NVIDIA-Nemotron-Nano-9B-v2-Q8_0-GGUF
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From language to images, open weights to APIs. Understand capabilities and compare real costs.
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*The showcase above was rendered entirely at home on two RTX 3090s with the files in this repo: OpenAI's Sora launch "Tokyo walk" prompt, then two made-up characters, picture and sound from the model, about 6 minutes per 5-second 1280×736 clip. All characters are fictional.* Quantizations of TenStrip/10Eros-Max `10Eros_Max_h3_8stepTURBO_beta6_pruned.safetensors` (MiniMax H3, 50 blocks + 2 token-refiner blocks), plus the MiniMax H3 text encoder (Qwen3-VL 32B) in a form that runs natively on Ampere. The diffusion files go in `models/diffusion_models`, the text encoder in `models/text_encoders`. The VAEs are the official ones from Comfy-Org/MiniMax-H3: `minimax_h3_video_vae_fp16` and `minimax_h3_audio_vae_fp32`. Every tensor outside the quantized Linears is the source's, byte for byte.
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Adjust provider, category or input/output filters. New models are added continuously.
Adjust monthly token usage to compare estimated API costs.
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.