The RTX 5060 has 8 GB of GDDR7 and 448 GB/s of bandwidth. That is about 65 percent more bandwidth than an RTX 4060, so 8B models should run noticeably faster, but the memory size is the same.
Check your own setup with the Can My GPU Run This AI Model? calculator. The numbers below come from the same data.
RTX 5060 specs that matter for local AI
- Memory: 8 GB GDDR7 on a 128-bit bus
- Memory bandwidth: 448 GB/s
- Board power: 145 W
VRAM decides which models fit. Bandwidth decides how fast they generate text, because every new token means reading the whole model from memory.
Which AI models fit on the RTX 5060?
The table assumes Q4_K_M quantization (about 4.9 bits per weight) and an 8K context. Totals include the KV cache and about 1 GB of runtime overhead. Speed is an estimate: memory bandwidth divided by model size, at 60 to 80 percent efficiency, which matches published llama.cpp results on several RTX cards.
| Model | Weights | KV cache (8K) | Total (est.) | On 8 GB | Speed (est.) |
|---|---|---|---|---|---|
| Qwen3 4B | 2.5 GB | 1.2 GB | 4.7 GB | Fits | 109 to 146 tok/s |
| Llama 3.1 8B Instruct | 4.9 GB | 1.1 GB | 7.0 GB | Fits | 55 to 73 tok/s |
| Qwen3 8B | 5.0 GB | 1.2 GB | 7.2 GB | Fits | 54 to 71 tok/s |
| Gemma 3 12B Instruct | 7.5 GB | 0.9 GB | 9.3 GB | No | – |
| Qwen3 14B | 9.0 GB | 1.3 GB | 11.4 GB | No | – |
| OpenAI gpt-oss-20b | 12.8 GB | 0.2 GB | 14.0 GB | No | – |
| Mistral Small 3.2 24B | 14.7 GB | 1.3 GB | 17.0 GB | No | – |
| Qwen3.6 27B | 17.0 GB | 0.5 GB | 18.6 GB | No | – |
| Gemma 3 27B Instruct | 16.8 GB | 1.1 GB | 18.9 GB | No | – |
| Qwen3 32B | 20.1 GB | 2.1 GB | 23.2 GB | No | – |
| Qwen3.6 35B-A3B | 22.0 GB | 0.2 GB | 23.2 GB | No | – |
“Fits” means the estimate is at most 92 percent of VRAM. “Tight” means it is between 92 and 100 percent, so close other GPU apps. Mixture-of-Experts models (gpt-oss-20b and Qwen3.6 35B-A3B) need their full weights in memory, but they only use part of them per token, so speed is not estimated.
Best models for the RTX 5060
- Llama 3.1 8B and Qwen3 8B (Q4_K_M): about 7 GB at 8K context. The fit is snug, so keep the context to 8K or less and close other GPU apps.
- Qwen3 4B: about 4.7 GB, leaving room for long context.
How context length changes things
The KV cache grows with every token of context, so a model that fits at 4K can fail at 32K.
| Model (Q4_K_M) | Context | KV cache | Total (est.) | On 8 GB |
|---|---|---|---|---|
| Llama 3.1 8B Instruct | 4K | 0.54 GB | 6.5 GB | Fits |
| Llama 3.1 8B Instruct | 8K | 1.07 GB | 7.0 GB | Fits |
| Llama 3.1 8B Instruct | 16K | 2.15 GB | 8.1 GB | No |
| Llama 3.1 8B Instruct | 32K | 4.29 GB | 10.2 GB | No |
| Qwen3 14B | 4K | 0.67 GB | 10.7 GB | No |
| Qwen3 14B | 8K | 1.34 GB | 11.4 GB | No |
| Qwen3 14B | 16K | 2.68 GB | 12.7 GB | No |
| Qwen3 14B | 32K | 5.37 GB | 15.4 GB | No |
A Q8 KV cache (an option in Ollama and llama.cpp) roughly halves the cache cost if you need more context.
What does not fit
Q6 and Q8 versions of 8B models, and every model of 12B and above (Gemma 3 12B needs about 9.3 GB, Qwen3 14B about 11.4 GB), exceed 8 GB once context is added. Layers can be offloaded to system RAM, but speed drops sharply.
A fast 8 GB card. It suits 4B to 8B models. If you want bigger models, 12 GB or 16 GB cards offer far more room.
Verdict
What speed to expect
At 448 GB/s, an 8B Q4 model has a ceiling near 91 tokens per second. Applying the 60 to 80 percent efficiency range seen in published llama.cpp tests gives about 55 to 73 tokens per second for Llama 3.1 8B and None for Gemma 3 12B. We did not find a reliable measured result for this exact card, so these are estimates that could be off by 10 to 20 percent.
Frequently asked questions
Can the RTX 5060 run Llama 3.1 8B?
Yes, at Q4_K_M, with about 7 GB at 8K context.
Is the RTX 5060 faster than the RTX 4060 for AI?
It has 448 vs 272 GB/s of bandwidth, so it should be faster.
Can it run a 14B model?
No, not fully on the GPU: Qwen3 14B needs about 11.4 GB.
Sources and method
- RTX 5060 specs: PNY’s NVIDIA GeForce linecard, January 2026 (8 GB, GDDR7, 448 GB/s, 145 W).
- Linear-attention and sliding-window layers are handled with reduced KV cache; small fixed states in some newer models are not counted.
- Model architecture: each model’s published
config.jsonon Hugging Face (Llama 3.1 8B and Gemma 3 are read from the unsloth mirrors because the original repositories require a login). - VRAM and speed figures are estimates from our calculator, not our own benchmarks. Last updated 3 Nov 2026.
