: Q4_K_M GGUF34.8 GB download | #38 of 82 in text to image77,988 votessnapshot 2026-10-07 | Diffusers, ComfyUI, stable-diffusion.cpp |
Quality: blind human votes from Arena (arena.ai), CC BY 4.0, snapshot 2026-10-07. Scores are for the Arena checkpoint, not your local quantized build.
Similar GPUs for local AI
Speeds are estimates for Qwen3 8B Q4_K_M, typical error 5%.
Compatible model families
- QwenAlibaba Cloud: Qwen3.8 27B local flagship (Apache 2.0, vision)
- LlamaMeta: Most popular open-weight model family
- DeepSeekDeepSeek AI: V4.1 Flash: multimodal 552B MoE, API only
- MistralMistral AI: Excellent performance-per-parameter ratio
- GemmaGoogle DeepMind: Gemma 4 E4B: 4.5B effective, runs on 8GB Macs
- PhiMicrosoft: Best quality-per-gigabyte at small sizes
RTX 5060 Ti FAQ: common questions
How much VRAM does the RTX 5060 Ti have for LLMs?
The RTX 5060 Ti has 16GB GDDR7 VRAM with 448 GB/s bandwidth. About 15.5GB is usable for model loading. The GDDR7 memory is 55% faster than the GDDR6 in the 4060 Ti, directly boosting inference speed.
What size LLM can I run on an RTX 5060 Ti?
Up to 14B parameter models at Q4 quantization, same as other 16GB cards. The difference is speed: the 5060 Ti processes tokens 58% faster than the 4060 Ti thanks to GDDR7 bandwidth.
Is the RTX 5060 Ti worth it over the RTX 3060 for AI?
Yes, if you want 14B models. The 5060 Ti offers 4GB more VRAM (16 vs 12GB) and 24% more bandwidth (448 vs 360 GB/s). For 7B-only workloads, the cheaper RTX 3060 is still excellent value.
RTX 5060 Ti vs RTX 5070 for local AI?
The RTX 5070 (12GB GDDR7) is faster with 8B models (est. 98 tok/s, typical error 5%) but has 4GB less VRAM. Choose the 5060 Ti for 14B models, or the 5070 for maximum speed with 7B-9B models.
How fast is a 27B-class model on the RTX 5060 Ti?
The RTX 5060 Ti's 16GB of VRAM cannot fit a 32B model comfortably. The largest size class it fits is 20B MoE (3.6B active), at est. 91 tok/s, typical error 15%. The largest individual model in the catalog that fits is LFM2 24B-A2B Instruct (24B).
Related guides & benchmarks
More local AI guides
- How Much RAM (or VRAM) Do You Need for a Local LLM?The model-size-to-memory matrix: what each VRAM and RAM tier actually runs.
- Best LLM for MacBook (Apple Silicon)Unified-memory Macs run bigger models per dollar. Picks by RAM tier, M1 to M5.
- Local AI image generator on MacCompare image models by RAM and app.
- Local image generation on MacImage models and apps by Mac RAM.
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