RTX 4090 24GB vs Radeon RX 7900 XTX 24GB for Local LLMs
Both have 24.0 GB, so they run the same models. The RTX 4090 24GB generates tokens about 23% faster. Below: speeds on seven popular models, the models only one card can run, and our verdict.
Specs that matter for AI
| RTX 4090 24GB | Radeon RX 7900 XTX 24GB | |
|---|---|---|
| Memory | 24 GB | 24 GB |
| GPU-usable memory | 24.0 GB | 24.0 GB |
| Bandwidth | 1008 GB/s | 960 GB/s |
| Architecture | Ada Lovelace | RDNA 3 |
| Released | 2022-10 | 2022-12 |
| Llama 3.1 8B Q4_K_M | ~117 tok/s | ~95 tok/s |
Speed and fit by model
Q4_K_M (or native format) with 8K context.
| Model | VRAM | RTX 4090 24GB | Radeon RX 7900 XTX 24GB |
|---|---|---|---|
| Llama 3.1 8B | 6.3 GB | Fits ~117 tok/s | Fits ~95 tok/s |
| Qwen 3 14B | 10.5 GB | Fits ~66 tok/s | Fits ~54 tok/s |
| gpt-oss-20b | 13.9 GB | Fits ~162 tok/s | Fits ~141 tok/s |
| Qwen 3 30B-A3B | 18.8 GB | Fits ~118 tok/s | Fits ~107 tok/s |
| Qwen 3 32B | 21.4 GB | Fits ~31 tok/s | Fits ~25 tok/s |
| Llama 3.3 70B | 43.2 GB | 52% on GPU ~1.9 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
Same 24 GB, nearly the same bandwidth (1008 vs 960 GB/s), so token generation in llama.cpp is close. The 4090 pulls ahead in prompt processing, image generation and anything built for CUDA first, such as vLLM, ExLlamaV2 and most training tools. The 7900 XTX is the cheaper way to get 24 GB if you mainly use llama.cpp, Ollama or LM Studio.
Which should you buy?
Choose the RTX 4090 24GB if…
- You use vLLM, ExLlama, ComfyUI or training tools.
- You want the fastest prompt processing and image generation.
- You want everything to work without extra setup.
Choose the Radeon RX 7900 XTX 24GB if…
- You mainly use Ollama, LM Studio or llama.cpp.
- You want 24 GB new for less money.
- You are comfortable with Linux and ROCm or 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 4090 24GB: best quant | Radeon RX 7900 XTX 24GB: best quant | RTX 4090 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 4090 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 4090 24GB or Radeon RX 7900 XTX 24GB better for local LLMs?
Same 24 GB, nearly the same bandwidth (1008 vs 960 GB/s), so token generation in llama.cpp is close. The 4090 pulls ahead in prompt processing, image generation and anything built for CUDA first, such as vLLM, ExLlamaV2 and most training tools. The 7900 XTX is the cheaper way to get 24 GB if you mainly use llama.cpp, Ollama or LM Studio.
Which is faster for LLM inference?
The RTX 4090 24GB, by about 23% for token generation on models that fit both (about 117 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.