Most buyers landing on our dedicated GPU hosting assume the newer Blackwell card automatically wins. For LLM inference that assumption breaks down fast. The RTX 4060 Ti 16GB ships with twice the VRAM of the RTX 5060, and memory capacity – not architecture year – is what decides which models you can actually load.
What You’ll See Here
- Spec sheet, side by side
- Why the VRAM gap flips the decision
- Throughput differences when both cards fit a model
- Which LLMs fit each card
- The verdict for small, mid, and edge workloads
Specifications Side by Side
| Spec | RTX 4060 Ti 16GB | RTX 5060 Blackwell 8GB |
|---|---|---|
| VRAM | 16 GB GDDR6 | 8 GB GDDR7 |
| Memory bandwidth | 288 GB/s | ~448 GB/s |
| Architecture | Ada Lovelace | Blackwell |
| FP16 tensor throughput | ~353 TFLOPS | Higher per-clock, lower on paper |
| FP8 support | No (Ada) | Yes |
| Power | 165 W | 150 W |
The 5060 brings GDDR7 and FP8 tensor cores. The 4060 Ti brings raw memory capacity. For inference, one wins the per-token race, the other wins “which model can I host at all.”
Why VRAM Capacity Dominates
An 8B parameter LLM at FP16 needs roughly 16 GB just for weights. Add KV cache, activations, and serving headroom and the 5060 cannot hold any 8B model at full precision. You have to quantise. At INT4 the model fits in around 5 GB, leaving 3 GB for everything else – workable but tight. The 4060 Ti hosts the same model comfortably at INT8 or even FP16 with short contexts. See our 8B LLM VRAM requirements piece for the precise numbers.
Throughput When Both Fit
For the models both cards can load – think 7B at INT4 or smaller – the 5060 pulls ahead because GDDR7 bandwidth matters for decode-bound inference. Our Mistral 7B tokens/sec studies show bandwidth-limited decode scaling almost linearly with memory speed. On smaller quantised models the 5060 runs roughly 15-25% faster per token. If your workload is “Phi-3-mini, many parallel users,” Blackwell wins. If it is “Llama 3 8B serving one chat at a time,” the 4060 Ti still competes at INT8.
Pick the GPU That Fits Your Model First
Fixed UK pricing, full root access, no cloud pricing games. We’ll provision either GPU on the same day.
Browse GPU ServersModel Fit Table
| Model | RTX 4060 Ti 16GB | RTX 5060 8GB |
|---|---|---|
| Phi-3-mini 3.8B | FP16 easy | FP16 tight, INT8 comfortable |
| Mistral 7B | FP16 short context, INT8 production | INT4 only |
| Llama 3 8B | INT8 with real headroom | INT4, reduced context |
| Gemma 2 9B | INT8 tight, INT4 comfortable | INT4 only, short context |
| Qwen 2.5 14B | INT4 fits | Does not fit |
If you are deciding between tiers more broadly, our best GPU for LLM inference guide walks every card side by side.
Which One Should You Book?
Choose the 4060 Ti 16GB when the model matters more than the speed – you want to host real 7-13B models without aggressive quantisation. Choose the 5060 Blackwell when you are serving very small quantised models to many parallel users and every token of latency matters. For mixed workloads, the 4060 Ti is the safer default in 2026 because the VRAM ceiling is what kills more projects than per-token speed ever does.
If you are at the top of the budget tier, also read RTX 3090 vs 4060 Ti value per VRAM – the 3090 often beats both cards on cost per GB if you can accept its older silicon.
