mxbai Embed Large needs ~4.5 GB VRAM. RTX 5000 Ada Laptop 16GB has 16.0 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
5.0 GB / 16.0 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 | A | Runs well | 4.7 tok/s | 22516 ms | 512 |
| Coding | A | Runs well | 4.7 tok/s | 41279 ms | 512 |
| Agentic Coding | A | Runs well | 4.7 tok/s | 60043 ms | 512 |
| Reasoning | A | 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 RTX 5000 Ada Laptop 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.1 GB | Low | A79 |
Q3_K_S | 3 | 0.2 GB | Low | A79 |
NVFP4 | 4 |
Copy-paste commands to run mxbai Embed Large on your machine.
Run
ollama run mxbai-embed-largeYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 82.3 tok/s | |
| Qwen 3 14B | 14B | S | 53.2 tok/s |
Yes, RTX 5000 Ada Laptop 16GB can run mxbai Embed Large with a A grade (Runs well). Expected decode speed: 4.7 tok/s.
mxbai Embed Large (0.33500000834465027B parameters) requires approximately 4.5 GB of memory with F16 quantization.
The recommended quantization for mxbai Embed Large is F16, which balances quality and memory efficiency.
On RTX 5000 Ada Laptop 16GB, 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 RTX 5000 Ada Laptop 16GB receives a A grade with 4.7 tok/s and 512 context.
On RTX 5000 Ada Laptop 16GB, 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-rtx-5000-ada-laptop-16gb" 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 |
| A79 |
Q4_K_M | 4 | 0.2 GB | Medium | A79 |
Q5_K_M | 5 | 0.2 GB | High | A79 |
Q6_K | 6 | 0.3 GB | High | A79 |
Q8_0 | 8 | 0.4 GB | Very High | A79 |
F16Best for your GPU | 16 | 0.7 GB | Maximum | A79 |
| Qwen 3.5 4B | 4B | S | 56 tok/s |
| Qwen 3 8B | 8B | S | 92.6 tok/s |
 Phi-4-reasoning-plus 14B | 14.7B | S | 50.4 tok/s |
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.