RTX 4090 24GB vs RTX 5060 Ti 16GB: Flagship Ada vs Entry Blackwell

The RTX 5060 Ti 16GB is the cheapest Blackwell card with enough VRAM to be taken seriously for AI inference. At roughly £450-500 in the UK in 2026, it is a fraction of the RTX 4090 24GB‘s £1,300 secondhand price. But cheap silicon does not change the laws of memory bandwidth: the 5060 Ti has a 128-bit bus and 448 GB/s of GDDR7. The 4090 has 1008 GB/s. For LLM decode — a memory-bandwidth-dominated workload — that gap is decisive. This post benchmarks both cards across nine real workloads on UK GPU hosting and explains exactly when the cheaper card is the rational pick.

Contents

• Spec sheet side by side
• Bandwidth physics — why 128-bit hurts
• 16GB vs 24GB — the model-fit question
• Throughput across nine workloads
• Power, price and tokens-per-pound
• Per-workload winner table
• vLLM serving examples
• Production gotchas
• Verdict

Spec sheet side by side

The 4090 is, in every dimension that matters for AI inference, more than twice the card. It has 3.6x the SMs, 2.25x the memory bandwidth, 2.9x the FP8 throughput, 2.25x the L2 cache and 50% more VRAM. The 5060 Ti’s only architectural advantage is FP4 support, and that helps only on models small enough to benefit from 4-bit weights — a category where you usually want the cheapest card anyway.

Bandwidth physics — why 128-bit hurts

Decode-phase LLM inference is memory-bandwidth-bound. For each token generated, the kernel reads the entire weight tensor of every layer through the matmul units. A 7B FP16 model is 14 GB; a single decode token requires reading roughly that much from VRAM (minus what L2 caches). On the 4090, 1008 GB/s gives a theoretical ceiling around 72 t/s for that workload before tensor-core latency or scheduling overhead reduces it. In practice with FP8 weights (halving memory traffic to 7 GB/token) the 4090 sustains ~198 t/s. The 5060 Ti at 448 GB/s caps far lower — about 32 t/s ceiling on FP8 7B, observed ~112 t/s in practice with kernel-fusion benefits. That maps directly to user experience: 198 t/s feels instantaneous; 112 t/s is still snappy; 30 t/s is sluggish for a coding assistant. See GDDR6X bandwidth for the full physics.

16GB vs 24GB — the model-fit question

The 5060 Ti is fundamentally a 7-9B model card with comfortable headroom. Anything 14B and above starts to squeeze; anything 30B and above does not fit. See 8B LLM VRAM requirements and Llama 70B VRAM requirements.

Throughput across nine workloads

For workloads both cards run, the 4090 is a consistent 1.7-2.1x faster. For workloads only the 4090 runs, the comparison is moot. Pair this with the 5060 Ti Llama 8B benchmark and the 4090 Llama 8B benchmark for the full data.

Power, price and tokens-per-pound

On every economic metric the 5060 Ti wins decisively — for workloads that fit in 16GB. £/decode-t/s is 35% better. £/GB-VRAM is 44% better. Annual electricity is 57% lower. This is why the 5060 Ti is a serious contender for solo developers, hobbyists, and any workload that genuinely lives in 8-14B model territory. See the monthly hosting cost calculation.

Per-workload winner table

vLLM serving examples

# RTX 4090 — Llama 3 8B FP8, 32-way batching, 16k context
docker run --rm --gpus all -p 8000:8000 \
  vllm/vllm-openai:latest \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --quantization fp8 --kv-cache-dtype fp8_e4m3 \
  --max-model-len 16384 --max-num-seqs 32 \
  --gpu-memory-utilization 0.92

# RTX 5060 Ti — same model, halve the batching, smaller context
docker run --rm --gpus all -p 8000:8000 \
  vllm/vllm-openai:latest \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --quantization fp8 --kv-cache-dtype fp8_e4m3 \
  --max-model-len 8192 --max-num-seqs 12 \
  --gpu-memory-utilization 0.90

# 5060 Ti only — Llama 8B in MX-FP4, recovers some throughput
docker run --rm --gpus all -p 8000:8000 \
  vllm/vllm-openai:latest \
  --model neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16 \
  --quantization fp4 --kv-cache-dtype fp8_e4m3 \
  --max-model-len 16384 --max-num-seqs 16

Production gotchas

• 5060 Ti PCIe Gen 5 x8 is fine for inference but a bottleneck for multi-card. If you ever scale to two cards with NCCL, the x8 link halves all-reduce throughput.
• 16GB cannot hold a 14B at long context. Qwen 14B AWQ at 16k context will OOM on the 5060 Ti. Cap at 8k or accept the OOM in production.
• 5060 Ti aggregate batching is brutal. The 128-bit bus chokes once you push --max-num-seqs above 12-16. The 4090 handles 32-64 comfortably for 8B models.
• FP4 quality risk. The 5060 Ti’s most distinctive capability is FP4. Validate quality on your eval suite — Qwen Coder loses 1-2 HumanEval points; Llama Instruct holds.
• SDXL Refiner cache trick. On 16GB you cannot keep SDXL base + refiner + VAE all on-card; you must offload one. Pipeline latency suffers.
• 4090 12VHPWR caveat applies. Older chassis won’t power a 4090 properly; the 5060 Ti drops into anything with a single 8-pin.
• Driver maturity. The 4090 has years of vLLM, Triton and FlashInfer tuning. The 5060 Ti’s kernels are newer and occasionally rough around the edges.

Verdict

• Pick the RTX 4090 24GB if you serve more than a handful of users; need 14B+ models; need long context (32k+); need FLUX.1-dev FP16; or value the 1.7-2.1x throughput edge for production.
• Pick the RTX 5060 Ti 16GB if you are a solo developer, a hobbyist, or a startup MVP at fewer than 10 concurrent users on an 8B model; you want the lowest electricity bill; or you are bound by capex under £600.
• Pick neither if you need 70B INT4 — go to RTX 5090 32GB.

For a 200-MAU SaaS, the 4090 is the right answer. For a solo founder building a Llama 8B chatbot demo, the 5060 Ti is the right answer. For a 12-engineer team running Qwen Coder 32B, only the 4090 fits.

See also: vs RTX 5080 16GB, 4090 or 5060 Ti decision, 5060 Ti vs 3090 benchmark, RTX 4090 spec breakdown, 2026 tier positioning, hybrid 4090 + 5060 Ti, tokens-per-watt.