When you provision a RTX 5060 Ti 16GB on our dedicated GPU hosting, it helps to know which specs actually affect your workload. This is every relevant number plus what it means in practice.
Contents
- Overview table
- Compute – CUDA cores and tensor cores
- Memory – VRAM and bandwidth
- TFLOPS across formats
- Power and thermals
- PCIe
- What it means for your workload
Overview
| Area | Spec | Why It Matters for AI |
|---|---|---|
| Architecture | Blackwell (GB206) | 5th-gen tensor cores, native FP8 |
| VRAM | 16 GB GDDR7 | Decides which models fit |
| Bandwidth | ~448 GB/s | Caps LLM decode throughput |
| Memory bus | 128-bit | Width × speed = bandwidth |
| CUDA cores | ~4,608 | Compute-bound workload speed (SDXL, training) |
| Tensor cores | 5th gen, FP8-native | Matmul acceleration |
| TDP | 180 W | Power cost, cooling envelope |
| PCIe | Gen 5 x8 | Multi-GPU + fast storage |
| NVENC/NVDEC | 9th gen | Video pipeline AI work |
Compute
The 4,608 CUDA cores deliver strong general compute. Combined with 5th-gen tensor cores, theoretical FP16 tensor throughput reaches ~200 TFLOPS. Real AI workloads see 60-70% of theoretical after kernel launch overhead and memory stalls, so expect 120-140 sustained FP16 TFLOPS on typical inference.
Tensor cores handle the bulk of AI matmul. Blackwell’s 5th gen adds native FP8 (both E4M3 and E5M2 variants) and improved 2:4 structured sparsity handling – the hardware is future-ready for formats that are still emerging.
Memory
16 GB at 448 GB/s via GDDR7 on a 128-bit bus. Per-pin speed is ~28 Gbps. Practical sustained bandwidth in production: 380-420 GB/s depending on access pattern.
For LLM decode on a 7B FP16 model (14 GB weights read per token): theoretical ceiling is 448/14 ≈ 32 t/s. Practical 70-80% of that: ~25 t/s. At INT8 (7 GB per token): ~50-65 t/s. At FP8 with native tensor cores: ~95-110 t/s.
TFLOPS Across Formats
| Format | Peak Tensor TFLOPS | Typical Use |
|---|---|---|
| FP32 (dense) | ~25 | Legacy, rarely used for AI |
| BF16 (dense) | ~200 | Training, mixed precision |
| FP16 (dense) | ~200 | Inference without FP8 |
| FP8 (dense) | ~400 | Best default for 2026 inference |
| INT8 (dense) | ~400 | Quantised inference (AWQ/GPTQ) |
| FP8 (sparse 2:4) | ~800 | Future models with sparsity |
Power and Thermals
180 W TDP is moderate. Under sustained LLM load draw is 140-170 W. SDXL pushes closer to 175 W. Idle with persistence mode: ~15-25 W. Thermal throttle point is 85-88°C core, 90°C memory – our chassis configurations keep the card at 65-75°C core under full load.
Multi-card implication: four 5060 Tis draw ~720 W total, fitting a standard 1000 W chassis budget. Same footprint as one-and-a-half 5090s.
PCIe
PCIe Gen 5 at x8 width gives ~32 GB/s per direction – same as Gen 4 x16 on older chassis. Matters for:
- Multi-GPU tensor parallel: all-reduce bandwidth
- Fast storage: Gen 5 NVMe at 13 GB/s feeds the bus directly
- Model loading from disk
For single-card inference with resident weights, PCIe is invisible after load.
What It Means
Translating specs to workload:
- 7-14B LLM serving: sweet spot, production ready at FP8
- SDXL/FLUX image: fast enough for real-time single user, moderate throughput for API
- Whisper: real-time + concurrent streams
- QLoRA fine-tune up to 14B: overnight job
- 20B+ models: look at 5090 or 6000 Pro
Blackwell Specs Delivered
Every spec tuned for mid-tier AI. UK dedicated hosting.
Order the RTX 5060 Ti 16GBSee also: bandwidth analysis, FP8 deep dive, 5th-gen tensor cores, TFLOPS comparison.
