01
Qwen3.5 9B Instruct (Q8)
Qwen / 9B / Q8_0 / ~10.7 GB
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: ~19 tok/s · first token ~0.6s
Local OKOK
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) at ~19 tok/s 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 runs at ~21 tok/s.
$ollama run qwen3.5:9b-q8_0
TOP PICK
Qwen3.5 9B Instruct (Q8)
EST. SPEED
~19 tok/s
VRAM NEEDED
~10.7 GB
Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks.
RTX 4060 Ti Estimated Tokens/sec by Model Size
Q4_K_M · ModelFit estimate| Model Size | Est. Speed | Fit on 16GB |
|---|---|---|
| 7B | ~38 tok/s | Fits in VRAM |
| 14B | ~21 tok/s | Fits in VRAM |
| 20B MoE (3.6B active) | ~32 tok/s | Fits in VRAM |
| 32B | ~2 tok/s | CPU offload (slow) |
| 35B MoE (3B active) | ~21 tok/s | CPU offload (slow) |
| 70B | ~1 tok/s | CPU offload (slow) |
| 120B MoE (5.1B active) | ~5 tok/s | CPU offload (slow) |
ModelFit estimates, not measured benchmarks: anchored to an 8B-class Q4_K_M model at 16K context on the RTX 4060 Ti's 288 GB/s bandwidth, then scaled by model size. 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.
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).
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.
Where to Buy the RTX 4060 Ti
≈ $409 streetStorage & accessories for your model library
Internal NVMe SSD · 2TB~$170
A Gen4 M.2 drive keeps your whole GGUF and quant collection on fast local storage, loading models straight off NVMe.
Check price on AmazonUSB4 NVMe Enclosure~$80
40Gbps external storage fast enough to run models from. Pair it with an M.2 drive for a portable model vault.
Check price on AmazonModelFit may earn a commission on purchases through these links, at no extra cost to you. Prices shown are approximate street references.
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.
RTX 4060 Ti vs Similar GPUs
| Hardware | Memory | Speed | Bandwidth | Price |
|---|---|---|---|---|
| RTX 3060 | 12 GB | 42 tok/s | 360 GB/s | $250 |
| RTX 4060 Ti | 16 GB | 34 tok/s | 288 GB/s | $409 |
| RTX 5060 Ti | 16 GB | 51 tok/s | 448 GB/s | $430 |
| RTX 4070 Ti SUPER | 16 GB | 72 tok/s | 672 GB/s | $1,148 |
Recommended Models
registry-verified10 models02
Gemma 4 12B
Gemma / 12B / Q4_K_M / ~8 GB
Best for: Chat, Coding, Multimodal·Pop: 80/100
Perf: ~24 tok/s · first token ~0.5s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for chat, coding, multimodal on RTX 4060 Ti.
03
Qwen3 14B
Qwen / 14B / Q4_K_M / ~11 GB
Best for: Coding, Quality·Pop: 84/100
Perf: ~21 tok/s · first token ~0.5s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for coding, quality on RTX 4060 Ti.
04
Gemma 3 12B Instruct
Gemma / 12B / Q4_K_M / ~9.5 GB
Best for: Chat, Quality·Pop: 76/100
Perf: ~24 tok/s · first token ~0.5s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for chat, quality on RTX 4060 Ti.
05
Mistral Nemo 12B
Mistral / 12B / Q4_K_M / ~9.5 GB
Best for: Chat, Translation·Pop: 78/100
Perf: ~24 tok/s · first token ~0.5s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for chat, translation on RTX 4060 Ti.
06
Llama 3.1 8B Instruct (Q5)
Llama / 8B / Q5_K_M / ~8 GB
Best for: Chat, Coding·Pop: 68/100
Perf: ~29 tok/s · first token ~0.5s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for chat, coding on RTX 4060 Ti.
07
GPT-OSS 20B
GPT-OSS / 21B / MXFP4 / ~13.8 GB
Best for: Chat, Coding, Reasoning·Pop: 85/100
Perf: ~29 tok/s · first token ~0.5s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for chat, coding, reasoning on RTX 4060 Ti.
08
LFM2 24B-A2B Instruct
LFM2 / 24B / Q4_K_M / ~14 GB
Best for: Local AI agents, privacy-first tool calling, MCP workflows·Pop: 80/100
Perf: ~38 tok/s · first token ~0.4s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for local ai agents, privacy-first tool calling, mcp workflows on RTX 4060 Ti.
09
Qwen3.5 9B Instruct
Qwen / 9B / Q4_K_M / ~7 GB
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: ~31 tok/s · first token ~0.5s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 4060 Ti.
10
Qwen2.5 Coder 14B
Qwen / 14B / Q4_K_M / ~11 GB
Best for: Coding·Pop: 68/100
Perf: ~21 tok/s · first token ~0.5s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for coding on RTX 4060 Ti.
Models Too Big for 16GB? Rent a Cloud GPU
by the hourThe RTX 4060 Ti tops out around up to 14b parameter models. For anything bigger, an hourly rented GPU runs the same open weights with the same Ollama workflow, billed by the hour, no hardware purchase needed.
RunPodHourly GPU pods (RTX 4090 to H100) with one-click Ollama/vLLM templates.Rent
Vast.aiMarketplace of rented GPUs, usually the cheapest per-hour prices.Rent
ModelFit may earn a commission on sign-ups made through these links, at no extra cost to you.
Similar GPUs for Local AI
Compatible Model Families
Qwen
Alibaba Cloud: Widest size range (0.5B to 235B)
LlamaMeta: Most popular open-weight model family
DeepSeekDeepSeek AI: Best-in-class reasoning with R1 models
MistralMistral AI: Excellent performance-per-parameter ratio
GemmaGoogle DeepMind: Excellent quality at small sizes (1B-9B)
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 50% faster with 8B models (~51 vs ~34 tok/s, estimated), 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 an estimated 32 tok/s.
The weekly local-AI refresh
New open-weight models, real Apple Silicon benchmarks, and the one model worth running on your Mac this week. Free, one email a week, unsubscribe anytime.
By subscribing you agree to our Privacy Policy and to receive the weekly email. Unsubscribe anytime.
Related Guides & Benchmarks
Want Personalized Recommendations?
Use our interactive wizard to compare models across Apple Silicon and NVIDIA GPUs.