nomic-embed-text v1.5 on NVIDIA RTX PRO 6000 Blackwell Max-Q
Yes — RTX PRO 6000 Blackwell Max-Q runs nomic-embed-text v1.5 excellently at FP16 — 1840 tok/s. 96 GB VRAM with plenty of headroom.
Model Size
137M
Device VRAM
96 GB
Bandwidth
1800 GB/s
Quantization
FP16
Performance by Quantization
OwnRig currently has one published compatibility entry for nomic-embed-text v1.5 on NVIDIA RTX PRO 6000 Blackwell Max-Q at FP16. This is the best supported pairing we can stand behind today.
| Quantization | Speed | TTFT | Fits in VRAM | Rating | Confidence |
|---|---|---|---|---|---|
| FP16 | 1840 tok/s | 2ms | ✓ Yes | Excellent | estimated |
Notes
FP16
Tiny model fits trivially with 96 GB headroom. Practically instant.
About nomic-embed-text v1.5
nomic-embed-text v1.5 (137M) is a embeddings, ai building model. High-quality text embedding model for RAG pipelines. 137M params, negligible VRAM. Competitive with OpenAI's ada-002 on MTEB benchmarks. Essential for builders running local RAG with Cursor or similar tools. Can run concurrently with coding models without meaningful VRAM impact.
View all nomic-embed-text v1.5 hardware options →About NVIDIA RTX PRO 6000 Blackwell Max-Q
NVIDIA RTX PRO 6000 Blackwell Max-Q has 96 GB at 1800 GB/s. Street price: $7,000.
See all models NVIDIA RTX PRO 6000 Blackwell Max-Q can run →Estimate method: Estimated from full RTX PRO 6000 with ~8% reduction for Max-Q 300W power envelope. Reference hardware source: nvidia.com (2026-03-29)
Performance varies by driver version, inference engine, quantization method, context length, and system configuration. Figures shown are estimates based on community benchmarks and may not reflect your exact setup. Product names are trademarks of their respective owners. OwnRig is independent and not affiliated with any hardware or AI model provider.