Table of Contents
Two-Generation Jump: 4060 to 5080
Upgrading from the RTX 4060 to the RTX 5080 is a two-generation leap: Ada Lovelace to Blackwell, GDDR6 to GDDR7, and 8GB to 16GB VRAM. On a dedicated GPU server, this upgrade doubles your VRAM, triples your memory bandwidth, and adds native FP4 inference. It transforms a budget inference box into a serious production GPU.
This guide quantifies every improvement so you can decide whether the RTX 5080 or the alternative RTX 3090 upgrade path makes more sense for your workload. For overall GPU rankings, see our best GPU for LLM inference guide.
Specification Comparison
| Specification | RTX 4060 | RTX 5080 | Improvement |
|---|---|---|---|
| VRAM | 8 GB GDDR6 | 16 GB GDDR7 | 2x capacity |
| Bandwidth | 272 GB/s | ~960 GB/s | 3.5x faster |
| Architecture | Ada Lovelace | Blackwell | 2 gens newer |
| FP4 Tensor | No | Yes | New capability |
| Power | 115W | ~250W | +135W |
| 8B FP16 | No (OOM) | Yes | Now possible |
The 3.5x bandwidth improvement is the headline upgrade. For token generation, this directly means 3x+ faster output for the same model. The VRAM doubling opens FP16 and larger quantised models.
Before and After Performance
| Workload | RTX 4060 | RTX 5080 | Improvement |
|---|---|---|---|
| Llama 3 8B Q4 (tok/s) | ~42 | ~135 | 3.2x |
| Mistral 7B Q4 (tok/s) | ~45 | ~140 | 3.1x |
| Llama 3 8B FP16 (tok/s) | OOM | ~88 | Now possible |
| Llama 3 8B FP4 (tok/s) | N/A | ~148 | 5080 exclusive |
| DeepSeek R1 7B Q4 (tok/s) | ~40 | ~130 | 3.3x |
| SDXL 1024×1024 | ~12s | ~4s | 3x faster |
| Whisper Large v3 (RTF) | ~0.18 | ~0.06 | 3x faster |
Every workload sees a 3x or greater improvement. Models that were impossible on the 4060 (any 8B FP16) now run comfortably. Check more benchmarks on the tokens-per-second benchmark tool.
Models the 5080 Unlocks
Moving from 8GB to 16GB opens these models:
- Llama 3 8B FP16 — full-precision inference, no quantisation artifacts
- Mistral 7B FP16 — maximum quality instruction following
- DeepSeek R1 7B FP16 — full-precision reasoning
- Gemma 2 9B Q4 — Google’s capable 9B model with headroom
- DeepSeek R1 14B Q4 — tight fit but possible at INT4
- SDXL with LoRA — enough VRAM for model plus fine-tuned adapters
- Dual small models — two 3B-4B models simultaneously
For the full compatibility breakdown, see our guides on Ollama on the RTX 4060 and vLLM on the RTX 5080.
Cost and ROI Analysis
| Metric | RTX 4060 | RTX 5080 |
|---|---|---|
| Monthly hosting | ~$50-70/mo | ~$120-160/mo |
| Cost per 1M tokens (8B Q4) | ~$0.10 | ~$0.03 |
| Models accessible | 7B Q4 only | 7B-8B FP16, 14B Q4 |
| Concurrent users | 1 | 4-6 |
| Equivalent API cost | ~$120/mo | ~$350/mo |
| Monthly savings vs API | ~$50-70 | ~$190-230 |
The RTX 5080 costs about $60-90 more per month than the 4060 but delivers 3x the throughput and 3x lower cost-per-token. At moderate production volumes, it saves more against API pricing than the 4060 does. Use the LLM cost calculator for your specific workload.
Verdict and Alternatives
Upgrade to the 5080 if: you have outgrown 8GB, you want FP16 quality, you need to serve multiple users, or you want 3x faster inference for your existing 7B workloads.
Consider the RTX 3090 instead if: you need 24GB for 13B+ FP16 models or 34B Q4 models. The 4060 to 3090 upgrade trades Blackwell speed for more VRAM at a similar price.
Stay on the 4060 if: 7B Q4 for a single user is all you need and the budget is tight.
Explore all upgrade options in the GPU Comparisons section and compare self-hosting costs against APIs using the GPU vs API cost comparison tool.
Upgrade to RTX 5080 Blackwell
16GB GDDR7, 3.5x the bandwidth. Turn your budget GPU into a production powerhouse.
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