Table of Contents
Specifications Side by Side
The RTX 4060 and RTX 3090 sit at opposite ends of the GPU spectrum for dedicated GPU hosting. One is a budget Ada Lovelace card, the other is Ampere’s former flagship. Understanding their hardware differences clarifies where each card excels for LLM inference.
| Specification | RTX 4060 | RTX 3090 |
|---|---|---|
| Architecture | Ada Lovelace | Ampere |
| VRAM | 8 GB GDDR6 | 24 GB GDDR6X |
| Memory bandwidth | 272 GB/s | 936 GB/s |
| FP16 tensor perf | ~176 TFLOPS | 142 TFLOPS |
| TDP | 115W | 350W |
The 4060 has newer tensor cores but dramatically less memory bandwidth and VRAM. For detailed GPU analysis, browse our GPU comparisons section.
LLM Throughput Benchmarks
We tested inference performance using vLLM and Ollama with models that fit the 4060’s 8 GB constraint.
| Model | Quantisation | RTX 4060 (tok/s) | RTX 3090 (tok/s) | 3090 Speedup |
|---|---|---|---|---|
| Llama 3 8B | GPTQ 4-bit | 28 | 92 | 3.29x |
| Mistral 7B | AWQ 4-bit | 31 | 98 | 3.16x |
| Phi-3 3.8B | FP16 | 52 | 135 | 2.60x |
| Llama 3 13B | GPTQ 4-bit | N/A (VRAM) | 58 | — |
The 3090 is 2.6-3.3x faster across all testable models, driven mainly by its 3.4x higher memory bandwidth. The 4060 cannot load any model above 7-8B at 4-bit quantisation. Check live numbers on the tokens per second benchmark.
Cost Efficiency Comparison
The RTX 4060 costs significantly less per month, but does that translate to better throughput per dollar?
| Metric | RTX 4060 | RTX 3090 |
|---|---|---|
| Approx. monthly cost | ~$65/mo | ~$140/mo |
| Llama 3 8B tok/s | 28 | 92 |
| tok/s per $/mo | 0.431 | 0.657 |
| Cost per 1M tokens | $0.089 | $0.058 |
The RTX 3090 delivers 52% more throughput per dollar for LLM inference. The memory bandwidth bottleneck on the 4060 prevents it from competing on efficiency. Model your costs with the cost per million tokens calculator.
VRAM Limitations: 8 GB vs 24 GB
The 4060’s 8 GB VRAM is its biggest constraint for LLM hosting. After loading a 7B 4-bit model (~4 GB weights), only ~3.5 GB remains for KV cache. This limits concurrent request handling and maximum context length severely.
The RTX 3090 loads the same 7B model and retains ~19 GB for KV cache, supporting far higher batch sizes and longer contexts. For memory planning, see our vLLM memory optimisation guide. The 3090 also handles 13B models comfortably, a class entirely out of reach for the 4060.
Which Workloads Suit Each Card?
RTX 4060 fits: Development and testing, lightweight 3-4B models in production, single-user inference, and prototyping before scaling. It is an affordable entry point for teams exploring self-hosted LLMs.
RTX 3090 fits: Production inference at scale, 7-13B models, multi-user serving, and any workload where throughput per dollar matters. For batch size tuning, read our batch size impact on tokens/sec analysis.
If you are deciding between these two for production use, the 3090 is almost always the better investment. Compare against cloud APIs with the GPU vs API cost comparison tool.
Final Verdict
The RTX 4060 is a capable development GPU, but it cannot compete with the RTX 3090 on throughput per dollar for LLM inference. The 3090’s 3x memory bandwidth and 3x VRAM make it the clear winner for any production workload.
Use the 4060 for prototyping and testing. Deploy on the 3090 for production. For the largest models, explore multi-GPU clusters to scale beyond single-GPU limits.
Start with the Right GPU for Production
Deploy RTX 4060 or RTX 3090 dedicated servers from GigaGPU. UK data centres, bare-metal performance, instant setup.
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