“16GB of newer VRAM beats 24GB of older VRAM” — this is wrong for LLMs. Unlike gaming where architecture improvements offset lower specs, LLM inference has a hard VRAM floor. If a model does not fit in memory, no amount of architectural improvement saves you. The RTX 4090 with 24GB remains the better LLM card despite being a generation older.
Check NVIDIA GeForce RTX 4090 on Amazon→Buy on Shopee SG→Who this is for
You are deciding between the RTX 5080 ($1,400) and RTX 4090 ($2,200). Both are serious GPUs, and the $800 price difference makes this a genuine dilemma. This guide breaks down exactly when each card wins.
Head-to-head specifications
| Spec | RTX 5080 | RTX 4090 |
|---|---|---|
| VRAM | 16GB GDDR7 | 24GB GDDR6X |
| Bandwidth | 960 GB/s | 1,008 GB/s |
| TDP | 250W | 450W |
| Price | ~$1,400 | ~$2,200 |
| Architecture | Blackwell | Ada Lovelace |
| Max model (Q4_K_M) | ~13-14B | ~32-34B |
The RTX 4090 has 50% more VRAM, slightly higher bandwidth, and runs larger models. The RTX 5080 costs 37% less, draws half the power, and has a newer architecture.
VRAM capacity memory bandwidth Specs are manufacturer figures. Bar lengths are scaled independently per metric.
Benchmark comparison
Ollama at Q4_K_M quantization. Both columns are bandwidth-derived estimates — see methodology:
| Model | RTX 5080 (16GB) | RTX 4090 (24GB) | Winner |
|---|---|---|---|
| Llama 3 8B (7B) | ~55 tok/s | ~65 tok/s | RTX 4090 |
| Mistral 7B | ~55 tok/s | ~65 tok/s | RTX 4090 |
| Qwen 2.5 14B | ~32 tok/s | ~38 tok/s | RTX 4090 |
| DeepSeek-R1 32B | Won’t fit | ~20 tok/s | RTX 4090 |
| Qwen 2.5 32B | Won’t fit | ~20 tok/s | RTX 4090 |
| CodeLlama 34B | Won’t fit | ~18 tok/s | RTX 4090 |
The RTX 4090 wins every comparison. It is faster on small models (higher bandwidth) and it is the only card that can run 32B+ models. The RTX 5080 loses on both speed and model capacity.
Check NVIDIA GeForce RTX 5080 on Amazon→Buy on Shopee SG→When the RTX 5080 still makes sense
The RTX 5080 is not a bad card. It wins in specific scenarios:
- You only run 7B-13B models. If you never plan to touch 32B models, 16GB is sufficient and you save $800.
- Power matters. 250W vs 450W is significant over thousands of hours. At $0.18/kWh running 8 hours daily, the RTX 5080 saves roughly $5/month on electricity.
- You want a newer platform. Blackwell gives you longer driver support, DLSS 4 for gaming, and a card that will hold resale value better.
- Budget caps out around $1,400. If $2,200 is simply not an option, the RTX 5080 is the fastest 16GB card available — and $1,400 is very close to what it costs, so treat it as the ceiling rather than a saving.
When the RTX 4090 is the clear choice
- You want to run 32B models. DeepSeek-R1 32B, Qwen 2.5 32B, CodeLlama 34B — none of these fit on 16GB. The 4090’s 24GB is non-negotiable for this class of model.
- You need longer context windows. Even with 7B models, 24GB gives you room for 16K-32K context versus 8K-12K on 16GB.
- You want the best tok/s. The 4090’s 1,008 GB/s bandwidth edges out the 5080 in every model size where both cards can run the model.
Which should you buy?
If you are committed to staying within 7B-13B models and want to save $800, the RTX 5080 is a solid card at $1,400. If there is any chance you want to run 32B models, or you value maximum context length on any model, the RTX 4090 at $2,200 is worth the premium. The extra 8GB of VRAM unlocks an entire tier of models that the 5080 simply cannot access.
Common mistakes to avoid
- Assuming newer generation means better for LLMs. Architecture improvements help gaming and compute tasks. For LLM inference, VRAM capacity and bandwidth are what matter. The 4090 wins both.
- Comparing only tok/s on 7B models. At 7B, the difference is 55 vs 65 tok/s — both feel fast. The gap that matters is 0 tok/s vs 20 tok/s on 32B models.
- Ignoring the used RTX 3090 alternative. A used 3090 (~$820) has 24GB VRAM and 936 GB/s bandwidth. It beats the RTX 5080 for LLM workloads at well under half the price — the gap has widened, not narrowed.
- Buying the RTX 5080 planning to upgrade later. If you know you will want 32B models eventually, buy the 4090 now. Upgrading from a 5080 to a 4090 later costs more than the $800 difference.
Our recommendation
For LLM inference, the RTX 4090 wins this comparison. The 8GB VRAM advantage is not a minor spec difference — it determines whether entire model classes are accessible or completely blocked. The RTX 5080 is a fine card for gaming and general compute, but for LLM workloads, the older 4090 remains king.
Check NVIDIA GeForce RTX 4090 on Amazon→Buy on Shopee SG→ Check NVIDIA GeForce RTX 5080 on Amazon→Buy on Shopee SG→In LLM inference, VRAM capacity is a cliff — your model either fits or it does not. There is no “almost fits” that works.
For more comparisons in this price range, see our best GPU for Ollama guide. Both cards here are now well past $1,000; if that is your actual ceiling, our under $1,000 GPU guide covers what fits, and a used 3090 is the card to read about first.