The RTX 5060 Ti 16GB is the cheapest Blackwell card in the GigaGPU lineup and the RTX 4090 24GB is the upper end of the consumer-class range. Picking between them is genuinely about scale: how many concurrent users, how big a model, how strict your latency targets, how much you care about watts-per-token versus raw throughput. The 5060 Ti wins on £/token at low scale and on per-watt efficiency in absolute terms; the 4090 wins on raw throughput, model menu, and concurrency. This guide walks through the decision with hard numbers and a 10-workload winner table, anchored to dedicated 4090 hosting and the broader UK GPU range.
Contents
- Spec sheet
- Throughput gap
- Model fit and the 8GB difference
- Concurrency math
- Cost-per-token and watts-per-token
- Per-workload winner (10 workloads)
- Production gotchas
- Verdict and when each card wins
Spec sheet
| Spec | RTX 4090 24GB | RTX 5060 Ti 16GB |
|---|---|---|
| Architecture | Ada AD102 | Blackwell GB206 |
| CUDA cores | 16,384 | 4,608 |
| Tensor cores | 512 (4th gen) | 144 (5th gen) |
| VRAM | 24GB GDDR6X | 16GB GDDR7 |
| Bandwidth | 1,008 GB/s | 448 GB/s |
| TDP | 450W | 180W |
| FP8 generation | 4th gen | 5th gen |
| FP4 native | No | Yes (limited) |
| PCIe | Gen4 x16 | Gen5 x8 |
| FP16 TFLOPS dense | 165 | ~60 |
| Launch year | 2022 | 2025 |
| Approx UK dedicated £/mo | £550 | £160 |
Throughput gap
The 4090 has 3.55x the CUDA cores and 2.25x the memory bandwidth. Real-world LLM inference is bandwidth-bound, so the throughput gap lands somewhere between those two ratios – typically 1.7-2.3x for chat workloads and 2-2.5x for image generation. Below are sustained vLLM measurements with continuous batching.
| Workload | 4090 t/s | 5060 Ti t/s | 4090 advantage |
|---|---|---|---|
| Llama 3.1 8B FP8 batch 1 | 198 | 112 | 1.77x |
| Llama 3.1 8B FP8 concurrency 8 | ~1,100 aggr | ~480 aggr | 2.29x |
| Llama 3.1 8B FP8 concurrency 32 | ~1,800 aggr | ~720 aggr | 2.50x |
| Llama 3.1 70B AWQ INT4 | 22 | OOM | n/a |
| Qwen 2.5 14B FP8 | 120 | 62 | 1.94x |
| Mistral 7B FP8 batch 1 | 220 | 130 | 1.69x |
| SDXL 1024×1024, 30 steps | 3.4s | 7.8s | 2.29x |
| Flux.1 Dev 1024×1024 | 14s | ~32s | 2.29x |
| Whisper Large v3, 1hr audio | 22s | 48s | 2.18x |
Model fit and the 8GB difference
The 5060 Ti’s 16GB rules out everything 70B-class. It is fine for 8B and OK for 14B at low concurrency. Mixtral 8x7B AWQ INT4 (~25GB) is impossible. Llama 70B AWQ INT4 (~17GB weights alone) is out. The 4090’s 24GB makes all of those feasible.
| Model | 4090 24GB | 5060 Ti 16GB |
|---|---|---|
| Llama 8B FP8 (4k context) | 16GB free for KV | 8GB free for KV |
| Llama 8B FP8 (32k context) | Comfortable | Tight, KV pressure |
| Qwen 14B FP8 | Fits with KV | Tight |
| Llama 70B AWQ INT4 | Fits | OOM |
| Mixtral 8x7B AWQ | ~25GB tight | OOM |
| SDXL + refiner | Fast, fits | Fits, slower |
| Flux.1 Dev | Offload required | OOM without aggressive offload |
| Whisper Large v3 | Fits cleanly | Fits, slower batch |
Concurrency math
The single most useful question to ask: how many concurrent chat sessions do you actually need to serve? Below ~5 concurrent users on Llama 8B FP8, the 5060 Ti can keep up. Above that, the 4090 separates from the pack. At 32 concurrent users the 5060 Ti saturates and queue length grows; the 4090 is still healthy.
| Concurrent chat users | 4090 8B FP8 TTFT / aggr t/s | 5060 Ti 8B FP8 TTFT / aggr t/s |
|---|---|---|
| 1 | 250ms / 198 t/s | 320ms / 112 t/s |
| 4 | 320ms / ~700 t/s | 500ms / ~340 t/s |
| 8 | 450ms / ~1,100 t/s | 900ms / ~480 t/s |
| 16 | 700ms / ~1,500 t/s | 1,800ms / ~620 t/s |
| 32 | 1,200ms / ~1,800 t/s | Saturated, queue grows |
| 64 | 2,000ms / ~2,000 t/s | Heavily queued |
Cost-per-token and watts-per-token
Assume £550/month for a 4090 and £160/month for a 5060 Ti. The 5060 Ti is roughly 3.4x cheaper but only delivers 1.7-2.3x less throughput – so per-token, the 5060 Ti is cheaper for any workload it can actually run. Per-watt the 5060 Ti is dramatically more efficient (180W vs 450W TDP).
| Workload | 4090 £/M tok | 5060 Ti £/M tok | 4090 W/Mtok | 5060 Ti W/Mtok | Cheaper |
|---|---|---|---|---|---|
| Llama 8B FP8 24/7 conc 8 | £0.039 | £0.018 | 0.061 | 0.038 | 5060 Ti |
| Qwen 14B FP8 24/7 conc 8 | £0.063 | £0.034 | 0.10 | 0.07 | 5060 Ti |
| Mistral 7B FP8 24/7 | £0.034 | £0.016 | 0.054 | 0.034 | 5060 Ti |
| SDXL £/image | £0.0009 | £0.0006 | 0.0014 | 0.0011 | 5060 Ti |
| Flux.1 Dev £/image | £0.0036 | £0.0024 | 0.0056 | 0.0042 | 5060 Ti |
| Llama 70B INT4 | £0.34 | n/a | 0.66 | n/a | 4090 only option |
Per-workload winner (10 workloads)
| Workload | 4090 wins | 5060 Ti wins | Why |
|---|---|---|---|
| Llama 8B FP8 chat under 5 conc users | No | Yes | Cheaper £/token, watts-bound |
| Llama 8B FP8 chat 30+ conc users | Yes | No | 5060 Ti saturates |
| Llama 70B AWQ INT4 | Yes | No | 5060 Ti OOM |
| Mixtral 8x7B AWQ | Yes | No | 5060 Ti OOM |
| Qwen 14B FP8 high conc | Yes | No | KV pressure on 5060 Ti |
| SDXL low-volume image gen | No | Yes | Cheaper £/image at low volume |
| SDXL 24/7 high-volume queue | Yes | No | 4090 2.3x faster, lower latency |
| Whisper batch transcription | Marginal | Yes | 5060 Ti cheaper if SLA tolerates |
| Sub-300ms TTFT chat at scale | Yes | No | 5060 Ti saturates above conc 8 |
| Mixed inference (LLM + image + audio) | Yes | No | 4090 VRAM and throughput |
Production gotchas
- 5060 Ti’s PCIe Gen5 x8 is functionally Gen4 x16. Hosts that only expose Gen4 x8 will halve effective lane bandwidth. Confirm motherboard and chassis topology.
- 16GB ceiling cuts off 70B in any quantisation. Plan for the largest model in your 18-month roadmap. Outgrowing 5060 Ti midway is a migration cost.
- Concurrency saturation hits faster than throughput math suggests. Aggregate t/s plateaus around 720 t/s on 8B FP8 at conc 32. After that, queue length grows and tail latency explodes.
- 180W TDP fits anywhere. The 5060 Ti runs in any chassis with PCIe power, no special cooling. The 4090 needs proper airflow. Affects deployment density.
- Flux.1 Dev needs aggressive CPU offload on 5060 Ti. Per-image latency rises 100%+ over a 4090. For high-volume image queues this matters.
- Multi-card 5060 Ti can match a 4090 on chat workloads. Three 5060 Tis (~£480/mo) at conc 8 deliver ~1,440 t/s aggregate vs 4090’s ~1,100 t/s – but at the cost of operational complexity.
- FP4 immaturity. The 5060 Ti has limited FP4 silicon and tooling support is thinner than 5080/5090. Do not rely on FP4 throughput.
Verdict and when each card wins
The 4090 wins decisively when (a) you need 70B-class models or Mixtral, (b) you need to handle more than 8 concurrent chat sessions with sub-500ms TTFT, (c) you want one card to cover both LLM and heavy image gen, or (d) your roadmap likely outgrows 16GB within 18 months. The 5060 Ti wins on per-token cost and per-watt efficiency for any 8B-class workload below 8 concurrent users, for low-volume image generation, and for cost-bound research labs. Many teams start on a 5060 Ti and graduate to a 4090 when traffic justifies it – the migration is straightforward as both cards share the same FP8 toolchain. Order via GigaGPU dedicated hosting.
The headroom you need at scale
16,384 CUDA cores, 24GB VRAM, ready for 30+ concurrent users on Llama 8B FP8 with sub-500ms TTFT. UK dedicated hosting.
Order the RTX 4090 24GBSee also: 4090 vs 5060 Ti deep-dive, hybrid 4090 + 5060 Ti pairing, spec breakdown, 8B benchmark, 5060 Ti vs 3090, or 3090 decision, or 5080 decision, or 5090 decision, FP8 tensor cores, tier positioning 2026, tokens per watt, power draw efficiency, concurrent users, for SaaS RAG, for multi-tenant SaaS, 5060 Ti when to upgrade, 70B INT4 VRAM.
