Mixedbread AI
mxbai Embed Large (0.33500000834465027B parameters) requires approximately 4.0 GB of VRAM with F16 quantization. For the best balance of quality and speed, we recommend hardware with at least 5 GB of VRAM.
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— copy & paste to run locallyCopy-paste commands to run mxbai Embed Large on your machine.
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ollama run mxbai-embed-largeQuick specs
About this model
Inference speed
Estimated decode speed (tokens/sec) for mxbai Embed Large at F16 across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~5 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | F16 | 4.7 | Fits |
| RTX 4090 24GB | 24 GB | F16 | 4.7 | Fits |
| RTX 4080 Super 16GB | 16 GB | F16 | 4.7 | Fits |
| RTX 3090 24GB | 24 GB | F16 | 4.7 | Fits |
| RTX 4070 12GB | 12 GB | F16 | 4.7 | Fits |
| RTX 3060 12GB | 12 GB | F16 | 4.7 | Fits |
| RTX 4060 8GB | 8 GB | F16 | 4.7 | Fits |
RX 7900 XTX 24GB | 24 GB | F16 | 4.7 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | F16 | 4.7 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | F16 | 4.7 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | F16 | 4.7 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | F16 | 4.7 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | F16 | 4.7 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | F16 | 4.7 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | F16 | 4.7 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | F16 | 4.7 | Fits |
Estimates for single-stream decoding at F16; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
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Quantization
How much VRAM mxbai Embed Large (0.33500000834465027B) needs at each GGUF quant, and whether it fits a 24 GB card (RTX 4090 / 3090).
| Quant | Bits | VRAM (weights) | Quality | Fits 24 GB? |
|---|---|---|---|---|
| Q2_K | 2 | 0.1 GB | Low | Fits |
| Q3_K_S | 3 | 0.2 GB | Low | Fits |
| NVFP4 | 4 | 0.2 GB | Medium | Fits |
| Q4_K_M | 4 | 0.2 GB | Medium | Fits |
| Q5_K_M | 5 | 0.2 GB | High | Fits |
| Q6_K | 6 | 0.3 GB | High | Fits |
| Q8_0 | 8 | 0.4 GB | Very High | Fits |
| F16recommended | 16 | 0.7 GB | Maximum | Fits |
VRAM shown is quantized weights only; add ~1–3 GB runtime overhead plus KV cache for your context length. Lower quants trade quality for memory — Q4_K_M is the usual sweet spot; Q2/Q3 only when you must fit a bigger model.
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
mxbai Embed Large (0.33500000834465027B parameters) requires approximately 4.0 GB of VRAM with F16 quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Intel Arc A380 6GB can run mxbai Embed Large with a compatibility score of 81/100. It provides 6 GB of memory and achieves approximately 4.7 tokens per second.
The recommended quantization for mxbai Embed Large is F16, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for mxbai Embed Large: Intel Arc A370M 4GB (score: 82/100), RTX 2060 6GB (score: 81/100), RTX 4050 Laptop 6GB (score: 81/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, mxbai Embed Large is well-suited for embedding as well as rag. It was designed with these use cases in mind.
See also
