Yes — mradermacher/Mistral_7B-Open_Hermes-NSFWV1-GGUF fits on A100 80GB: estimated 5.72 GB (weights + KV cache) against 80 GB.
Hugging Bay is intentionally crawlable for search engines, answer engines, user-requested AI fetches, frontier AI assistants, and open-source agents. Public pages describe verified open-source AI artifact metadata, provenance, licenses, community trust signals, and selective hosted mirrors with hashes. Hugging Bay does not bypass gated or restricted upstream access controls.
Machine-readable entrypoints: robots.txt, AI search guidance, well-known AI search JSON, AI citation packs, well-known AI citation JSON, llms.txt, llms-full.txt, AI crawler policy, AI bot allowlist, answer-engine manifest, agent manifest, OpenAPI, and indexing status.
Yes — mradermacher/Mistral_7B-Open_Hermes-NSFWV1-GGUF fits on A100 80GB: estimated 5.72 GB (weights + KV cache) against 80 GB.
| GPU | Verdict | VRAM |
|---|---|---|
| NVIDIA T4 16GB (free Colab) | Yes | 16 GB |
| NVIDIA L4 24GB | Yes | 24 GB |
| RTX 3060 12GB | Yes | 12 GB |
| RTX 4080 16GB | Yes | 16 GB |
| RTX 3090 24GB | Yes | 24 GB |
| RTX 4090 24GB | Yes | 24 GB |
| A100 40GB | Yes | 40 GB |
| A100 80GB | Yes | 80 GB |
| H100 80GB | Yes | 80 GB |
| Apple M-series 16GB (unified) | Yes | 16 GB |
| Apple M-series 32GB (unified) | Yes | 32 GB |
| Apple M-series 64GB (unified) | Yes | 64 GB |
| Apple M-series 128GB (unified) | Yes | 128 GB |
| CPU / 32GB system RAM | Yes | 32 GB |
| CPU / 64GB system RAM | Yes | 64 GB |
| Repo | Quant | Approx weights | Hosting |
|---|---|---|---|
| MaziyarPanahi/Llama-2-7b-chat-hf-GGUF | q3_k_m | 3.1 GB | external |
| mradermacher/VeriPrefer-Mistral-7B-v0.2-GGUF | q2_k | 2.3 GB | external |
| alexandreteles/bonito-v1-gguf | f16 | — | external |
| mstyslavity/Mistral-Small-3.1-24B-Base-2503-mlx-fp16 | unknown | 48 GB | external |
| mradermacher/Mistral_7B-Open_Hermes-NSFWV1-GGUF | q3_k_m | 3.1 GB | external |
| mradermacher/Mistral-Small-24B-Instruct-2501-GGUF | q3_k_m | 10.6 GB | external |
| mradermacher/DiscoPhoenix-7B-dpo-i1-GGUF | unknown | 14 GB | external |
| dagda76/Ministral-3-14B-Reasoning-2512-Q4_K_M-GGUF | q4_k_m | 8 GB | external |
| sachin-sith/Mistral-Small-4-119B-2603-MLX-4bit | unknown | 238 GB | external |
| MaziyarPanahi/Mistral-7B-KNUT-v0.3-Mistral-7B-Instruct-v0.2-slerp-GGUF | q3_k_m | 3.1 GB | external |
| mradermacher/Kunpeng-4x7B-mistral-GGUF | q3_k_m | 12.3 GB | external |
| mradermacher/Hercules-2.5-Mistral-7B-i1-GGUF | unknown | 14 GB | external |
Methodology: weights + KV-cache estimate from parameter count, quant precision, and declared context. Not a benchmark; runtimes differ. Data recomputed hourly.