Mac Studio M3 Ultra 256GBBudget pick
256 GB Unified (+128)
A
Adds memory headroom for longer context windows and future model growth.4.7 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
mxbai Embed Large needs ~15.7 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With F16 quantization, expect ~5 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
4.7 tok/s
TTFT
41279 ms
Safe context
512
Memory
16.2 GB / 128.0 GB
This model fits, but memory bandwidth is the part holding decode speed back.
Throughput will feel slow
Estimated decode speed is only 4.7 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
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 | 4.7 tok/s | 22516 ms | 512 |
| Coding | B | Runs well | 4.7 tok/s | 41279 ms | 512 |
| Agentic Coding | B | Runs well | 4.7 tok/s | 60043 ms | 512 |
| Reasoning | B | Runs well | 4.7 tok/s | 48785 ms | 512 |
| RAG | B | Runs well | 4.7 tok/s | 75053 ms | 512 |
How mxbai Embed Large (0.33500000834465027B 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 | 0.1 GB | Low | A72 |
Q3_K_S | 3 | 0.2 GB | Low | A72 |
NVFP4 | 4 |
Copy-paste commands to run mxbai Embed Large on your machine.
Run
ollama run mxbai-embed-largeUpgrade options
Yes, Intel Data Center GPU Max 1550 128GB can run mxbai Embed Large with a B grade (Runs well). Expected decode speed: 4.7 tok/s.
mxbai Embed Large (0.33500000834465027B parameters) requires approximately 15.7 GB of memory with F16 quantization.
The recommended quantization for mxbai Embed Large is F16, which balances quality and memory efficiency.
On Intel Data Center GPU Max 1550 128GB, mxbai Embed Large achieves approximately 4.7 tokens per second decode speed with a time-to-first-token of 41279ms using F16 quantization.
For coding workloads, mxbai Embed Large on Intel Data Center GPU Max 1550 128GB receives a B grade with 4.7 tok/s and 512 context.
On Intel Data Center GPU Max 1550 128GB, mxbai Embed Large can safely use up to 512 tokens of context. The model's official context limit is 512, 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/mxbai-embed-large-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:
0.2 GB |
| Medium |
| A72 |
Q4_K_M | 4 | 0.2 GB | Medium | A72 |
Q5_K_M | 5 | 0.2 GB | High | A72 |
Q6_K | 6 | 0.3 GB | High | A72 |
Q8_0 | 8 | 0.4 GB | Very High | A72 |
F16Best for your GPU | 16 | 0.7 GB | Maximum | A72 |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
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.