Can the RTX 3090 run a 70B model? Technically yes, but not the way you want. A 70B model at q4_K_M is a 43GB download. The 3090 has 24GB. That means heavy CPU offloading, slow token generation, and a user experience that ranges from “tolerable” to “painful.” Here is exactly what to expect.
Check NVIDIA GeForce RTX 3090 on Amazon→Buy on Shopee SG→Who this is for
This guide is for RTX 3090 owners — or anyone considering a used 3090 — who want to run 70B parameter models like Llama 3 70B, Qwen 72B, or Mixtral 8x22B locally. If you already know 70B is your target, this will save you hours of frustrating experiments.
70B model VRAM requirements
| Quantization | Model Size | VRAM Needed | Fits on 3090? |
|---|---|---|---|
| FP16 | 141GB | ~143GB | No |
| q8_0 | 75GB | ~77GB | No |
| q6_K | 58GB | ~60GB | No |
| q4_K_M | 43GB | ~45GB | Partially (CPU offload) |
| q3_K_M | 34GB | ~36GB | Partially (CPU offload) |
| Q2_K | ~26GB | ~28GB | Barely (heavy offload) |
| IQ2_XXS | ~20GB | ~22GB | Yes (tight) |
Even the most aggressive quantization (IQ2_XXS) barely fits in 24GB with context window overhead. Anything above Q3 requires offloading layers to system RAM.
Real-world performance on the RTX 3090
Llama 3 70B at various quantization levels under llama.cpp, splitting layers between GPU and CPU. Throughput is derived from memory bandwidth rather than measured — our methodology explains the model:
| Setup | Quant | GPU Layers | Speed | Quality |
|---|---|---|---|---|
| Full GPU | IQ2_XXS | 80/80 | ~8 tok/s | Poor (heavy quality loss) |
| Hybrid | Q3_K_M | 45/80 | ~5 tok/s | Acceptable |
| Hybrid | Q4_K_M | 35/80 | ~3 tok/s | Good |
| Hybrid | Q6_K | 20/80 | ~1.5 tok/s | Very good |
| Mostly CPU | Q4_K_M | 10/80 | ~1 tok/s | Good |
At 3 tokens per second (Q4_K_M, 35 GPU layers), a typical 200-word response takes about 25 seconds. That is usable for research and experimentation but frustrating for interactive chat.
At 1-1.5 tok/s, you are essentially watching paint dry. Each response takes over a minute.
What actually works well on the 3090
Instead of struggling with 70B, the 3090 excels with smaller models:
| Model | Quant | Speed on 3090 | Fits Fully? |
|---|---|---|---|
| Llama 3 8B | Q4_K_M | ~65 tok/s | Yes |
| Llama 3 8B | Q8 | ~45 tok/s | Yes |
| CodeLlama 13B | Q4_K_M | ~38 tok/s | Yes |
| Qwen 2.5 32B | Q4_K_M | ~15 tok/s | Yes (tight) |
| Mixtral 8x7B | Q4_K_M | ~25 tok/s | Yes |
The 3090’s sweet spot is 7B-32B models. At these sizes, the full model fits in VRAM and inference is fast and responsive. A 32B Q4 model at 15 tok/s provides better output quality and faster responses than a 70B model limping along at 3 tok/s with CPU offloading.
Check NVIDIA GeForce RTX 3090 on Amazon→Buy on Shopee SG→What you need to run 70B properly
If 70B models are a hard requirement, here is what actually works:
| Setup | VRAM | 70B Q4 Speed | Cost |
|---|---|---|---|
| RTX 3090 (hybrid) | 24GB + CPU | ~3 tok/s | ~$800 used |
| RTX 4090 (hybrid) | 24GB + CPU | ~4 tok/s | ~$2,200 |
| RTX 5090 | 32GB + CPU | ~8 tok/s | ~$4,900+ |
| 2x RTX 3090 | 48GB | ~10 tok/s | ~$1,640 used |
| 2x RTX 4090 | 48GB | ~18 tok/s | ~$4,400 |
Two used RTX 3090s at $1,640 total give you 48GB combined VRAM — enough to fit a 70B Q4 model entirely on GPU. That setup outperforms any single card under $2,000 for this specific workload.
Which GPU should you buy?
You own a 3090 and want to try 70B: Use Q3_K_M or Q4_K_M with partial offloading. Expect 3-5 tok/s. It works for experimentation, not daily use.
You want 70B at usable speeds on one card: No single consumer GPU under $2,000 does this well. The RTX 5090 at 32GB gets closest but still needs some offloading.
You want the cheapest fast 70B setup: Buy two used RTX 3090s. 48GB combined VRAM at ~$1,640 runs 70B Q4 at ~10 tok/s entirely on GPU.
You want the best 3090 experience: Run 7B-32B models instead. The quality of modern 32B models rivals 70B from a year ago, and they run at 15+ tok/s fully on GPU.
Common mistakes to avoid
- Expecting interactive chat speeds with 70B on 24GB. At 3 tok/s, every response feels sluggish. Set realistic expectations before investing time in configuration.
- Using Q2_K or IQ2 quantization to force a fit. Extreme quantization degrades 70B output quality to the point where a well-tuned 13B model produces better answers.
- Ignoring 32B models as an alternative. Qwen 2.5 32B and similar models run at 15 tok/s on the 3090 and produce excellent results. Do not assume bigger is always better.
- Forgetting that CPU offloading needs fast RAM. The offloaded layers run at system memory speed. DDR5-5600 or faster makes a measurable difference. DDR4-2400 creates a severe bottleneck.
Final verdict
| Goal | RTX 3090 Verdict | Better Option |
|---|---|---|
| Run 70B (any speed) | Works at 3-5 tok/s | 2x RTX 3090 ($1,640) |
| Run 70B (interactive) | Too slow | 2x RTX 3090 or RTX 5090 |
| Run 7B-32B fast | Excellent | — |
| Best value per VRAM | Great (used at ~$820) | — |
The RTX 3090 can technically run 70B models, but “can” and “should” are different questions. For practical daily use, stick to 7B-32B models where the 3090 genuinely excels. If 70B is non-negotiable, explore our 3090 vs 4090 comparison and best used GPUs for AI to find the most cost-effective path.
The 3090 is a 7B-32B powerhouse. Forcing 70B through 24GB of VRAM is like shipping freight in a sedan — possible, but not recommended.