See sarvam-30b MLX in action - demonstration video
Tested on a M3 Ultra 512GB RAM using Inferencer app
- Single inference ~44 tokens/s @ 1000 tokens (measured in debug mode)
- Batched inference ~ total tokens/s across five inferences
- Memory usage: ~42.2 GiB
10bpw quant typically achieves near lossless accuracy in our coding test
| Quantization (bpw) | Perplexity | Token Accuracy | Missed Divergence |
|---|
| q4.5 | 1.32812 | 90.5% | 26.44% |
| q5.5 | 1.23437 | 95.4% | 16.03% |
| q6.5 | 1.21875 | 96.85% | 12.55% |
| q8.5 | 1.21875 | 97.65% | 9.92% |
| q10 | 1.21093 | 97.95% | 9.61% |
| Base | 1.20312 | 100.0% | 0.000% |
Quantized with a modified version of MLX
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We are not the creator, originator, or owner of any model listed. Each model is created and provided by third parties. Models may not always be accurate or contextually appropriate. You are responsible for verifying the information before making important decisions. We are not liable for any damages, losses, or issues arising from its use, including data loss or inaccuracies in AI-generated content.
