Qwen3.5 9B Instruct (Q8)
Local OKOKB · 26
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: est. 28 tok/s, typical error 5% · first token ~2.3s
Fits in 16 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 4060 Ti.
Best Local AI Models for RTX 4060 Ti (16GB)
The RTX 4060 Ti 16GB offers entry-level 16GB VRAM at an affordable price. Despite lower bandwidth than newer cards, its 16GB capacity allows running 14B parameter models that 12GB cards cannot. A solid choice for users who need larger models on a budget.
Quick answer
The best local LLM for the RTX 4060 Ti is Qwen3.5 9B Instruct (Q8) (est. 28 tok/s, typical error 5%) on its 16GB VRAM. It uses ~10.7GB of VRAM; the RTX 4060 Ti handles up to 14B parameter models at Q4. A 14B model at Q4 runs at est. 25 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: Gemma 4 E4B (Q8) (4.5B, ~7.5GB) and Gemma 4 12B (12B, ~8GB). On this card Qwen3.5 9B Instruct (Q8) runs at est. 28 tok/s, typical error 5%. 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 qwen3.5:9b-q8_0
- Top pick
- Qwen3.5 9B Instruct (Q8)
- Decode speed
- est. 28 tok/s, typical error 5%
- VRAM needed
- ~10.7 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 4060 Ti 16GB Local LLM (2026): Runs 14B at est. 25 tok/s, https://modelfit.io/gpu/rtx-4060-ti/, updated October 2026, CC BY 4.0.
Specs
VRAM
16 GB GDDR6
Speed (8B Q4)
est. 43 tok/stypical error 5%
Bandwidth
288 GB/s
Architecture
Ada Lovelace
Max model size
Up to 14B parameter models
Compatibility
10 excellent, 0 workable
Table 1
RTX 4060 Ti estimated tokens/sec by model size
Q4_K_M · ModelFit estimate| Model size | Est. speed | Fit on 16GB |
|---|---|---|
| 7B | est. 49 tok/stypical error 5% | Fits in VRAM |
| 14B | est. 25 tok/stypical error 15% | Fits in VRAM |
| 20B MoE (3.6B active) | est. 58 tok/stypical error 15% | Fits in VRAM |
| 32B | est. 2 tok/stypical error 5% | CPU offload (slow) |
| 35B MoE (3B active) | est. 24 tok/stypical error 15% | CPU offload (slow) |
| 70B | est. 1 tok/stypical error 10% | CPU offload (slow) |
| 120B MoE (5.1B active) | est. 15 tok/stypical error 15% | CPU offload (slow) |
Fig. 1RTX 4060 Ti: estimated speed by model size
Decode speed, generic Q4_K_M model of each size at 4K context, 288 GB/s, typical error 5-15%
Fits in 16 GBCPU offload (slow)
7B49
14B25
20B MoE (3.6B active)58
32B2
35B MoE (3B active)24
70B1
120B MoE (5.1B active)15
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 4060 Ti's 288 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. What typical error means.
Fig. 2
Context costs VRAM too.
Qwen3.5 9B Instruct (Q8) loads ~10.7 GB of weights; at 16k context the KV cache adds ~0.5 GB (still fits the ~14 GB usable VRAM), and at 64k it adds ~2.0 GB (still fits).
Weights 11 GBKV cache · 16k context 0.5 GBHeadroom 3.2 GBRuntime reserve 1.6 GB
The model leaves about 3.2 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 4060 Ti
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RTX 4060 Ti VRAM for AI: what actually fits?
16GB VRAM opens the door to 14B parameter models at Q4 quantization. DeepSeek-R1 14B, Qwen 2.5 14B, and other mid-size models use about 9-10GB, fitting comfortably. The main limitation is bandwidth: at 288 GB/s, the 4060 Ti is slower per-token than the RTX 3060 despite being a newer card. Think of it as a capacity card, not a speed card. If you plan to run 14B models, the extra 4GB of VRAM matters more than raw speed.
Table 2
RTX 4060 Ti vs similar GPUs
| Hardware | Memory | Speed | Bandwidth |
|---|---|---|---|
| RTX 4060 | 8 GB | est. 40 tok/stypical error 5% | 272 GB/s |
| RTX 3060 | 12 GB | est. 50 tok/stypical error 5% | 360 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 |
| RTX 4070 Ti SUPER | 16 GB | est. 94 tok/stypical error 5% | 672 GB/s |
Recommended models
registry-verified10 modelsGemma 4 E4B (Q8)
Local OKExcellentA · 48
Best for: On-device, Mobile, Chat·Pop: 82/100
Perf: est. 46 tok/s, typical error 5% · first token ~2.0s
Fits in 16 GB VRAM with room to spare. Best for on-device, mobile, chat on RTX 4060 Ti.
Gemma 4 12B
Local OKExcellentA · 44
Best for: Chat, Coding, Multimodal·Pop: 80/100
Perf: est. 30 tok/s, typical error 15% · first token ~0.6s
Fits in 16 GB VRAM with room to spare. Best for chat, coding, multimodal on RTX 4060 Ti.
LFM2 24B-A2B Instruct
Local OKOKB · 3
Best for: Local AI agents, privacy-first tool calling, MCP workflows·Pop: 80/100
Perf: est. 96 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 4060 Ti.
Gemma 4 12B (Q8)
Local OKOKB · 11
Best for: Chat, Coding, Multimodal·Pop: 80/100
Perf: est. 18 tok/s, typical error 15% · first token ~3.3s
Fits in 16 GB VRAM with room to spare. Best for chat, coding, multimodal on RTX 4060 Ti.
Qwen3.5 9B Instruct
Local OKExcellentA · 51
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: est. 46 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 4060 Ti.
Ministral 3 14B
Local OKExcellentA · 37
Best for: Chat, Reasoning, Vision·Pop: 74/100
Perf: est. 26 tok/s, typical error 15% · first token ~0.7s
Fits in 16 GB VRAM with room to spare. Best for chat, reasoning, vision on RTX 4060 Ti.
Ornith 1.0 9B
Local OKExcellentA · 61
Best for: Agentic coding on small machines·Pop: 76/100
Perf: est. 46 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 4060 Ti.
LFM2.5 8B-A1B
Local OKExcellentA · 62
Best for: On-device agents, tool calling, multilingual chat·Pop: 72/100
Perf: est. 124 tok/s, typical error 15% · first token ~0.1s
Fits in 16 GB VRAM with room to spare. Best for on-device agents, tool calling, multilingual chat on RTX 4060 Ti.
MiMo-V2.6-Distill-Qwen-9B
Local OKExcellentA · 60
Best for: Agentic coding, Small machines·Pop: 60/100
Perf: est. 44 tok/s, typical error 5% · first token ~0.4s
Fits in 16 GB VRAM with room to spare. Best for agentic coding, small machines on RTX 4060 Ti.
Can I run it on the RTX 4060 Ti?
Per-model verdict pages for this exact card: weights, KV cache by context length, and estimated tok/s, each derived from the same engine the recommendations above use.
- Qwen3 8B Q4_K_Mfits
- Qwen3 14B Q4_K_Mfits
- GPT-OSS 20B MXFP4fits
- Qwen3.8 27B Q4_K_Mtight
- Llama 3.3 70B Instruct Q4_K_Mno
- Qwen3.5 35B-A3B Instruct Q4_K_Mtight
- Gemma 4 26B-A4B Q4_K_Mtight
- Qwen3.5 4B Instruct Q4_K_Mfits
- Gemma 4 E4B Q4_K_Mfits
- Gemma 3 4B Instruct Q4_K_Mfits
- Llama 3.1 8B Instruct Q4_K_Mfits
- Qwen3.5 9B Instruct Q4_K_Mfits
- Gemma 4 12B Q4_K_Mfits
- Mistral Nemo 12B Q4_K_Mfits
- Qwen3.5 9B Instruct (Q8) Q8_0fits
- Gemma 4 12B (Q8) Q8_0fits
- LFM2 24B-A2B Instruct Q4_K_Mfits
- Qwen3.5 27B Instruct Q4_K_Mtight
- Qwen3.6 27B Q4_K_Mtight
- Gemma 4 31B Q4_K_Mtight
- Qwen3.6 35B-A3B Q4_K_Mno
- Qwen3.8 27B (Q8) Q8_0no
Similar GPUs for local AI
- RTX 40608GB · est. 40 tok/s
- RTX 306012GB · est. 50 tok/s
- RTX 5060 Ti16GB · est. 68 tok/s
- RTX 4070 Ti SUPER16GB · est. 94 tok/s
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 4060 Ti FAQ: common questions
How much VRAM does the RTX 4060 Ti have for LLMs?
The RTX 4060 Ti comes in 8GB and 16GB variants. For local AI, you need the 16GB version. It provides about 15.5GB usable VRAM after overhead, enough to load 14B parameter models at Q4 quantization.
What size LLM can I run on an RTX 4060 Ti 16GB?
Up to 14B parameter models at Q4 quantization. This includes DeepSeek-R1 14B, Qwen 2.5 14B, and Phi-3 14B. Smaller 7B models run with plenty of room for longer context windows.
Is the RTX 4060 Ti good for local AI?
It depends on what you need. The 16GB variant is excellent for running 14B models on a budget. However, its low memory bandwidth (288 GB/s) makes it slower than the RTX 3060 for 7B models. Choose it for model size, not speed.
RTX 4060 Ti vs RTX 5060 Ti for AI workloads?
The RTX 5060 Ti (16GB GDDR7) is 58% faster with 8B models (est. 68 tok/s, typical error 5% vs est. 43 tok/s), thanks to GDDR7 bandwidth (448 vs 288 GB/s). At similar pricing, the 5060 Ti is the clear winner if you can find one in stock.
How fast is a 27B-class model on the RTX 4060 Ti?
The RTX 4060 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. 58 tok/s, typical error 15%. The largest individual model in the catalog that fits is LFM2 24B-A2B Instruct (24B).
Related guides & benchmarks
Sizing local AI? Start with RAM & VRAM
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