RTX 4090 24GB vs RTX 4060 Ti 16GB: Same Generation, Different Universes

The RTX 4060 Ti 16GB and the RTX 4090 24GB share the Ada Lovelace architecture, the same TSMC 4N node, the same 4th-generation tensor cores with native FP8 (E4M3 + E5M2), the same Transformer Engine path, and the same NVENC AV1 encoder. They run identical CUDA, identical Triton kernels and identical vLLM containers. Architecture is not the variable here — silicon scale is. The 4060 Ti has 4,352 CUDA cores; the 4090 has 16,384. The 4060 Ti has 288 GB/s of bandwidth; the 4090 has 1,008 GB/s. For AI inference on UK GPU hosting, this is the cleanest test case for “how much does scale matter when architecture is identical?” The answer is: a lot.

Contents

• Spec sheet side by side
• 288 vs 1008 GB/s — the bottleneck
• 16GB vs 24GB — what fits where
• Throughput across nine workloads
• Power, price and value
• Per-workload winner table
• vLLM serving examples
• Production gotchas
• Verdict

Spec sheet side by side

Architectural identity, scalar difference. The 4060 Ti is essentially “RTX 4090 / 3.8” with a smaller cache and a brutally narrower memory bus. The 16GB variant of the 4060 Ti is interesting — NVIDIA pasted extra GDDR6 onto the same 128-bit bus, which means capacity grew but bandwidth stayed at 288 GB/s. For any decode-bound LLM, the 4060 Ti’s bandwidth ceiling is the deciding factor. See the RTX 4090 spec breakdown for the full Ada deep-dive.

288 vs 1008 GB/s — the bottleneck

Decode-phase LLM inference is dominated by reading model weights from VRAM into the tensor cores. For Llama 3 8B at FP8, you read roughly 8 GB of weights per token. The 4060 Ti’s 288 GB/s caps theoretical decode at ~36 t/s; in practice, helped by L2 cache and kernel fusion, it sustains ~52 t/s. The 4090’s 1008 GB/s sustains ~198 t/s. The ratio (3.8x) tracks the bandwidth ratio (3.5x) almost exactly, with a small additional advantage from the 4090’s 2.25x larger L2.

For batched inference the gap widens. The 4060 Ti’s narrow 128-bit bus chokes on KV-cache reads from concurrent sequences; aggregate throughput at --max-num-seqs 16 peaks around 240 t/s. The 4090 at the same configuration reaches ~880 t/s. The 4060 Ti is fundamentally a single-stream card; the 4090 is a server card. See GDDR6X bandwidth physics.

16GB vs 24GB — what fits where

Throughput across nine workloads

The 4090 is consistently 3.6-3.8x faster on workloads both can run. This near-perfect tracking with the bandwidth ratio confirms: when architecture is constant, scale rules. See the Llama 3 8B benchmark.

Power, price and value

Despite costing 3.1x as much, the 4090 wins on £/decode-t/s by 19% and on tokens-per-joule by 51%. The 4060 Ti’s only winning metric is £/GB VRAM (capacity matters, but not at 288 GB/s).

Per-workload winner table

vLLM serving examples

# RTX 4090 — Llama 3 8B FP8, 32-way batch, 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 4060 Ti 16GB — same model, half the batch, 8k 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 8 \
  --gpu-memory-utilization 0.90

# 4060 Ti — Qwen 14B AWQ at 8k, the practical ceiling
docker run --rm --gpus all -p 8000:8000 \
  vllm/vllm-openai:latest \
  --model Qwen/Qwen2.5-14B-Instruct-AWQ \
  --quantization awq_marlin --kv-cache-dtype fp8_e4m3 \
  --max-model-len 8192 --max-num-seqs 4

Production gotchas

• 4060 Ti PCIe Gen 4 x8. Half the lanes of the 4090. For multi-card inference or high-bandwidth host transfers (image batches), you will feel it.
• 16GB GDDR6 (not 6X) is slower per pin. 18 Gbps vs the 4090’s 21 Gbps GDDR6X. Combined with the 128-bit bus, the gap is structural.
• FP8 works the same — that’s the trap. The 4060 Ti supports FP8 natively, so vLLM happily runs the same image. Throughput is just 3.7x slower, which surprises teams that expected closer parity from “same generation”.
• Concurrency cliff at b16. Push --max-num-seqs past ~12-16 on the 4060 Ti and aggregate throughput plateaus instead of scaling.
• FLUX.1-dev is a non-starter at FP16. 22 GB peak does not fit; FP8 with text-encoder offload is sluggish.
• QLoRA fine-tuning works but is slow. Llama 8B QLoRA is ~3.7x slower than 4090 — usable, just expect a long evening.
• Driver is identical. No need to special-case the 4060 Ti in your toolchain — both run sm_89.

Verdict

• Pick the RTX 4090 24GB if you serve any non-trivial number of users; need 14B+ models; need long context; need FLUX.1-dev; or value the consistent 3.6-3.8x throughput edge for production.
• Pick the RTX 4060 Ti 16GB if you are a solo developer, hobbyist, or running an internal tool for a tiny team; budget is tight; you do not need fast SDXL or FLUX; and you accept ~50 t/s on Llama 8B as snappy enough.
• Pick neither if you need 70B at FP8 or any 32GB+ workload — go to RTX 5090 32GB.

For a 12-engineer coding team, the 4090 is required. For a solo founder building a Llama 8B demo, the 4060 Ti is defensible.

See also: RTX 4090 spec breakdown, vs RTX 5060 Ti 16GB, FP8 tensor cores on Ada, 2026 tier positioning, tokens-per-watt, vLLM setup, FP8 Llama deployment.