readygpu

Intel Arc B580 vs NVIDIA GeForce RTX 5090

For running models locally, priced in Germany. At 32k context the first runs 23 models fully in memory, the second 52.

In short

The NVIDIA GeForce RTX 5090 has more memory for models: 32 GB against 12 GB. At 32k context the NVIDIA GeForce RTX 5090 runs 52 of the models we track fully in memory and the Intel Arc B580 23; only the NVIDIA GeForce RTX 5090 holds Qwen 3.8 27B. On the models both run fully in memory, the NVIDIA GeForce RTX 5090 is about 4.9× as fast as the Intel Arc B580 (estimated, median across models). In Germany the Intel Arc B580 costs 360 € at Amazon and the NVIDIA GeForce RTX 5090 6.028 € at Amazon: the Intel Arc B580 is 5.669 € cheaper.

Updated . Speeds are estimates; how they are worked out.

Intel Arc B580

360 €

Amazon

Add to build
NVIDIA GeForce RTX 5090

6.028 €

Amazon

Add to build

Specifications

SpecIntel Arc B580NVIDIA GeForce RTX 5090
GPUArc B580GeForce RTX 5090
Memory12 GB GDDR632 GB GDDR7
Memory bus192-bit512-bit
Memory bandwidth456 GB/s1,792 GB/s
FP16 compute116.5 TFLOPS209 TFLOPS
SoftwareSYCL / oneAPICUDA
Board power190 W575 W
PCIe generationGen 4Gen 5
PCIe lanesx8x16

What each runs

Estimated decode speed with the most precise quant that fits each one est.

ModelIntel Arc B580NVIDIA GeForce RTX 5090
Qwen 3.8 27Boffload35tok/sQ8_0
Qwen 3.6 27Boffload36tok/sQ8_0
Gemma 4 31Boffload39tok/sQ6_K
Qwen 3.6 35B-A3Boffload201tok/sQ6_K
Qwen 3.5 27Boffload36tok/sQ8_0
Gemma 4 26B-A4Boffload132tok/sQ8_0
Qwen 3.5 35B-A3Boffload212tok/sQ6_K
GLM 4.7 Flashoffload114tok/sQ6_K
Devstral Small 2 24Boffload37tok/sQ8_0
gpt-oss 20Boffload206tok/sMXFP4
Kimi Linear 48B-A3Boffload311tok/sQ4_K_M
Qwen3 Coder 30B-A3Boffload117tok/sQ6_K
Qwen3 32Boffload35tok/sQ5_K_M
Gemma 4 12B21tok/sQ6_K82tok/sQ8_0
Qwen 3.5 9B21tok/sQ8_0104tok/sQ8_0
Gemma 3 27Boffload35tok/sQ8_0
Mistral Small 3.2 24Boffload37tok/sQ8_0
DeepSeek R1 Distill Qwen 32Boffload35tok/sQ5_K_M
Ministral 3 14Boffload56tok/sQ8_0
Qwen3 14Boffload53tok/sQ8_0
DeepSeek R1 0528 Qwen3 8B21tok/sQ5_K_M82tok/sQ8_0
Qwen3 8B21tok/sQ5_K_M82tok/sQ8_0
Phi-4 14Boffload50tok/sQ8_0
Qwen 3.5 4B41tok/sQ8_0198tok/sQ8_0
Ministral 3 8B22tok/sQ5_K_M82tok/sQ8_0
Granite 4.2 8B21tok/sQ4_K_M76tok/sQ8_0
Gemma 4 E4B25tok/sQ8_0121tok/sQ8_0
LFM2.5 8B-A1B47tok/sQ8_0249tok/sQ8_0
Llama 3.1 8B21tok/sQ6_K102tok/sQ6_K
Qwen2.5 0.5B244tok/sQ8_01190tok/sQ8_0
Qwen3.5 0.8B185tok/sQ8_0901tok/sQ8_0
Qwen3.5 2B123tok/sQ5_K_M598tok/sQ5_K_M
DeepSeek R1 Distill Qwen 1.5B81tok/sQ8_0392tok/sQ8_0
MiniCPM5 2B56tok/sQ8_0272tok/sQ8_0
LFM2.5 2.6B47tok/sQ8_0227tok/sQ8_0
LFM2.5 VL 3B47tok/sQ8_0227tok/sQ8_0
granite vision 4.1 4b36tok/sQ8_0176tok/sQ8_0
granite 4.2 3b35tok/sQ8_0169tok/sQ8_0
GLM 4.6V Flash30tok/sQ4_K_M98tok/sQ8_0
DeepSeek R1 Distill Qwen 7B23tok/sQ8_0111tok/sQ8_0
Ministral 3 8B Reasoning 251222tok/sQ5_K_M82tok/sQ8_0
DeepSeek R1 Distill Llama 8B21tok/sQ6_K87tok/sQ8_0
Mellum2.1 12B A2.5B Thinkingoffload210tok/sQ8_0
Kimi VL A3Boffload156tok/smlx-8bit
LFM2.5 24B A2Boffload139tok/sQ8_0
GLM 4.1V 9B Thinkingoffload85tok/smlx-8bit
DeepSeek R1 Distill Qwen 14Boffload50tok/sQ8_0
Devstral Small 2507offload37tok/sQ8_0
Magistral Small 2506offload37tok/sQ8_0
Magistral Small 2507offload37tok/sQ8_0
Magistral Small 2509offload37tok/sQ8_0
granite 4.2 30boffload38tok/sQ5_K_M
Qwen 3.5 122B-A10Bwon't runoffload
Nemotron 3 Super 120B-A12Bwon't runoffload
Qwen3 Coder Nextwon't runoffload
Mistral Small 4 119Bwon't runoffload
GLM 4.5 Airwon't runoffload
Qwen3 Next 80B-A3Bwon't runoffload
Nemotron 3.5 Lightning 30B-A3Boffloadoffload
Llama 3.3 70Bwon't runoffload
DeepSeek R1 Distill Llama 70Bwon't runoffload
DeepSeek V4 Flashwon't runwon't run
GLM 5.3 Flashwon't runwon't run
Qwen 3.8 Flash Nextwon't runwon't run
MiniMax M2.7won't runwon't run
gpt-oss 120Bwon't runwon't run
DeepSeek R1won't runwon't run
DeepSeek R1 0528won't runwon't run
DeepSeek V3 0324won't runwon't run
DeepSeek V3.1won't runwon't run
DeepSeek V3.2won't runwon't run
DeepSeek V4 Flash 0731won't runwon't run
DeepSeek V4 Flash Vision Expwon't runwon't run
Devstral 2 123B Instruct 2512won't runwon't run
GLM 4.5Vwon't runwon't run
GLM 4.6Vwon't runwon't run
GLM 4.7won't runwon't run
GLM 5.1won't runwon't run
GLM 5.2won't runwon't run
GLM 5.3won't runwon't run
Kimi K2won't runwon't run
Kimi K2 Instruct 0905won't runwon't run
Kimi K2 Thinkingwon't runwon't run
Kimi K2.5won't runwon't run
MiniMax M2won't runwon't run
MiniMax M2.1won't runwon't run
MiniMax M2.5won't runwon't run
MiniMax M3won't runwon't run
Mistral Medium 3.5 128Bwon't runwon't run

Questions

Which is better for local AI, the Intel Arc B580 or the NVIDIA GeForce RTX 5090?
Choose the NVIDIA GeForce RTX 5090 to run bigger models. Choose the NVIDIA GeForce RTX 5090 for speed. Choose the Intel Arc B580 to spend less.
Which can run bigger models, the Intel Arc B580 or the NVIDIA GeForce RTX 5090?
At 32k context the NVIDIA GeForce RTX 5090 runs 52 of the models we track fully in memory and the Intel Arc B580 23; only the NVIDIA GeForce RTX 5090 holds Qwen 3.8 27B.
Which is faster for local AI, the Intel Arc B580 or the NVIDIA GeForce RTX 5090?
On the models both run fully in memory, the NVIDIA GeForce RTX 5090 is about 4.9× as fast as the Intel Arc B580 (estimated, median across models).
What is the price difference between the Intel Arc B580 and the NVIDIA GeForce RTX 5090?
In Germany the Intel Arc B580 costs 360 € at Amazon and the NVIDIA GeForce RTX 5090 6.028 € at Amazon: the Intel Arc B580 is 5.669 € cheaper.

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