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Intel Arc Pro B60 vs NVIDIA GeForce RTX 5090

For running models locally, priced in Canada. At 128k context the first runs 23 models fully in memory, the second 41.

In short

The NVIDIA GeForce RTX 5090 has more memory for models: 32 GB against 24 GB. At 128k context the NVIDIA GeForce RTX 5090 runs 41 of the models we track fully in memory and the Intel Arc Pro B60 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 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 27Boffload36tok/sUD-Q6_K
Qwen 3.6 27Boffload36tok/sQ6_K
Gemma 4 31Boffload37tok/sQ4_K_M
Qwen 3.6 35B-A3Boffload128tok/sUD-Q6_K
Qwen 3.5 27Boffload36tok/sQ6_K
Gemma 4 26B-A4B21tok/sQ5_K_M95tok/sQ8_0
Qwen 3.5 35B-A3Boffload129tok/sQ6_K
GLM 4.7 Flashoffload60tok/sQ6_K
gpt-oss 20B22tok/sMXFP4118tok/sMXFP4
Kimi Linear 48B-A3Boffload227tok/sQ4_K_M
Qwen3 Coder 30B-A3Boffload45tok/sQ4_K_M
Gemma 4 12B15tok/sQ8_073tok/sQ8_0
Qwen 3.5 9B16tok/sQ8_080tok/sQ8_0
Gemma 3 27Boffload37tok/sQ5_K_M
Ministral 3 14Boffload36tok/sQ5_K_M
Qwen3 14Boffload35tok/sQ5_K_M
DeepSeek R1 0528 Qwen3 8Boffload40tok/sQ8_0
Qwen3 8Boffload40tok/sQ8_0
Qwen 3.5 4B26tok/sQ8_0126tok/sQ8_0
Ministral 3 8Boffload41tok/sQ8_0
Granite 4.2 8Boffload36tok/sQ8_0
Gemma 4 E4B19tok/sQ8_093tok/sQ8_0
LFM2.5 8B-A1B17tok/sQ8_089tok/sQ8_0
Llama 3.1 8B10tok/sQ5_K_M47tok/sQ6_K
Qwen2.5 0.5B107tok/sQ8_0519tok/sQ8_0
Qwen3.5 0.8B93tok/sQ8_0455tok/sQ8_0
Qwen3.5 2B74tok/sQ5_K_M363tok/sQ5_K_M
DeepSeek R1 Distill Qwen 1.5B40tok/sQ8_0197tok/sQ8_0
Mellum2.1 12B A2.5B Thinking27tok/sQ8_0146tok/sQ8_0
MiniCPM5 2B27tok/sQ8_0134tok/sQ8_0
LFM2.5 2.6B21tok/sQ8_0102tok/sQ8_0
LFM2.5 VL 3B21tok/sQ8_0102tok/sQ8_0
Kimi VL A3B17tok/smlx-8bit91tok/smlx-8bit
granite vision 4.1 4b16tok/sQ8_077tok/sQ8_0
granite 4.2 3b16tok/sQ8_076tok/sQ8_0
GLM 4.6V Flash15tok/sQ8_072tok/sQ8_0
DeepSeek R1 Distill Qwen 7B15tok/sQ8_071tok/sQ8_0
GLM 4.1V 9B Thinking13tok/smlx-8bit65tok/smlx-8bit
DeepSeek R1 Distill Llama 8B10tok/sQ5_K_M43tok/sQ8_0
Ministral 3 8B Reasoning 2512offload41tok/sQ8_0
LFM2.5 24B A2Boffload53tok/sQ6_K
Qwen 3.5 122B-A10Boffloadoffload
Nemotron 3 Super 120B-A12Bwon't runoffload
Qwen3 Coder Nextwon't runoffload
Mistral Small 4 119Boffloadoffload
Devstral Small 2 24Boffloadoffload
Qwen3 Next 80B-A3Bwon't runoffload
Nemotron 3.5 Lightning 30B-A3Boffloadoffload
Qwen3 32Bwon't runoffload
Mistral Small 3.2 24Boffloadoffload
DeepSeek R1 Distill Qwen 32Bwon't runoffload
Phi-4 14Boffloadoffload
DeepSeek R1 Distill Qwen 14Boffloadoffload
Devstral Small 2507offloadoffload
Magistral Small 2506offloadoffload
Magistral Small 2507offloadoffload
Magistral Small 2509offloadoffload
granite 4.2 30bwon'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
GLM 4.5 Airwon't runwon't run
Llama 3.3 70Bwon't runwon't run
DeepSeek R1won't runwon't run
DeepSeek R1 0528won't runwon't run
DeepSeek R1 Distill Llama 70Bwon'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 128k context the NVIDIA GeForce RTX 5090 runs 41 of the models we track fully in memory and the Intel Arc Pro B60 23; only the NVIDIA GeForce RTX 5090 holds Qwen 3.8 27B.
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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