llamaperf

NVIDIA RTX 5090 Laptop 24GB vs AMD RX 7800 XT 16GB for local LLMs

4 reports on the NVIDIA RTX 5090 Laptop 24GB and 3 on the AMD RX 7800 XT 16GB

Which is faster for local LLMs?

On Qwen3.8 27B at 4-bit, the NVIDIA RTX 5090 Laptop 24GB runs at 30.0 tokens per second (one run) and the AMD RX 7800 XT 16GB at 30.0 (one run). Memory bandwidth, which caps how fast a card can write, is 44% higher on the NVIDIA RTX 5090 Laptop 24GB (896 GB/s on the NVIDIA RTX 5090 Laptop 24GB, 624 on the AMD RX 7800 XT 16GB).

NVIDIA RTX 5090 Laptop 24GB
Memory
24GB
Memory bandwidth
896 GB/s
FP16 compute
200 TFLOPS
Reports
4
AMD RX 7800 XT 16GB
Memory
16GB
Memory bandwidth
624 GB/s
FP16 compute
75 TFLOPS
Reports
3

Measured on both

Median tokens per second from plain runs (one device, one request, no speculative decoding, the whole model in the device's memory) of the same model size at the same quant level. Engines and context lengths can still differ between the runs. How to read these

ModelNVIDIA RTX 5090 Laptop 24GBAMD RX 7800 XT 16GB
Qwen3.8 27B4-bit30.0one run30.0one run

Estimated by the calculator

The quant the calculator recommends for each card and its estimated speed at a 32,768-token context with 32 GB of system RAM, from memory bandwidth and the model's shape. Where a model was also measured above, the measurement wins.

ModelNVIDIA RTX 5090 Laptop 24GBAMD RX 7800 XT 16GB
Qwen3.8 27B32.4 t/sQ4_K_M40.7 t/sQ2_K
Qwen3.8 125B · 6B active19.4 t/sQ2_K, experts in RAMdoesn't fit at these settings
DeepSeek V4 Flash 284B · 13B activedoesn't fit at these settingsdoesn't fit at these settings
Qwen3.6 35B · 3B active115.0 t/sQ4_K_S134.6 t/sQ2_K
Qwen3.6 27B32.4 t/sQ4_K_M40.7 t/sQ2_K
Gemma 4 26B · 4B active73.2 t/sQ5_K_S90.7 t/sQ3_K_S
DeepSeek V4.1 Flash 552B · 16B activedoesn't fit at these settingsdoesn't fit at these settings

Change the context, RAM or model in the calculator: NVIDIA RTX 5090 Laptop 24GB · AMD RX 7800 XT 16GB

Every report on each card, with its source: NVIDIA RTX 5090 Laptop 24GB · AMD RX 7800 XT 16GB

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