MacBook Pro M3 Max 128GBBudget pick
128 GB Unified (+48)
A
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.4.7 tok/s decode
~$2,499 MSRP
mxbai Embed Large needs ~10.9 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 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
11.4 GB / 80.0 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.
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.
| 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 | A | Runs well | 4.7 tok/s | 60043 ms | 512 |
| Reasoning | B | Runs well | 4.7 tok/s | 48785 ms | 512 |
| RAG | A | Runs well | 4.7 tok/s | 75053 ms | 512 |
How mxbai Embed Large (0.33500000834465027B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.1 GB | Low | A74 |
Q3_K_S | 3 | 0.2 GB | Low | A74 |
NVFP4 | 4 |
Copy-paste commands to run mxbai Embed Large on your machine.
Run
ollama run mxbai-embed-largeUpgrade options
~$2,499 MSRP
~$3,999 MSRP
NVIDIA DGX Spark 128GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
Yes, NVIDIA H100 PCIe 80GB 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 10.9 GB of memory with F16 quantization.
The recommended quantization for mxbai Embed Large is F16, which balances quality and memory efficiency.
On NVIDIA H100 PCIe 80GB, 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 NVIDIA H100 PCIe 80GB receives a B grade with 4.7 tok/s and 512 context.
On NVIDIA H100 PCIe 80GB, 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-h100-pcie-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| A74 |
Q4_K_M | 4 | 0.2 GB | Medium | A74 |
Q5_K_M | 5 | 0.2 GB | High | A74 |
Q6_K | 6 | 0.3 GB | High | A74 |
Q8_0 | 8 | 0.4 GB | Very High | A74 |
F16Best for your GPU | 16 | 0.7 GB | Maximum | A74 |
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.