Mac Studio M3 Ultra 256GBBudget pick
256 GB Unified (+128)
B
Adds memory headroom for longer context windows and future model growth.80.7 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
Gemma 2 9B needs ~24.3 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~126 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
126.0 tok/s
TTFT
1537 ms
Safe context
8K
Memory
24.3 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 | B | Runs well | 126.0 tok/s | 838 ms | 8K |
| Coding | B | Runs well | 126.0 tok/s | 1537 ms | 8K |
| Agentic Coding | B | Runs well | 126.0 tok/s | 2235 ms | 8K |
| Reasoning | B | Runs well | 126.0 tok/s | 1816 ms | 8K |
| RAG | B | Runs well | 126.0 tok/s | 2794 ms | 8K |
How Gemma 2 9B (9B 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 | 3.5 GB | Low | C53 |
Q3_K_S | 3 | 4.4 GB | Low | C53 |
NVFP4 | 4 | 5.0 GB | Medium | C53 |
Q4_K_M | 4 | 5.5 GB | Medium | C53 |
Q5_K_M | 5 | 6.5 GB | High | C53 |
Q6_K | 6 | 7.4 GB | High | C53 |
Q8_0 | 8 | 9.6 GB | Very High | C53 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C53 |
Copy-paste commands to run Gemma 2 9B on your machine.
Run
ollama run gemma2Upgrade options
Yes, Intel Data Center GPU Max 1550 128GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 126.0 tok/s.
Gemma 2 9B (9B parameters) requires approximately 24.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.
On Intel Data Center GPU Max 1550 128GB, Gemma 2 9B achieves approximately 126.0 tokens per second decode speed with a time-to-first-token of 1537ms using Q4_K_M quantization.
For coding workloads, Gemma 2 9B on Intel Data Center GPU Max 1550 128GB receives a B grade with 126.0 tok/s and 8K context.
On Intel Data Center GPU Max 1550 128GB, Gemma 2 9B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-2-9b-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: