for Ollama Local Inference
TL;DR
For local AI inference with Ollama, the RTX 4080 Super offers superior memory bandwidth (736 GB/s vs 960 GB/s effective) and native CUDA/Optimus support, but the RX 7900 XTX has 24GB VRAM vs 16GB, often a bottleneck for larger models. The winner depends on model size: 7900 XTX for 13B+ models, 4080 Super for smaller models or tasks needing fast token generation.
It depends on your workload. For local AI inference with Ollama, the RTX 4080 Super offers superior memory bandwidth (736 GB/s vs 960 GB/s effective) and native CUDA/Optimus support, but the RX 7900 XTX has 24GB VRAM vs 16GB, often a bottleneck for larger models. The winner depends on model size: 7900 XTX for 13B+ models, 4080 Super for smaller models or tasks needing fast token generation.
Winner: GPU Architecture
Tie
Winner: VRAM
AMD RX 7900 XTX
Winner: Memory Bus Width
AMD RX 7900 XTX
Overall Pick
It Depends
| Specification | AMD RX 7900 XTX | NVIDIA RTX 4080 Super |
|---|---|---|
| GPU Architecture | RDNA 3 (Navi 31) | Ada Lovelace (AD103) |
| VRAM | 24 GB GDDR6 | 16 GB GDDR6X |
| Memory Bus Width | 384-bit | 256-bit |
| Memory Bandwidth (Effective) | 960 GB/s (with Infinity Cache) | 736 GB/s |
| CUDA Cores / Compute Units | 6144 Stream Processors | 10240 CUDA Cores |
| Tensor Cores / AI Accelerators | 96 AI Accelerators (per CU) | 320 4th Gen Tensor Cores |
| FP16 (Half) TFLOPS | 61 TFLOPS (sparse) | 83 TFLOPS (sparse) |
| FP8 (Transformer Engine) | 122 TFLOPS (sparse) | 166 TFLOPS (sparse) |
| Inference Software Support (Ollama) | ROCm / Vulkan (experimental) | CUDA / cuDNN (full native) |
| Maximum Model Size (7B parameter, fp16) | 10+ simultaneous models | 6-7 |
| Maximum Model Size (13B parameter, 4-bit quant) | Full 24GB fits easily | Tight fit (16GB) |
| Power Consumption (TBP) | 355W (reference) | 320W (reference) |
| PCIe Interface | PCIe 4.0 x16 | PCIe 4.0 x16 |
| VRAM Error Correction (ECC) | No | No |
| Price (MSRP, launch) | $999 | $999 |
Cross-referenced from our benchmark database , higher is better. Numbers are tokens/sec for LLM workloads, images/min for image workloads.
If you mostly
run a 7B parameter model (e.g., Llama 3) at fp16 and need max tokens/sec for interactive chatbot use
Recommend
NVIDIA RTX 4080 Super
The RTX 4080 Super's Tensor Cores deliver ~15-20% higher tokens/sec in CUDA-optimized Ollama builds. The 16GB VRAM is sufficient for 7B at fp16, and the software stack is plug-and-play while AMD requires ROCm or Vulkan workarounds.
If you mostly
run a 13B or 34B parameter model (e.g., CodeLlama, Mixtral) in 4-bit quantized mode
Recommend
AMD RX 7900 XTX
The RX 7900 XTX's 24GB VRAM lets you load 13B in fp16 or 34B in 4-bit, whereas the 4080 Super's 16GB often forces offloading to system RAM or smaller quants. Memory capacity is king here, even though tokens/sec may be slightly lower due to less optimized ROCm drivers.
If you mostly
run multi-model pipelines or fine-tune LoRAs on local data while still using the GPU for inference
Recommend
NVIDIA RTX 4080 Super
Nvidia's CUDA ecosystem (bitsandbytes, Hugging Face PEFT) works out-of-box. The 4080 Super's higher FP8/FP16 throughput accelerates training, and its lower power draw makes it easier to cool in a continuous-load scenario. The 7900 XTX works but requires more tinkering.
At identical $999 MSRP, the RX 7900 XTX provides 50% more VRAM (24GB vs 16GB), making it the better value for running large models that don't fit in 16GB. However, the RTX 4080 Super delivers higher performance per watt (320W vs 355W) and per dollar in smaller models due to superior software optimization. Total cost of ownership favors the 4080 Super for its lower power bills and hassle-free setup, but the 7900 XTX wins on maximum model capacity.
AMD RX 7900 XTX
NVIDIA RTX 4080 Super
Affiliate disclosure: As an Amazon Associate (tag fredoline-20), MyAIHardware earns from qualifying purchases. Prices are MSRP at publication; verify on Amazon. Use code fredoline-20 at qualifying checkouts.
If you primarily run 7B or smaller models with rapid token generation, the RTX 4080 Super is the clear winner, its CUDA-native Ollama support, higher FP16 throughput, and lower power consumption make it a drop-in solution that just works. For anyone needing to run 13B+ models (especially in fp16) or multi-model chains, the RX 7900 XTX's 24GB VRAM is a decisive advantage that no amount of software optimization can overcome, though you'll deal with ROCm or Vulkan quirks. Neither card is perfect; the ideal choice hinges on your model size sweet spot and tolerance for Linux driver setup.
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