Most people buying a GPU for local LLM inference should skip the RTX 5090 and pick up a used RTX 3090 instead. The 5090 is a genuinely impressive card, but spending $4,900 versus $820 only makes sense in a narrow set of circumstances. Here is the full breakdown.
NVIDIA GeForce RTX 3090
24GB GDDR6X24GB VRAM at ~$820 used. Runs 13B at any quantization and 34B at Q3. The best value card for serious LLM inference.
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Quick answer
For most LLM users running 7B–13B models daily, the RTX 3090 at ~$820 used is the smarter buy. The RTX 5090 only pulls ahead if you need 32GB VRAM for 34B models at high quantization — a real but minority use case.
Spec comparison
| Spec | RTX 5090 | RTX 3090 |
|---|---|---|
| VRAM | 32GB GDDR7 | 24GB GDDR6X |
| Memory bandwidth | 1,792 GB/s | 936 GB/s |
| Architecture | Blackwell (2025) | Ampere (2020) |
| TDP | 575W | 350W |
| Price (2026) | ~$4,900 new | ~$820 used |
| Price gap | — | 2.5x cheaper |
The bandwidth gap is real — the 5090 is nearly twice as fast for token generation. But both cards share a critical trait: 24GB+ VRAM. That matters more than bandwidth for most inference workloads.
VRAM capacity memory bandwidth Specs are manufacturer figures. Bar lengths are scaled independently per metric.
Token generation benchmarks
Ollama at Q4_K_M quantization, with figures modelled from each card’s bandwidth (methodology):
| Model | RTX 5090 tok/s | RTX 3090 tok/s | Speed delta |
|---|---|---|---|
| Llama 3 8B | ~155 | ~55 | +182% |
| Llama 2 13B | ~90 | ~32 | +181% |
| CodeLlama 34B | ~40 | ~18 | +122% |
| Yi-34B (Q4_K_M) | ~35 | ~16 | +119% |
| 70B (Q3_K_M) | ~12 | Won’t fit | N/A |
The 5090 is dramatically faster. But “fast enough” is the relevant benchmark for most users — 32 tok/s on a 13B model is perfectly comfortable for interactive chat and code completion.
Where the 3090 wins: the value case
A used RTX 3090 at ~$820 delivers:
- 24GB VRAM — fits every model the 4090 fits, including 13B at FP16 and 34B at Q3
- 936 GB/s bandwidth — still fast enough for comfortable 13B inference at ~32 tok/s
- Proven reliability with a massive LLM community and years of Ollama/llama.cpp benchmarks
- Power draw 62% lower than the 5090 (350W vs 575W), which matters for 24/7 inference servers
If your models live in the 7B–13B range, the 3090 delivers everything you need for about a sixth of the price.
Where the 5090 wins: the 32GB case
The RTX 5090’s 32GB advantage matters when:
- You regularly run 34B models at Q5–Q6 — these require 26–30GB and won’t fit on 24GB
- You want to test 70B models at Q3_K_M (~30GB) on a single card
- You need long context windows (32K+) where KV cache eats VRAM beyond model weights
- You are doing LoRA fine-tuning where 32GB enables larger batch sizes
- Speed is critical — the 5090’s 1,792 GB/s makes it feel twice as fast on the same models
For these use cases, the $4,080 premium is justified. For everyone else, it is not — and at six times the price, “not” covers most people.
Which GPU should YOU buy?
Buy the RTX 3090 (used) if:
- Your primary models are 7B–13B
- Budget matters and you want maximum VRAM per dollar
- You run an always-on inference server (lower power draw = lower electricity cost)
- You are new to local LLM and want to experiment without overspending
Buy the RTX 5090 if:
- You specifically need 32GB for 34B+ models at high quantization
- Speed is a priority and 13B at 155 tok/s versus 55 tok/s genuinely changes your workflow
- You plan to fine-tune models locally
- You want a card to last 4+ years as LLM model sizes grow
Common mistakes to avoid
- Paying 2.5x more for speed you will not notice on 13B models. At 32 tok/s vs 155 tok/s, both feel fast in interactive use. The difference only matters for batch processing.
- Buying the 5090 expecting to run 70B models comfortably. The 5090 can technically load 70B at Q2–Q3, but quality at that quantization is poor and context is limited. Do not buy a 5090 for a good 70B experience.
- Ignoring the power draw difference. Running a 575W GPU 24/7 costs meaningfully more in electricity than a 350W card over 12–24 months.
- Overlooking the used 3090 risk. Buy from a reputable seller with a return window. Data center pulls are often fine; mined-hard gaming cards less so.
Final verdict
| Your goal | Best GPU | Price |
|---|---|---|
| Daily 7B–13B inference | RTX 3090 (used) | ~$820 |
| 34B models at Q5+ | RTX 5090 | ~$4,900 |
| Max speed, 13B | RTX 5090 | ~$4,900 |
| Budget 24GB VRAM | RTX 3090 (used) | ~$820 |
| Fine-tuning locally | RTX 5090 | ~$4,900 |
The RTX 3090 is not a compromise — it is a deliberate value choice that makes the right trade-offs for most LLM users. If you find yourself running 34B models regularly, the 5090’s 32GB tips the scales. Otherwise, pocket the $4,080 difference.
Check NVIDIA GeForce RTX 5090 on Amazon→Buy on Shopee SG→For more context on used GPU picks, see our best used GPU for LLM guide. If you run through Ollama, our best GPU for Ollama article covers setup and per-model benchmarks. For the current-gen flagship comparison, see RTX 5090 vs 4090 for LLM. Looking at the cheaper Blackwell alternative? Our RTX 5070 Ti vs 3090 for LLM breakdown covers the new $1,050 vs used $820 decision.