Budget Pick
NVIDIA GeForce GTX 1060 3GB3 GB VRAM · ~286.6 tok/s
Lowest cost that meets recommended VRAM
Check price on AmazonCompatibility Check
Can I Run mxbai-embed-large?
mxbai-embed-large is a 0.335B parameter model from the Mixedbread family. Check if your hardware can handle it.
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Social proof
79% of 1,431 scanned PCs run mxbai-embed-large fully on GPU.
1,139 keep at least some work on GPU. Based on anonymous compatibility checks.
Full GPU
1,135Partial GPU
1Hybrid CPU+GPU
3CPU Only
241Can't Run
51Test Your Hardware
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Hardware Requirements
Beginner tip: minimum values mean the model can start, while recommended values usually feel smoother during real use. VRAM is your GPU's dedicated memory; RAM is your system memory used as fallback. See the full glossary.
| Quantization | File Size | Min VRAM | Recommended VRAM | Min RAM | Context |
|---|---|---|---|---|---|
| FP16Easiest | 0.67 GB | 1 GB | 2 GB | 2 GB | 512 / 512 |
Not sure your GPU has enough VRAM? Compare GPUs that can run mxbai-embed-large.
Recommended GPUs for mxbai-embed-large
These GPUs meet the recommended 2 GB VRAM for the FP16 quantization. Estimated speeds are approximate and assume full GPU offloading.
Fastest Pick
NVIDIA GeForce RTX 509032 GB VRAM · ~2674.6 tok/s
Highest estimated throughput
Check price on AmazonBest Value
NVIDIA GeForce RTX 3070 Ti8 GB VRAM · ~907.5 tok/s
Best speed per dollar of VRAM
Check price on AmazonNeed a detailed comparison? See all GPU rankings for mxbai-embed-large.
Why choose mxbai-embed-large?
General-purpose local model brief
- • Pilot testing with your own tasks
- • Controlled local experiments
Quantization tip: Benchmark at least two quantizations and validate with a task-specific eval set before production use.