RTX 3090 24GB vs RTX 4090 24GB for Local LLMs
Both have 24.0 GB, so they run the same models. The RTX 4090 24GB generates tokens about 7% faster. Below: speeds on seven popular models, the models only one card can run, and our verdict.
Specs that matter for AI
| RTX 3090 24GB | RTX 4090 24GB | |
|---|---|---|
| Memory | 24 GB | 24 GB |
| GPU-usable memory | 24.0 GB | 24.0 GB |
| Bandwidth | 936 GB/s | 1008 GB/s |
| Architecture | Ampere | Ada Lovelace |
| Released | 2020-09 | 2022-10 |
| Llama 3.1 8B Q4_K_M | ~109 tok/s | ~117 tok/s |
Speed and fit by model
Q4_K_M (or native format) with 8K context.
| Model | VRAM | RTX 3090 24GB | RTX 4090 24GB |
|---|---|---|---|
| Llama 3.1 8B | 6.3 GB | Fits ~109 tok/s | Fits ~117 tok/s |
| Qwen 3 14B | 10.5 GB | Fits ~62 tok/s | Fits ~66 tok/s |
| gpt-oss-20b | 13.9 GB | Fits ~155 tok/s | Fits ~162 tok/s |
| Qwen 3 30B-A3B | 18.8 GB | Fits ~114 tok/s | Fits ~118 tok/s |
| Qwen 3 32B | 21.4 GB | Fits ~28 tok/s | Fits ~31 tok/s |
| Llama 3.3 70B | 43.2 GB | 52% on GPU ~1.8 tok/s | 52% on GPU ~1.9 tok/s |
| gpt-oss-120b | 62.0 GB | 36% on GPU ~20 tok/s | 36% on GPU ~20 tok/s |
Verdict
Both have 24 GB, so they run exactly the same models. Token generation is limited by bandwidth (936 vs 1008 GB/s), so the 4090 is only about 8% faster at writing text. It is roughly twice as fast at prompt processing, image generation and training. For pure local LLM chat, a used 3090 is the value pick; two 3090s for 48 GB often cost less than one 4090.
Which should you buy?
Choose the RTX 3090 24GB if…
- Your main use is LLM chat, coding or document work.
- You plan to add a second card later for 48 GB.
- You want the lowest cost per gigabyte of fast VRAM.
Choose the RTX 4090 24GB if…
- You also generate images or video, where it is roughly twice as fast.
- You fine-tune models or need fast prompt processing for long inputs.
- You want lower power for the same work and FP8 support.
Best quality and longest context on each card
The highest-quality quantization that fits fully at 8K context, and the longest context that fits at Q4_K_M with an FP16 cache.
| Model | RTX 3090 24GB: best quant | RTX 4090 24GB: best quant | RTX 3090 24GB: max context | RTX 4090 24GB: max context |
|---|---|---|---|---|
| Qwen 3.5 9B | FP16 / BF16 | FP16 / BF16 | 256K | 256K |
| Qwen 3 14B | Q8_0 | Q8_0 | 40K | 40K |
| gpt-oss-20b | MXFP4 (official) | MXFP4 (official) | 128K | 128K |
| Qwen 3.6 27B | Q6_K | Q6_K | 98K | 98K |
| Qwen 3 32B | Q4_K_M | Q4_K_M | 12K | 12K |
| Llama 3.3 70B | Does not fit | Does not fit | – | – |
Image generation
Best weight precision for image models with model swapping, at each model's default resolution. Image generation is also limited by compute, so the card with more tensor-core throughput finishes each image faster even when both fit.
| Model | RTX 3090 24GB | RTX 4090 24GB |
|---|---|---|
| SDXL 1.0 | FP16 / BF16 | FP16 / BF16 |
| FLUX.1 [dev] | FP16 / BF16 | FP16 / BF16 |
| Qwen-Image | FP8 | FP8 |
| Wan 2.2 A14B (T2V / I2V) | FP8 | FP8 |
Frequently asked questions
Is the RTX 3090 24GB or RTX 4090 24GB better for local LLMs?
Both have 24 GB, so they run exactly the same models. Token generation is limited by bandwidth (936 vs 1008 GB/s), so the 4090 is only about 8% faster at writing text. It is roughly twice as fast at prompt processing, image generation and training. For pure local LLM chat, a used 3090 is the value pick; two 3090s for 48 GB often cost less than one 4090.
Which is faster for LLM inference?
The RTX 4090 24GB, by about 7% for token generation on models that fit both (about 117 vs 109 tokens/s on Llama 3.1 8B Q4_K_M).
Which can run bigger models?
Neither: both have 24.0 GB usable, so they run the same models.