ASUS TUF RTX 3090 24GB Review | Generated by AI
Question: Intro to ASUS TUF RTX 3090 O24G Gaming (used, ¥6799)
This is the ASUS TUF Gaming GeForce RTX 3090 OC 24GB, one of the higher-quality RTX 3090 models from the Ampere generation.
The seller listing:
- GPU: NVIDIA GeForce RTX 3090
- VRAM: 24GB GDDR6X
- Model: ASUS TUF RTX 3090 O24G GAMING
- Cooling: Triple-fan TUF cooler
- Condition: “almost new”, no repair, original box
- Price: 6799 CNY
Small correction: the listing says “16GB-32GB range”; RTX 3090 is 24GB, not 16GB.
Core specs
| Item | RTX 3090 |
|---|---|
| Architecture | Ampere |
| CUDA cores | 10496 |
| VRAM | 24GB GDDR6X |
| Memory bus | 384-bit |
| VRAM bandwidth | ~936 GB/s |
| FP32 compute | ~35.6 TFLOPS |
| Tensor cores | 328 (3rd gen) |
| RT cores | 82 |
| PCIe | 4.0 x16 |
| Power | ~350W |
| Launch price | $1499 |
AI / LLM perspective
For your use case (local models, training experiments), RTX 3090 is still a very interesting card.
Compared with your RTX 4070
| RTX 4070 | RTX 3090 | |
|---|---|---|
| VRAM | 12GB | 24GB |
| Architecture | Ada | Ampere |
| FP32 | ~29 TFLOPS | ~36 TFLOPS |
| Memory bandwidth | ~504 GB/s | 936 GB/s |
| Power | 200W | 350W |
| Tensor | newer | older |
The important thing for LLM:
VRAM wins.
A 24GB card can run models that a 12GB card simply cannot.
Examples:
- Qwen 7B/14B comfortably
- Llama 8B/13B
- Qwen2.5 32B quantized
- DeepSeek distilled models
- LoRA fine-tuning of many 7B-14B models
Training capability
A single RTX 3090:
Good:
- LoRA / QLoRA
- small model training
- GPT-2 scale experiments
- diffusion models
- embedding models
Possible:
- 7B fine-tuning with QLoRA
Hard:
- full fine-tuning 7B+
- pretraining large models
For example:
RTX 3090 24GB
FP16:
24GB / 2 bytes ≈ 12B parameters theoretical
But:
weights
+ gradients
+ optimizer states
+ activations
usually need 5-10x memory.
So full training of a 7B model is not realistic on one card.
ASUS TUF RTX 3090 quality
TUF is actually one of the better RTX 3090 designs:
Pros:
✅ Strong VRM ✅ Large heatsink ✅ Good cooling ✅ Less likely to throttle ✅ Better than many cheap 3090 models
Cons:
❌ Huge card (~3 slots) ❌ Heavy (~1.7kg+) ❌ High power consumption ❌ Hot GDDR6X memory
3090 memory temperature is the main issue.
During AI workloads:
GPU core: 70-75°C
VRAM: 90-105°C
is common.
Price evaluation (6799 CNY)
For 2026 used market:
I would say:
- Good price: 3500-4500 CNY
- Acceptable: 4500-5500 CNY
- Expensive: >6000 CNY
¥6799 is quite high.
Why?
Because newer cards exist:
- RTX 4090: much faster but expensive
- RTX 3090 Ti: slightly faster
- RTX 5000 Ada / professional cards
- Used server GPUs
For AI, the value calculation is:
VRAM / price
RTX 3090:
24GB / 5000 = 0.0048 GB/CNY
RTX 4070:
12GB / 4000 = 0.003 GB/CNY
3090 wins, but at 6799 the advantage disappears.
For your multi-GPU AI workstation idea
A better setup:
CPU: i9-13900KF
Motherboard: Z790 with 2 x PCIe slots
GPU:
RTX 3090 24GB
+
RTX 3090 24GB
Total:
48GB VRAM
This is a very capable local AI machine.
You can run:
- 30B-70B quantized models
- multi-GPU inference with vLLM
- QLoRA experiments
- distributed training experiments
The GPU itself is good. The price is the problem.
I would negotiate around 4500-5000 CNY. At ¥6799, I would probably wait for another RTX 3090 listing.
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