Mac Studio M3 Ultra 256GBBudget pick
256 GB Unified (+128)
C
Adds memory headroom for longer context windows and future model growth.76.1 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
gemma 3 12b it needs ~22.4 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~168 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
Fit status
Runs well
Decode
168.0 tok/s
TTFT
1152 ms
Safe context
1.2M
Memory
22.4 GB / 128.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 168.0 tok/s | 629 ms | 1.2M |
| Coding | C | Runs well | 168.0 tok/s | 1152 ms | 1.2M |
| Agentic Coding | C | Runs well | 168.0 tok/s | 1676 ms | 1.2M |
| Reasoning | C | Runs well | 168.0 tok/s | 1362 ms | 1.2M |
| RAG | C | Runs well | 168.0 tok/s | 2095 ms | 1.2M |
How gemma 3 12b it (12B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | D38 |
Q3_K_S | 3 | 5.9 GB | Low | D38 |
NVFP4 | 4 |
Copy-paste commands to run gemma 3 12b it on your machine.
Run
lms load hf-maziyarpanahi--gemma-3-12b-it-gguf && lms server startUpgrade options
Yes, Intel Data Center GPU Max 1550 128GB can run gemma 3 12b it with a C grade (Runs well). Expected decode speed: 168.0 tok/s.
gemma 3 12b it (12B parameters) requires approximately 22.4 GB of memory with Q4_K_M quantization.
The recommended quantization for gemma 3 12b it is Q4_K_M, which balances quality and memory efficiency.
On Intel Data Center GPU Max 1550 128GB, gemma 3 12b it achieves approximately 168.0 tokens per second decode speed with a time-to-first-token of 1152ms using Q4_K_M quantization.
For coding workloads, gemma 3 12b it on Intel Data Center GPU Max 1550 128GB receives a C grade with 168.0 tok/s and 1.2M context.
On Intel Data Center GPU Max 1550 128GB, gemma 3 12b it can safely use up to 1.2M tokens of context. The model's official context limit is —, 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/hf-maziyarpanahi--gemma-3-12b-it-gguf-on-max-1550-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
6.7 GB |
| Medium |
| D38 |
Q4_K_M | 4 | 7.3 GB | Medium | D38 |
Q5_K_M | 5 | 8.6 GB | High | D38 |
Q6_K | 6 | 9.8 GB | High | D38 |
Q8_0 | 8 | 12.8 GB | Very High | D39 |
F16Best for your GPU | 16 | 24.6 GB | Maximum | D40 |
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.
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.