Gemma 2 27B needs ~27.8 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q3_K_S quantization, expect ~8 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
7.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.4 tok/s
TTFT
35870 ms
Safe context
6K
Memory
31.0 GB / 24.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload (needs ~0.9 GB host RAM) | 8.1 tok/s | 13015 ms | 6K |
| Coding | F | Too heavy | 5.4 tok/s | 35870 ms | 6K |
| Agentic Coding | F | Too heavy | 2.9 tok/s | 98028 ms | 6K |
| Reasoning | F | Too heavy | 5.4 tok/s | 42392 ms | 6K |
| RAG | F | Too heavy | 2.9 tok/s | 122535 ms | 6K |
How Gemma 2 27B (27B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | B69 |
Q3_K_S | 3 | 13.2 GB | Low | B70 |
NVFP4 | 4 |
Copy-paste commands to run Gemma 2 27B on your machine.
Run
ollama run gemma2:27bUpgrade options
NVIDIA A100 40GBBiggest leapMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$10,000 MSRP
Gaudi 3 128GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$15,000 MSRP
Intel Data Center GPU Max 1550 128GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$15,000 MSRP
Yes, Intel Arc Pro B60 24GB can run Gemma 2 27B at Q3_K_S quantization (Very compromised (needs ~1.8 GB host RAM)). The recommended Q4_K_M requires 31.0 GB which exceeds available memory, but at Q3_K_S it needs only 27.8 GB. Expected decode speed: 7.8 tok/s.
Gemma 2 27B (27B parameters) requires approximately 31.0 GB at Q4_K_M quantization. On Intel Arc Pro B60 24GB, it fits at Q3_K_S using 27.8 GB.
The recommended quantization is Q4_K_M, but on Intel Arc Pro B60 24GB the best fitting quantization is Q3_K_S, which uses 27.8 GB.
On Intel Arc Pro B60 24GB, Gemma 2 27B achieves approximately 7.8 tokens per second decode speed with a time-to-first-token of 24735ms using Q3_K_S quantization.
For coding workloads, Gemma 2 27B on Intel Arc Pro B60 24GB receives a F grade with 5.4 tok/s and 6K context.
On Intel Arc Pro B60 24GB, Gemma 2 27B can safely use up to 8K tokens of context at Q3_K_S quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-2-27b-on-arc-pro-b60-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
15.1 GB |
| Medium |
| B69 |
Q4_K_MBest for your GPU | 4 | 16.5 GB | Medium | B69 |
Q5_K_M | 5 | 19.4 GB | High | F0 |
Q6_K | 6 | 22.1 GB | High | F0 |
Q8_0 | 8 | 28.9 GB | Very High | F0 |
F16 | 16 | 55.4 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.