The RTX 5060 8GB and the RTX 5060 Ti 16GB share Blackwell architecture and family name. The 16GB VRAM on the Ti is the headline difference, with smaller compute gaps alongside. On dedicated GPU hosting the choice almost always comes down to model size – but the details matter.
Contents
Specs Side by Side
| Spec | 5060 8GB | 5060 Ti 16GB |
|---|---|---|
| Architecture | Blackwell | Blackwell |
| VRAM | 8 GB GDDR7 | 16 GB GDDR7 |
| Memory bandwidth | ~448 GB/s | ~448 GB/s |
| Memory bus | 128-bit | 128-bit |
| CUDA cores | ~3,840 | ~4,608 |
| FP8 tensor | Yes | Yes |
| TDP | 150 W | 180 W |
| PCIe | Gen 5 x8 | Gen 5 x8 |
Same architecture, same bandwidth (both GDDR7 on 128-bit), same FP8 support. The Ti has 20% more CUDA cores and double the VRAM.
VRAM Decides
8 GB is a hard ceiling. It hosts:
- Phi-3-mini (3.8B) at FP16 comfortably
- SDXL with aggressive memory optimisation
- Quantised 7B LLMs at INT4 (tight)
- Small embedder or reranker
- Whisper any size
It does NOT fit:
- Llama 3 8B at FP16 (needs 16 GB)
- Mistral 7B at FP16 (14 GB)
- Qwen 2.5 14B at any useful precision
- Multiple concurrent users on any 7B model
- Full RAG stack (LLM + embedder + reranker together)
16 GB unlocks all of those.
Compute Delta
Per-token decode speed is similar because bandwidth is identical. For models both cards can host, the 5060 Ti is 15-20% faster due to higher CUDA core count, but this is modest.
The real speed advantage is that the Ti lets you avoid aggressive quantisation. Running Llama 3 8B FP16 on the Ti versus INT4 on the base 5060 yields better output quality at similar tokens/sec – so you get quality headroom, not just speed.
Workload Fit
| Workload | 5060 8GB | 5060 Ti 16GB |
|---|---|---|
| Phi-3-mini FP16 | ~115 t/s | ~135 t/s |
| Mistral 7B INT4 | ~70 t/s | ~95 t/s |
| Mistral 7B FP16 | Does not fit | Fits, ~65 t/s |
| Mistral 7B FP8 | Tight (no KV headroom) | Comfortable, ~110 t/s |
| Qwen 14B AWQ | Does not fit | ~44 t/s |
| SDXL Lightning | ~1.3 s (tight VRAM) | ~0.95 s (comfortable) |
Pick Rule
Pick the 5060 8GB when:
- Your workload is Phi-3-mini class or smaller
- You run a single embedder or reranker, nothing else
- Budget is the absolute constraint
- You are experimenting with quantised models
Pick the 5060 Ti 16GB when:
- You want to run any 7-8B model at FP16 or FP8 natively
- You want 13-14B models at INT8 or AWQ
- You need production KV cache capacity for concurrent users
- You run multiple co-resident models (RAG stack)
- You want room to upgrade models later without new hardware
For most production AI workloads, the Ti upgrade pays for itself by avoiding tight quantisation and enabling real concurrency. The base 5060 is sensible for personal experimentation or tiny workloads; anything you want to run in production lands on the Ti.
16GB Blackwell for Production
The VRAM headroom that makes production LLM workloads comfortable.
Order the RTX 5060 Ti 16GBSee also: 5060 Ti introduction, 5060 vs 5060 Ti benchmarks, VRAM choice in the Blackwell family.
