NVIDIA RTX 5060 Ti 16 GB Hosting — The Recommended Starter
The cheapest serious 16 GB Blackwell card we host. Hardware FP8 and FP4, the same tensor architecture as a 5080 or 5090, and the canonical entry point to the GigaGPU catalogue. The card we recommend when you’re moving from “running on a laptop” to “running in production”.
RTX 5060 Ti 16 GB Server Specs
The hardware you actually rent.
| GPU model | NVIDIA GeForce RTX 5060 Ti 16 GB (Blackwell, GB206) |
|---|---|
| Architecture | Blackwell — 5th gen Tensor Cores |
| VRAM | 16 GB GDDR7 @ 448 GB/s |
| CUDA cores | 4,608 |
| FP16 compute | ~ 24 TFLOPS |
| FP8 / FP4 | ~ 190 / ~ 380 TOPS |
| TDP | 180 W |
| Host CPU | AMD Ryzen 7 / 9 |
| Host RAM | Up to 64 GB DDR5 |
| Storage | 1 TB NVMe + 4 TB SATA SSD |
| Network | 1 Gbps unmetered |
| Location | London, United Kingdom |
What Fits on a Single RTX 5060 Ti 16 GB
16 GB is the smallest VRAM we’d recommend for production LLM serving — and the 5060 Ti is the cheapest way to get there. Comfortable for 7B–8B at FP8 with long context, with hardware-accelerated FP8/FP4 paths that the older 4060 simply doesn’t have.
| Model | Params | FP16 | INT4 / FP8 | Notes |
|---|---|---|---|---|
| Mistral 7B Instruct | 7B | 14 GB FP16 | 5 GB FP8 | FP16 fits 8K context; FP8 has plenty of headroom |
| Llama 3.1 8B | 8B | 16 GB FP16 | 5 GB FP8 | FP16 tight — comfortable at FP8 with long context |
| Qwen 2.5 7B | 7B | 14 GB FP16 | 5 GB FP8 | FP16 fits 8K context; FP8 plenty of headroom |
| Phi-3 Mini | 3.8B | 8 GB FP16 | 2.5 GB INT4 | Plenty of headroom for 128K context |
| Gemma 2 9B | 9B | 18 GB FP16 | 6 GB FP8 | FP8 only — FP16 won’t fit |
| Qwen 2.5 14B | 14B | 28 GB FP16 | 9 GB AWQ-INT4 | AWQ-INT4 only — tight KV budget |
| DeepSeek-Coder 6.7B | 6.7B | 13 GB FP16 | 4.5 GB FP8 | FP8 fits with embeddings alongside |
| Whisper Large-v3 | 1.5B | 6 GB | n/a | Real-time + room for an 8B LLM |
| SDXL 1.0 | 3.5B | 8 GB FP16 | 4 GB FP8 | FP8 fits with basic ControlNet |
| FLUX.1 schnell | 12B | 24 GB FP16 | 12 GB FP8 | FP8 only — FP16 won’t fit |
| FLUX.1 dev | 12B | 24 GB FP16 | 12 GB FP8 | FP8 only, tight |
When the RTX 5060 Ti Is the Right Card
The workloads we see most often on the entry-tier Blackwell.
The recommended starter
The cheapest production-grade 16 GB host in our catalogue, and the canonical entry point if you’re new to self-hosting. Same Blackwell tensor cores as a 5080, ~70% the throughput at 63% the price.
7B-class chatbots in FP8
Mistral 7B, Qwen 2.5 7B and Llama 3.1 8B all run comfortably in FP8 with long context. The best cost-per-token ratio in the catalogue for production 7B serving.
Coding assistants
DeepSeek-Coder 6.7B in FP8 fits with embeddings alongside, comfortably serving a team of around 10 developers. A solid private alternative to copilots.
Embeddings + reranker
BGE-large plus a reranker leaves plenty of throughput for a 7B LLM on the same card. The right shape for a single-box RAG pilot.
ComfyUI / SDXL hobbyist API
FP8 SDXL pipelines run cleanly and ComfyUI workflows are responsive enough for a hobbyist or small-team API. Not the fastest card we host, but the cheapest one that’s actually pleasant to use.
Voice agent backend
Whisper Large-v3 + a 7B FP8 LLM + a small TTS all share the card. Roughly 6 concurrent voice sessions per server — enough to validate a voice product before scaling up.
Internal demos & staging
An always-on staging environment that’s not embarrassing to demo. The economics work out at £119/mo for an environment your team would otherwise share with production.
RTX 5060 Ti vs the Rest of the Catalogue
How the entry-tier Blackwell stacks up against the cards buyers most often compare it against.
| GPU | VRAM | Throughput / Notes | Where it sits | Price |
|---|---|---|---|---|
| RTX 5060 | 8 GB GDDR6 | Ada — no hardware FP8 | Hobbyist tier; half the VRAM | from £99 |
| RTX 5060 Ti 16 GB | 16 GB GDDR7 | ~70 tok/s (Mistral 7B FP8 single-stream) | Entry production tier | from £119 |
| RTX 3090 | 24 GB GDDR6X | Similar throughput; 50% more VRAM | Pick when you need the bigger model to fit | from £159 |
| RTX 5080 | 16 GB GDDR7 | ~95 tok/s — about 40% faster, same VRAM | Pick when latency matters more than price | from £189 |
| RTX 5090 | 32 GB GDDR7 | ~2× throughput, 2× VRAM | The serious-workload Blackwell | from £399 |
Deep Dive
Why we call this the canonical starter
If you’re moving a workload off a laptop or a single desktop GPU into hosted production for the first time, the question isn’t really “which card is fastest”. It’s “which card is the cheapest one that won’t immediately become the limiting factor”. The 5060 Ti 16 GB is that card. 16 GB is the smallest VRAM where production 7B–8B serving stops being a constant memory-juggling exercise. Hardware FP8 and FP4 mean you’re not stuck on the slow lane the way an older 4060 or 3060 would be. And £119/mo is well inside the budget where teams stop having a meeting about whether to provision the box.
It’s the card we point new customers at by default. If you outgrow it, the upgrade path to a 5080 or 5090 is straightforward — same Blackwell architecture, same tooling, same FP8/FP4 paths.
The 5060 Ti vs 4060 question
The 4060 at £99 looks like a bargain on the catalogue page. It almost never is for actual AI work. 8 GB of VRAM forces you into INT4 quantisation for anything 7B-class, and even then the KV cache budget for long context is painful. The 5060 Ti gives you twice the VRAM, hardware FP8 (which the 4060 lacks entirely), and meaningfully higher bandwidth — for an extra £20/mo. If you’re picking between them for inference work, the 5060 Ti is almost always the right answer.
The 5060 Ti vs 3090 question
This is the more interesting comparison. The 3090 at £159 has 24 GB of VRAM — 50% more than the 5060 Ti — and hits broadly similar throughput on 7B-class FP8 workloads in real terms. So which one wins depends on what you’re optimising for:
- If your bottleneck is “the model I want to run is 14B–22B and I need it to fit” — the 3090’s 24 GB wins. The 5060 Ti will force you into AWQ-INT4 with a tight KV budget.
- If your bottleneck is “I want fast FP8 inference on 7B–8B models at the lowest possible price” — the 5060 Ti wins. You’re paying £40 less for the modern FP8/FP4 path.
Both are valid answers. We host both for a reason.
FP8 / FP4 paths matter on a 16 GB card
The 5060 Ti has the same Blackwell tensor cores as the 5080 and 5090. Hardware FP8 and FP4 aren’t just a performance feature on a 16 GB card — they’re a memory-saver:
- Llama 3 8B at FP16 → 16 GB. Tight enough to be uncomfortable.
- Llama 3 8B at FP8 → 8 GB. Comfortable, with headroom for KV cache and a long context window.
- Llama 3 8B at FP4 (NVFP4) → 4–5 GB. You can run a second model alongside.
Most production deployments on a 5060 Ti land at FP8. It’s the best balance of quality, memory, and speed — and it’s the precision the rest of the Blackwell line is also moving toward, so anything you build here ports cleanly upmarket.
Frequently Asked Questions
The questions buyers actually ask before committing to a GPU server.
Is the 5060 Ti fast enough for a customer-facing chatbot?
For a 7B–8B model in FP8 serving up to a few hundred users with sensible queueing — yes. Single-stream Mistral 7B FP8 hits roughly 70 tok/s, which feels responsive in a chat UI. If you need sub-100ms first-token latency or want to serve dozens of concurrent power-users, step up to a 5080.
Can I run Llama 3.1 8B at full FP16?
Just barely with short context. Most teams run it at FP8 (about 8 GB peak weights + 4–6 GB KV cache) which fits comfortably with a 32K context window and leaves room for an embedding model or Whisper alongside.
Why is this card cheaper than the 4060 actually a savings?
It isn’t cheaper — the 4060 is £99, the 5060 Ti is £119. But the 5060 Ti has 2× the VRAM and hardware FP8/FP4 the 4060 doesn’t have. For AI inference the £20 difference is the best £20 in our catalogue.
Can I fine-tune on a 5060 Ti?
QLoRA on 7B models works comfortably. Anything heavier — full SFT, larger models — go to a 5090 or 6000 PRO instead.
5060 Ti or 3090 for my workload?
If you need to fit a 13B–22B model: 3090 (24 GB wins). If you want fast FP8 inference on 7B–8B at the lowest possible price: 5060 Ti. See the deep-dive section above for a longer take.
5060 Ti or 5080?
Same VRAM, same architecture. The 5080 is roughly 40% faster on real workloads and costs £189 vs £119. If your budget is tight or your traffic is light, start on a 5060 Ti — the upgrade path to a 5080 is a config change, not a re-architecture.
Will FLUX run on a 5060 Ti?
Power draw at 100% load?
180 W. The lowest-power card in our Blackwell line — easy to cool and friendly on PSU headroom for multi-GPU configurations.
Same-day deployment?
Yes for in-stock SKUs. The 5060 Ti is one of the most reliably-stocked cards in our catalogue — typical lead time when not in stock is 1–2 working days.
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Starting out with self-hosted AI? The 5060 Ti is your card.
16 GB GDDR7, hardware FP8 and FP4, and the cheapest serious Blackwell card in our catalogue. From £119/mo with same-day deployment.
