RTX 5060 Ti 16GB vs 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) for local LLMs
Memory fit, speed and context compared with the same math as the advisor. Both offer about the same usable memory (15.0 GB), so the difference is mostly bandwidth (448 vs 288 GB/s).
Verdict from the numbers
- Fit. RTX 5060 Ti 16GB fits 10 of 28 models fully at 16k context (largest: LFM2.5 24B-A2B Q4_K_M); 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) fits 10 (largest: LFM2.5 24B-A2B Q4_K_M). Both fit the same set of models.
- Speed. On the 10 models that fit fully on both, RTX 5060 Ti 16GB is faster by a typical 1.5x. RTX 5060 Ti 16GB is ahead on 10, 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) on 0. 5 of those comparisons use measured speeds on both sides; the rest involve at least one estimate (see the badges).
- Trade-off. Both offer about the same usable memory (15.0 GB), so the difference is mostly bandwidth (448 vs 288 GB/s).
Where to check prices
Price tier note
We do not list prices because they move daily and differ between new and used stock. What the data does show is what the memory difference buys: both have the same usable memory, so model fit is identical and the choice comes down to speed (448 vs 288 GB/s bandwidth). Use the Amazon links above to compare current listings and decide whether the difference is worth it for the models you want to run.
Model by model · 16k context, f16 KV cache
| Model | RTX 5060 Ti 16GB fit | tok/s | 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) fit | tok/s | Faster / fits |
|---|---|---|---|---|---|
| Gemma 4 26B-A4B UD-Q4_K_M | MoE offload to CPU | 82 estimated | MoE offload to CPU | 68 estimated | - |
| Gemma 4 31B Q4_K_M | partial offload (slow) | 8 estimated | partial offload (slow) | 7 estimated | - |
| Gemma 4 12B Q8_0 | fits fully | 31 measured | fits fully | 21 measured | RTX 5060 Ti 16GB |
| Qwen3.6 27B Q4_K_M | partial offload (slow) | 11 estimated | partial offload (slow) | 10 estimated | - |
| Qwen3.6 35B-A3B UD-Q4_K_M | MoE offload to CPU | 66 estimated | MoE offload to CPU | 60 estimated | - |
| Qwen3.5 9B Q8_0 | fits fully | 46 measured | fits fully | 30 measured | RTX 5060 Ti 16GB |
| Qwen3.5 4B Q8_0 | fits fully | 80 measured | fits fully | 54 measured | RTX 5060 Ti 16GB |
| gpt-oss 20B Q8_0 | fits fully | 125 measured | fits fully | 94 measured | RTX 5060 Ti 16GB |
| gpt-oss 120B | does not fit | - | does not fit | - | - |
| Qwen3-Coder 30B-A3B Q4_K_M | MoE offload to CPU | 71 estimated | MoE offload to CPU | 63 estimated | - |
| Mistral Small 3.2 24B Q4_K_M | partial offload (slow) | 16 estimated | partial offload (slow) | 13 estimated | - |
| Qwen3-VL 8B Q8_0 | fits fully | 49 measured | fits fully | 33 measured | RTX 5060 Ti 16GB |
| GLM-4.5-Air | does not fit | - | does not fit | - | - |
| Qwen3.8 27B UD-Q4_K_M | partial offload (slow) | 12 estimated | partial offload (slow) | 10 estimated | - |
| LFM2.5 24B-A2B Q4_K_M | fits fully | 180 scaled from measured | fits fully | 140 scaled from measured | RTX 5060 Ti 16GB |
| Nemotron 3.5 Super VL 120B-A12B | does not fit | - | does not fit | - | - |
| Gemma 4 E4B Q8_0 | fits fully | 52 scaled from measured | fits fully | 35 scaled from measured | RTX 5060 Ti 16GB |
| Qwen3.8 Flash Next | does not fit | - | does not fit | - | - |
| Nemotron 3 Nano 30B-A3B Q4_K_M | MoE offload to CPU | 41 estimated | MoE offload to CPU | 38 estimated | - |
| Nemotron 3 Nano 4B Q8_0 | fits fully | 84 scaled from measured | fits fully | 57 scaled from measured | RTX 5060 Ti 16GB |
| Nemotron 3.5 Lightning 30B-A3B UD-Q4_K_M | MoE offload to CPU | 45 estimated | MoE offload to CPU | 42 estimated | - |
| GLM-4.7-Flash Q4_K_M | MoE offload to CPU | 92 estimated | MoE offload to CPU | 80 estimated | - |
| Ornith 1.5 9B Q8_0 | fits fully | 46 scaled from measured | fits fully | 30 scaled from measured | RTX 5060 Ti 16GB |
| Ornith 1.5 35B-A3B Q4_K_M | MoE offload to CPU | 69 estimated | MoE offload to CPU | 63 estimated | - |
| DeepSeek R1 Distill Qwen 7B Q8_0 | fits fully | 53 scaled from measured | fits fully | 35 scaled from measured | RTX 5060 Ti 16GB |
| DeepSeek R1 Distill Qwen 32B Q4_K_M | partial offload (slow) | 6 estimated | partial offload (slow) | 5 estimated | - |
| Laguna XS 2.1 Q4_K_M | MoE offload to CPU | 72 estimated | MoE offload to CPU | 65 estimated | - |
| Llama 3.3 70B | does not fit | - | does not fit | - | - |
Badges: measured medians of real llama.cpp runs; scaled from measured and estimated come from our bandwidth model. See sources and methods.
How much context fits?
Largest context where weights plus KV cache stay inside usable memory, per model that fits fully on at least one side. q8_0 KV roughly halves the cache.
| Model | RTX 5060 Ti 16GB f16 | 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) f16 | RTX 5060 Ti 16GB q8_0 | 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) q8_0 |
|---|---|---|---|---|
| LFM2.5 24B-A2B | 16k | 16k | 32k | 32k |
| gpt-oss 20B | 64k | 64k | 128k | 128k |
| Gemma 4 12B | 64k | 64k | 128k | 128k |
| Qwen3.5 9B | 128k | 128k | 256k | 256k |
| Ornith 1.5 9B | 128k | 128k | 256k | 256k |
| Qwen3-VL 8B | 32k | 32k | 64k | 64k |
| Gemma 4 E4B | 128k | 128k | 128k | 128k |
| DeepSeek R1 Distill Qwen 7B | 64k | 64k | 128k | 128k |
| Qwen3.5 4B | 256k | 256k | 512k | 512k |
| Nemotron 3 Nano 4B | 256k | 256k | 256k | 256k |
Spec summary
| RTX 5060 Ti 16GB | 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) | |
|---|---|---|
| Usable memory for model + KV cache | 15.0 GB | 15.0 GB |
| Memory bandwidth | 448 GB/s | 288 GB/s |
| Models that fit fully at 16k | 10 of 28 | 10 of 28 |
| Models that need offload or do not fit | 18 | 18 |
| Measured data points on file | 11 | 14 |
FAQ
Which is faster for local LLMs, RTX 5060 Ti 16GB or 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM)?
On the 10 models that fit fully on both, RTX 5060 Ti 16GB is typically 1.5x faster (geometric mean). RTX 5060 Ti 16GB wins 10 and 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) wins 0. 5 comparisons are measured on both.
Which fits more models, RTX 5060 Ti 16GB or 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM)?
RTX 5060 Ti 16GB fits 10 of 28 models fully at 16k context and 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) fits 10.
Can I run long context on RTX 5060 Ti 16GB or 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM)?
At f16 KV cache, the largest-model context limits are in the table above (for example LFM2.5 24B-A2B on RTX 5060 Ti 16GB: 16k; LFM2.5 24B-A2B on 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM): 16k). A q8_0 KV cache extends it further.
Do you state prices for RTX 5060 Ti 16GB or 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM)?
No. Prices change daily, so we link to Amazon search results instead and focus on measured and estimated speed and memory fit.
Related
RTX 5060 Ti 16GB · 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) · Measured model pages: gpt-oss 20B · Mistral Small 3.2 24B · Gemma 4 12B · Qwen3.5 9B · Qwen3-VL 8B · gpt-oss 20B
More comparisons: RTX 5060 Ti 16GB vs RTX 5070 Ti 16GB · RTX 5070 Ti 16GB vs 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) · all comparisons
By memory: 8 GB · 12 GB · 16 GB · 24 GB · 32 GB · 48 GB · 64 GB · 96 GB · 128 GB · 192 GB · 256 GB · 512 GB