What can Intel Arc Pro B60 24GB run for coding?
Build: Intel Arc Pro B60 24GB + — + 32 GB RAM (windows)
Runs comfortably143 models
Ranked by fit for coding use case + predicted speed. Click a row for VRAM breakdown.
Quant: Q4_K_MContext: 8,192VRAM: 8.9 GBHeadroom: 15.1 GBollama run codegemma:7b49tok/sEstimated
ollama run codegemma:7bQuant: Q4_K_MContext: 8,192VRAM: 19.0 GBHeadroom: 5.0 GBollama run deepseek-coder-v2:16b21tok/sEstimated
ollama run deepseek-coder-v2:16bQuant: Q4_K_MContext: 2,048VRAM: 17.4 GBHeadroom: 6.6 GBollama run codestral:22b15tok/sEstimated
ollama run codestral:22bQuant: Q4_K_MContext: 8,192VRAM: 16.8 GBHeadroom: 7.2 GBollama run qwen3:14b24tok/sEstimated
ollama run qwen3:14bQuant: Q5_K_MContext: 8,192VRAM: 19.0 GBHeadroom: 5.0 GBollama run qwen2.5:14b21tok/sEstimated
ollama run qwen2.5:14bQuant: Q4_K_MContext: 8,192VRAM: 19.6 GBHeadroom: 4.4 GBollama run muse-glimmer11tok/sEstimated
ollama run muse-glimmerQuant: Q8_0Context: 8,192VRAM: 10.0 GBHeadroom: 14.0 GBollama run qwen2.5:7b28tok/sEstimated
ollama run qwen2.5:7bQuant: Q8_0Context: 8,192VRAM: 13.6 GBHeadroom: 10.4 GBollama run qwen3:8b24tok/sEstimated
ollama run qwen3:8bQuant: Q4_K_MContext: 2,048VRAM: 18.7 GBHeadroom: 5.3 GBollama run mistral-small:24b14tok/sEstimated
ollama run mistral-small:24bQuant: Q4_K_MContext: 8,192VRAM: 4.6 GBHeadroom: 19.4 GB113tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 4.5 GBHeadroom: 19.5 GB113tok/sEstimated
Quant: FP16Context: 8,192VRAM: 19.0 GBHeadroom: 5.0 GBollama run llama3.1:8b13tok/sEstimated
ollama run llama3.1:8bRuns with tradeoffs56 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: Q4_K_MContext: 8,192VRAM: 23.1 GBHeadroom: 0.9 GB- • Tight VRAM fit — only 0.9 GB headroom left for context growth
ollama run qwen2.5-coder:32b11tok/sEstimated
- • Tight VRAM fit — only 0.9 GB headroom left for context growth
ollama run qwen2.5-coder:32bQuant: Q4_K_MContext: 2,048VRAM: 23.7 GBHeadroom: 0.3 GB- • Tight VRAM fit — only 0.3 GB headroom left for context growth
ollama run qwen3:30b11tok/sEstimated
- • Tight VRAM fit — only 0.3 GB headroom left for context growth
ollama run qwen3:30bQuant: Q4_K_MContext: 2,048VRAM: 23.8 GBHeadroom: 0.2 GB- • Tight VRAM fit — only 0.2 GB headroom left for context growth
ollama run gemma4:31b11tok/sEstimated
- • Tight VRAM fit — only 0.2 GB headroom left for context growth
ollama run gemma4:31bQuant: Q4_K_MContext: 8,192VRAM: 37.0 GBHeadroom: 6.2 GB- • Partial CPU offload: ~35% of layers run on CPU
ollama run qwen2.5:32b11tok/sEstimated
- • Partial CPU offload: ~35% of layers run on CPU
ollama run qwen2.5:32bQuant: Q5_K_MContext: 8,192VRAM: 40.1 GBHeadroom: 3.1 GB- • Partial CPU offload: ~40% of layers run on CPU
ollama run qwen3:32b9tok/sEstimated
- • Partial CPU offload: ~40% of layers run on CPU
ollama run qwen3:32bQuant: Q4_K_MContext: 8,192VRAM: 36.0 GBHeadroom: 7.2 GB- • Partial CPU offload: ~33% of layers run on CPU
ollama run qwen3-coder:30b11tok/sEstimated
- • Partial CPU offload: ~33% of layers run on CPU
ollama run qwen3-coder:30bQuant: AWQ-INT4Context: 8,192VRAM: 37.0 GBHeadroom: 6.2 GB- • Partial CPU offload: ~35% of layers run on CPU
6tok/sEstimated
- • Partial CPU offload: ~35% of layers run on CPU
Quant: AWQ-INT4Context: 8,192VRAM: 37.5 GBHeadroom: 5.7 GB- • Partial CPU offload: ~36% of layers run on CPU
6tok/sEstimated
- • Partial CPU offload: ~36% of layers run on CPU
What if you upgraded?
Hypothetical scenarios. We re-ran the compatibility engine for each.
+32 GB system RAM
~$80–150
Doubles your CPU-offload working set. Helps when models don't quite fit in VRAM.
Unlocks: 41 new comfortable, 73 new tradeoff
- • Qwen 3 0.6B
- • Qwen 3 1.7B
- • Gemma 3 270M
- • SmolLM2 135M Instruct
Upgrade to Intel Gaudi 2
see current pricing
96 GB VRAM (vs your 24 GB) plus a bandwidth jump from ~456 GB/s to ~2450 GB/s.
Unlocks: 122 new comfortable
- • Qwen 3 0.6B
- • Llama 4 Scout
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
Add a second Intel Arc Pro B60 24GB
see current pricing
Tensor parallelism splits the model across both cards, effectively doubling VRAM. Bandwidth doesn't double — runs ~1.5× the single-card speed in practice.
Unlocks: 94 new comfortable
- • Qwen 3 0.6B
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • Qwen3.6 27B
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Won't runtop 5 popular models
Need more memory than you have. Shown for orientation.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
How to read these numbers
Want a specific benchmark we don't have? Email Contact support and we'll prioritize it.