RTX 3090 24GB vs Radeon RX 7900 XTX 24GB for Local LLMs
Both have 24.0 GB, so they run the same models. The RTX 3090 24GB generates tokens about 15% 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 | Radeon RX 7900 XTX 24GB | |
|---|---|---|
| Memory | 24 GB | 24 GB |
| GPU-usable memory | 24.0 GB | 24.0 GB |
| Bandwidth | 936 GB/s | 960 GB/s |
| Architecture | Ampere | RDNA 3 |
| Released | 2020-09 | 2022-12 |
| Llama 3.1 8B Q4_K_M | ~109 tok/s | ~95 tok/s |
Speed and fit by model
Q4_K_M (or native format) with 8K context.
| Model | VRAM | RTX 3090 24GB | Radeon RX 7900 XTX 24GB |
|---|---|---|---|
| Llama 3.1 8B | 6.3 GB | Fits ~109 tok/s | Fits ~95 tok/s |
| Qwen 3 14B | 10.5 GB | Fits ~62 tok/s | Fits ~54 tok/s |
| gpt-oss-20b | 13.9 GB | Fits ~155 tok/s | Fits ~141 tok/s |
| Qwen 3 30B-A3B | 18.8 GB | Fits ~114 tok/s | Fits ~107 tok/s |
| Qwen 3 32B | 21.4 GB | Fits ~28 tok/s | Fits ~25 tok/s |
| Llama 3.3 70B | 43.2 GB | 52% on GPU ~1.8 tok/s | 52% on GPU ~1.8 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 and almost the same bandwidth (936 vs 960 GB/s), so they run the same models at similar speed in llama.cpp; the 3090 is slightly ahead in practice because CUDA kernels are more mature. The 3090 is the safer choice for vLLM, ExLlama, training and image tools, and it has NVLink. The 7900 XTX is a new card with a warranty and a good pick if you mainly use llama.cpp-based apps.
Which should you buy?
Choose the RTX 3090 24GB if…
- You want CUDA and the widest tool support.
- You want NVLink or a multi-card setup with mature drivers.
- You find a good used one.
Choose the Radeon RX 7900 XTX 24GB if…
- You prefer a new card with warranty.
- You mainly use llama.cpp-based apps.
- You run Linux or use the Vulkan backend.
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 | Radeon RX 7900 XTX 24GB: best quant | RTX 3090 24GB: max context | Radeon RX 7900 XTX 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 | Radeon RX 7900 XTX 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 Radeon RX 7900 XTX 24GB better for local LLMs?
Both have 24 GB and almost the same bandwidth (936 vs 960 GB/s), so they run the same models at similar speed in llama.cpp; the 3090 is slightly ahead in practice because CUDA kernels are more mature. The 3090 is the safer choice for vLLM, ExLlama, training and image tools, and it has NVLink. The 7900 XTX is a new card with a warranty and a good pick if you mainly use llama.cpp-based apps.
Which is faster for LLM inference?
The RTX 3090 24GB, by about 15% for token generation on models that fit both (about 109 vs 95 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.