Can you run deepsweet/GigaChat3.1-10B-A1.8B-MLX-MXFP8 on A100 80GB?

Yes — deepsweet/GigaChat3.1-10B-A1.8B-MLX-MXFP8 fits on A100 80GB: estimated 22.94 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 — deepsweet/GigaChat3.1-10B-A1.8B-MLX-MXFP8 fits on A100 80GB: estimated 22.94 GB (weights + KV cache) against 80 GB.

Verdict across common GPUs

GPUVerdictVRAM
NVIDIA T4 16GB (free Colab)No16 GB
NVIDIA L4 24GBTight — close to the limit24 GB
RTX 3060 12GBNo12 GB
RTX 4080 16GBNo16 GB
RTX 3090 24GBTight — close to the limit24 GB
RTX 4090 24GBTight — close to the limit24 GB
A100 40GBYes40 GB
A100 80GBYes80 GB
H100 80GBYes80 GB
Apple M-series 16GB (unified)No16 GB
Apple M-series 32GB (unified)Yes32 GB
Apple M-series 64GB (unified)Yes64 GB
Apple M-series 128GB (unified)Yes128 GB
CPU / 32GB system RAMYes32 GB
CPU / 64GB system RAMYes64 GB

Next steps

  • Open deepsweet/GigaChat3.1-10B-A1.8B-MLX-MXFP8 on Hugging Bay — trust verdict, hosted files, reviews.
  • Machine-readable deployment plan (JSON)
  • Raw fit report (JSON)

Methodology: weights + KV-cache estimate from parameter count, quant precision, and declared context. Not a benchmark; runtimes differ. Data recomputed hourly.