readygpu

Intel Arc Pro B60 vs NVIDIA GeForce RTX 5090

For running models locally, priced in Canada. At 32k context the first runs 48 models fully in memory, the second 52.

In short

The NVIDIA GeForce RTX 5090 has more memory for models: 32 GB against 24 GB. At 32k context the NVIDIA GeForce RTX 5090 runs 52 of the models we track fully in memory and the Intel Arc Pro B60 48; only the NVIDIA GeForce RTX 5090 holds Kimi Linear 48B-A3B. On the models both run fully in memory, the NVIDIA GeForce RTX 5090 is about 4.9× as fast as the Intel Arc Pro B60 (estimated, median across models). In Canada the Intel Arc Pro B60 costs $985 at Amazon and the NVIDIA GeForce RTX 5090 $10,000 at eBay: the Intel Arc Pro B60 is $9,015 cheaper.

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

Intel Arc Pro B60

$985

Amazon

Add to build
NVIDIA GeForce RTX 5090

$10,000

eBay

Add to build

Specifications

SpecIntel Arc Pro B60NVIDIA GeForce RTX 5090
GPUArc Pro B60GeForce RTX 5090
Memory24 GB GDDR632 GB GDDR7
Memory bus192-bit512-bit
Memory bandwidth456 GB/s1,792 GB/s
FP16 compute98.5 TFLOPS209 TFLOPS
SoftwareSYCL / oneAPICUDA
Board power200 W575 W
PCIe generationGen 5Gen 5
PCIe lanesx8x16

What each runs

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

ModelIntel Arc Pro B60NVIDIA GeForce RTX 5090
Qwen 3.8 27B10tok/sQ5_K_M35tok/sQ8_0
Qwen 3.6 27B11tok/sQ5_K_M36tok/sQ8_0
Gemma 4 31B10tok/sQ4_K_M39tok/sQ6_K
Qwen 3.6 35B-A3B48tok/sQ4_K_M201tok/sQ6_K
Qwen 3.5 27B10tok/sQ5_K_M36tok/sQ8_0
Gemma 4 26B-A4B28tok/sQ6_K132tok/sQ8_0
Qwen 3.5 35B-A3B49tok/sQ4_K_M212tok/sQ6_K
GLM 4.7 Flash24tok/sQ5_K_M114tok/sQ6_K
Devstral Small 2 24B10tok/sQ5_K_M37tok/sQ8_0
gpt-oss 20B39tok/sMXFP4206tok/sMXFP4
Qwen3 Coder 30B-A3B25tok/sQ4_K_M117tok/sQ6_K
Kimi Linear 48B-A3Boffload311tok/sQ4_K_M
Qwen3 32Boffload35tok/sQ5_K_M
Gemma 4 12B17tok/sQ8_082tok/sQ8_0
Qwen 3.5 9B21tok/sQ8_0104tok/sQ8_0
Mistral Small 3.2 24B10tok/sQ5_K_M37tok/sQ8_0
Gemma 3 27B10tok/sQ5_K_M35tok/sQ8_0
DeepSeek R1 Distill Qwen 32Boffload35tok/sQ5_K_M
Ministral 3 14B12tok/sQ8_056tok/sQ8_0
Qwen3 14B11tok/sQ8_053tok/sQ8_0
DeepSeek R1 0528 Qwen3 8B17tok/sQ8_082tok/sQ8_0
Qwen3 8B17tok/sQ8_082tok/sQ8_0
Phi-4 14B10tok/sQ8_050tok/sQ8_0
Qwen 3.5 4B41tok/sQ8_0198tok/sQ8_0
Ministral 3 8B17tok/sQ8_082tok/sQ8_0
Granite 4.2 8B16tok/sQ8_076tok/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
Mellum2.1 12B A2.5B Thinking40tok/sQ8_0210tok/sQ8_0
granite vision 4.1 4b36tok/sQ8_0176tok/sQ8_0
granite 4.2 3b35tok/sQ8_0169tok/sQ8_0
Kimi VL A3B29tok/smlx-8bit156tok/smlx-8bit
LFM2.5 24B A2B28tok/sQ6_K139tok/sQ8_0
DeepSeek R1 Distill Qwen 7B23tok/sQ8_0111tok/sQ8_0
GLM 4.6V Flash20tok/sQ8_098tok/sQ8_0
DeepSeek R1 Distill Llama 8B18tok/sQ8_087tok/sQ8_0
GLM 4.1V 9B Thinking17tok/smlx-8bit85tok/smlx-8bit
Ministral 3 8B Reasoning 251217tok/sQ8_082tok/sQ8_0
DeepSeek R1 Distill Qwen 14B10tok/sQ8_050tok/sQ8_0
Devstral Small 250710tok/sQ5_K_M37tok/sQ8_0
Magistral Small 250610tok/sQ5_K_M37tok/sQ8_0
Magistral Small 250710tok/sQ5_K_M37tok/sQ8_0
Magistral Small 250910tok/sQ5_K_M37tok/sQ8_0
granite 4.2 30boffload38tok/sQ5_K_M
Qwen 3.5 122B-A10Boffloadoffload
Qwen3 Coder Nextoffloadoffload
Nemotron 3 Super 120B-A12Bwon't runoffload
Mistral Small 4 119Boffloadoffload
GLM 4.5 Airoffloadoffload
Qwen3 Next 80B-A3Boffloadoffload
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 Pro B60 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 Pro B60 to spend less.
Which can run bigger models, the Intel Arc Pro B60 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 Pro B60 48; only the NVIDIA GeForce RTX 5090 holds Kimi Linear 48B-A3B.
Which is faster for local AI, the Intel Arc Pro B60 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 Pro B60 (estimated, median across models).
What is the price difference between the Intel Arc Pro B60 and the NVIDIA GeForce RTX 5090?
In Canada the Intel Arc Pro B60 costs $985 at Amazon and the NVIDIA GeForce RTX 5090 $10,000 at eBay: the Intel Arc Pro B60 is $9,015 cheaper.

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