RTX 4060 Ti 16GB for Local LLMs: What Fits, What Doesn't, and How Fast
The RTX 4060 Ti 16GB is the card a lot of serious hobbyists land on — 16GB of VRAM at a mid-range price. But it comes with a catch that raw capacity hides: a 128-bit memory bus that gives it less bandwidth than a 3060, despite holding more. That shapes everything about what it's good for. Here's exactly what fits, at what quant and context, and where the speed tradeoff actually bites.
The hardware baseline
The RTX 4060 Ti 16GB has 16GB of GDDR6 on a 128-bit bus, giving 288 GB/s of memory bandwidth. That's the number that determines decode speed — and it's the card's weak point. A 3060, one tier down in VRAM, runs a 192-bit bus at 360 GB/s. So the 4060 Ti holds more but feeds it slower.
At roughly 70% real-world utilization (accounting for KV cache reads, framework overhead, and memory controller efficiency), effective bandwidth is around 201 GB/s for decode — below the 3060's ~252 GB/s. The practical consequence: the 16GB lets you fit models a 3060 can't, but you generate tokens more slowly once they're loaded. This card rewards capacity-bound workloads, not speed-bound ones.
What fits — model by model
Weight sizes below are calculated from real GGUF file sizes rather than theoretical bit counts. K-quants carry embedding tables, output heads, and per-block scale factors that naive bit-math misses — the effective bytes per parameter at Q4_K_M is closer to 0.61 than the 0.5 bytes (4 bits ÷ 8) a flat calculation gives.
| Model | Quant | Weight size | Fits 16GB | Est. speed |
|---|---|---|---|---|
| Llama 3.1 8B | Q8_0 | 8.51 GB | ✓ | ~24 tok/s |
| Gemma 2 9B | Q8_0 | 9.79 GB | ✓ | ~21 tok/s |
| Qwen3 14B | Q4_K_M | 9.03 GB | ✓ | ~22 tok/s |
| Qwen3 14B | Q5_K_M | 10.51 GB | ✓ | ~19 tok/s |
| Qwen3 14B | Q6_K | 12.14 GB | ✓ | ~17 tok/s |
| Phi-4 14B | Q6_K | 12.05 GB | ✓ | ~17 tok/s |
| Qwen3 14B | Q8_0 | 15.69 GB | ✗ | — |
| Mistral Small 24B | Q4_K_M | 14.64 GB | ✓ | ~14 tok/s |
| Mistral Small 24B | Q5_K_M | 17.04 GB | ✗ | — |
| Qwen3 32B | Q4_K_M | 20.01 GB | ✗ | — |
Weight sizes calculated from calibrated bytes-per-parameter values sourced from real GGUF file sizes. Speed estimates assume 288 GB/s peak bandwidth at 70% real-world efficiency. Actual speed varies by backend and context length. Treat speeds as ±20%. Qwen3-14B Q8_0 is marked ✗ because although 15.69GB of weights loads, it leaves almost no room for KV cache — you can't actually run it at usable context.
Context length: the hidden constraint
The 16GB is what lets you run a 14B at higher quant than a 12GB card — but the higher the quant, the less room is left for KV cache, which grows with context length and comes out of the same pool. Here's how context changes the total for Qwen3 14B at Q6_K (12.14GB weights), at F16 KV cache:
| Context length | KV cache | Total (weights + KV + overhead) | Fits 16GB |
|---|---|---|---|
| 4K (4,096) | 0.67 GB | 13.45 GB | ✓ |
| 8K (8,192) | 1.34 GB | 14.12 GB | ✓ |
| 16K (16,384) | 2.68 GB | 15.46 GB | ✓ |
| 32K (32,768) | 5.37 GB | 18.15 GB | ✗ |
Qwen3-14B at Q6_K fits to roughly 18–20K context on 16GB — a real win over a 12GB card, which can only run the same model at Q4_K_M and to about 8K. The 16GB buys you both higher quant and longer context on a 14B. If you need to go further, Q8 KV cache roughly halves the KV cost. The tradeoff is speed: Q6_K runs around 17 tok/s here versus about 28 tok/s for Q4_K_M on a 3060.
Recommended configurations
Best use of the 16GB: Qwen3 14B Q6_K
12.14GB weights, fits to ~18–20K context, ~17 tok/s. This is the config that justifies the card — a 14B at near-Q8 quality with long context, which a 12GB card can't hold. Just go in knowing ~17 tok/s is slower than the same 14B at Q4 on a 3060; you're spending speed to buy quality and context.
Better speed: Qwen3 14B Q4_K_M
9.03GB weights, ~22 tok/s, with a large amount of the 16GB left for context. If ~17 tok/s at Q6_K feels sluggish, dropping to Q4_K_M is noticeably faster and still leaves far more headroom than a 12GB card would — a good default when responsiveness matters more than the last increment of quality.
Biggest model: Mistral Small 24B Q4_K_M
14.64GB weights, short context only (roughly 4K before KV cache tips it over 16GB), ~14 tok/s. This is the 24B tier the extra 4GB unlocks — but it's tight and slow. Worth it only when raw model capability matters more than speed or long conversations; otherwise a 14B at Q6_K is the better all-round use of the card.
The bandwidth reality
The 4060 Ti 16GB's 128-bit bus gives 288 GB/s — narrower than the 3060's 192-bit/360 GB/s. It holds more but feeds it slower, landing about 20% fewer tok/s than a 3060 on a model both can run. Choose this card for capacity — a high-quant 14B or a 24B — not for speed. If your target model fits in 12GB, a 3060 will run it faster.
Check any model or quant combination
The table above covers the most common configurations, but you can check any model, quantization, and context length combination — including KV cache quantization and estimated decode speed for your specific GPU — using the calculator.
FAQ
Is the RTX 4060 Ti 16GB good for local LLMs?
It depends what you want. The 16GB is real and lets you run a 14B model at Q6_K quality or a 24B at Q4 — more than a 12GB 3060 can hold. But the 128-bit memory bus caps bandwidth at 288 GB/s, below the 3060's 360 GB/s, so it generates tokens slower. It's a capacity card, not a speed card: buy it to fit bigger or higher-quality models, not to run them fast.
What is the largest model that fits on a 4060 Ti 16GB?
Mistral Small 24B at Q4_K_M (about 14.6GB) fits, but only at short context — roughly 4K tokens before KV cache pushes it over 16GB. More comfortably, a 14B model fits up to Q6_K (about 12.1GB) with room for long context. A 14B at Q8_0 (about 15.7GB) technically loads but leaves almost nothing for KV cache. Qwen3-32B needs about 20GB and does not fit.
How fast does the RTX 4060 Ti 16GB run local LLMs?
Its 288 GB/s memory bandwidth works out to roughly 201 GB/s effective for decode. That gives about 24 tok/s for an 8B model at Q8_0, about 17 tok/s for a 14B at Q6_K, and about 14 tok/s for a 24B at Q4_K_M. Usable, but slower than a 3060 on the same model despite the larger VRAM — the narrow 128-bit bus is the limiting factor.
RTX 4060 Ti 16GB or RTX 3060 12GB for local LLMs?
The 16GB fits bigger models, higher quants, and longer context; the 3060's 360 GB/s runs about 20% faster per token. If the model you want fits in 12GB, the 3060 is the faster and cheaper choice. If you need the extra 4GB for a 14B at Q6_K/Q8_0 or a 24B at Q4, the 4060 Ti 16GB earns its place — as long as you accept slower generation as the price of capacity.