Can you run bartowski/Qwen_Qwen3-Next-80B-A3B-Thinking-GGUF on H100 80GB?

Yes — bartowski/Qwen_Qwen3-Next-80B-A3B-Thinking-GGUF fits on H100 80GB: estimated 3.48 GB (weights + KV cache) against 80 GB.

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Yes — bartowski/Qwen_Qwen3-Next-80B-A3B-Thinking-GGUF fits on H100 80GB: estimated 3.48 GB (weights + KV cache) against 80 GB.

Verdict across common GPUs

GPUVerdictVRAM
NVIDIA T4 16GB (free Colab)Yes16 GB
NVIDIA L4 24GBYes24 GB
RTX 3060 12GBYes12 GB
RTX 4080 16GBYes16 GB
RTX 3090 24GBYes24 GB
RTX 4090 24GBYes24 GB
A100 40GBYes40 GB
A100 80GBYes80 GB
H100 80GBYes80 GB
Apple M-series 16GB (unified)Yes16 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

Quantizations of bartowski/Qwen_Qwen3-Next-80B-A3B-Thinking-GGUF

RepoQuantApprox weightsHosting
bartowski/Qwen_Qwen3-Next-80B-A3B-Thinking-GGUFunknown160 GBexternal
VesNFF/Qwen3-VL-Embedding-8B-GGUFunknown16 GBexternal
mradermacher/Qwen3.5-27B-heretic-i1-GGUFunknown54 GBexternal
Mungert/OlympicCoder-32B-GGUFunknown64 GBexternal
NoirZangetsu/unsloth-Qwen2.5-Coder-3B-Instruct-flutter-turkish-trained-ggufunknown6 GBexternal
DhruvalLabs/Qwen3-4B-GGUFunknown8 GBexternal
sizzlebop/Qwen3.5-9B-Fable-5-v1-Q5_K_M-GGUFq5_k_m6.2 GBexternal
mradermacher/Qwen3-VL-32B-Instruct-GGUFunknown64 GBexternal
mradermacher/Huihui-Qwopus3.5-4B-v3-abliterated-GGUFunknown8 GBexternal
prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUFunknown3 GBexternal
mradermacher/huihui-ai-abliteratedV2-Qwen2.5-14B-Inst-BaseMerge-TIES-i1-GGUFunknown28 GBexternal
jrad123777/effect-qwen36-35b-ggufunknown70 GBexternal

Next steps

  • Open bartowski/Qwen_Qwen3-Next-80B-A3B-Thinking-GGUF 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.