# Qwen3.8-Flash-Next-P48NVFP4-MoESQ: Open-Weight Model Source: https://savrn.com/models/qwen3-8-flash-next-p48nvfp4-moesq Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve Qwen3.8-Flash-Next-P48NVFP4-MoESQ (134.7B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 269.4 GB | 323.3 GB | 2x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $3.70 | [2x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $4.00 · [2x MI355X](https://savrn.com/ai-index/pricing/gpus/mi355x) $5.18 | | 8-bit | 134.7 GB | 161.6 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 · [1x MI355X](https://savrn.com/ai-index/pricing/gpus/mi355x) $2.59 | | 4-bit | 67.4 GB | 80.8 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 · [1x MI355X](https://savrn.com/ai-index/pricing/gpus/mi355x) $2.59 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 7, 2026. [Qwen3.8-Flash-Next-P48NVFP4-MoESQ on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/qwen3-8-flash-next-p48nvfp4-moesq/gpus) ## Model Card A W4A4 + paired-4:8 sparse compressed checkpoint of Qwen/Qwen3.8-Flash-Next, produced with MoESQ. The routed MoE expert weights are NVFP4 with paired-4:8 structured sparsity and are stored sparse: only the kept values plus a small mask are on disk. The target is NVIDIA Blackwell (SM100) sparse tensor cores. expert, 48 layers: 36 linear-attention and 12 sparse-attention, hyper-connections, per-layer n-gram embedding) weight including the sparsity mask and scales (2 bits per weight for the kept values). TP2). The per-layer n-gram embedding tables (95.4 GiB, BF16, unchanged) are lookup tables served from CPU memory, as in the base model. paired48nvfp4 MoE backend Links This checkpoint does not… Excerpt from the card by IST Austria Distributed Algorithms and Systems Lab, licensed other. ## Configuration Architecture Qwen4ExpForConditionalGeneration Context length (tokens) 262,144 Layers 48 Hidden size 2,560 Attention heads 24 Key/value heads 2 Head dimension 256 Vocabulary size 248,320 Experts 512 Experts active per token 10 Model type qwen4_exp Quantization compressed-tensors ## Identity and Version Repository ISTA-DASLab/Qwen3.8-Flash-Next-P48NVFP4-MoESQ Publisher IST Austria Distributed Algorithms and Systems Lab Task Text generation Modality Text Library transformers Parameters 134.7B parameters Languages en Revision b25abf193ab0428d70be5888bdc75573c6a70d30 First published 2026-10-02 Last updated 2026-10-04 ## Files and Weights 62 files, 160.1 GB in total. The weights are 49 files totalling 160.0 GB in safetensors. Weights49 files · 160.0 GB Configuration5 files · 43.4 MB Tokenizer4 files · 22.9 MB Documentation1 file · 10.3 KB Other2 files · 13.8 KB Repository1 file · 1.6 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model-00001-of-00049.safetensors | Weights | 3.5 GB | e755141e3c2c | | model-00002-of-00049.safetensors | Weights | 6.2 GB | 28a6a1be17a1 | | model-00003-of-00049.safetensors | Weights | 103.5 GB | f61623b59675 | | model-00004-of-00049.safetensors | Weights | 1.0 GB | 40e96351d9a8 | | model-00005-of-00049.safetensors | Weights | 1.0 GB | 2dd1c82ed884 | | model-00006-of-00049.safetensors | Weights | 1.0 GB | be667bcfde93 | | model-00007-of-00049.safetensors | Weights | 1.0 GB | 9332c1281edf | | model-00008-of-00049.safetensors | Weights | 1.0 GB | 56e34d62c56e | | model-00009-of-00049.safetensors | Weights | 1.0 GB | 3920d1d1ca9e | | model-00010-of-00049.safetensors | Weights | 1.0 GB | 07b0ccd7facf | | model-00011-of-00049.safetensors | Weights | 1.0 GB | 03792ae08c81 | | model-00012-of-00049.safetensors | Weights | 1.0 GB | a7b047c5b241 | | model-00013-of-00049.safetensors | Weights | 1.0 GB | f86b2e2f05bb | | model-00014-of-00049.safetensors | Weights | 1.0 GB | 25e732ccaa55 | | model-00015-of-00049.safetensors | Weights | 1.0 GB | 3eb0f69a151d | | model-00016-of-00049.safetensors | Weights | 1.0 GB | 86b4a5c0af02 | | model-00017-of-00049.safetensors | Weights | 1.0 GB | 253c6ea237fb | | model-00018-of-00049.safetensors | Weights | 1.0 GB | 0735f8d5210c | | model-00019-of-00049.safetensors | Weights | 1.0 GB | 6757585e8f73 | | model-00020-of-00049.safetensors | Weights | 1.0 GB | 68642a7844e2 | | model-00021-of-00049.safetensors | Weights | 1.0 GB | 9c6e825b8cd1 | | model-00022-of-00049.safetensors | Weights | 1.0 GB | d2a9d4dc5276 | | model-00023-of-00049.safetensors | Weights | 1.0 GB | 499fab4b99d7 | | model-00024-of-00049.safetensors | Weights | 1.0 GB | 9baa303daee7 | | model-00025-of-00049.safetensors | Weights | 1.0 GB | 240a46b47d73 | | model-00026-of-00049.safetensors | Weights | 1.0 GB | 9efdda2bd3d4 | | model-00027-of-00049.safetensors | Weights | 1.0 GB | 4116e3a5061f | | model-00028-of-00049.safetensors | Weights | 1.0 GB | 9df5298a39e6 | | model-00029-of-00049.safetensors | Weights | 1.0 GB | 71bd712ca1dc | | model-00030-of-00049.safetensors | Weights | 1.0 GB | 6ca3198ac554 | | model-00031-of-00049.safetensors | Weights | 1.0 GB | 032cceb16c39 | | model-00032-of-00049.safetensors | Weights | 1.0 GB | 92cd3153fac3 | | model-00033-of-00049.safetensors | Weights | 1.0 GB | dbc112205575 | | model-00034-of-00049.safetensors | Weights | 1.0 GB | 3a649099a0be | | model-00035-of-00049.safetensors | Weights | 1.0 GB | b39a992be4d4 | | model-00036-of-00049.safetensors | Weights | 1.0 GB | f3cad2627402 | | model-00037-of-00049.safetensors | Weights | 1.0 GB | 31dc4465361a | | model-00038-of-00049.safetensors | Weights | 1.0 GB | 1661258c60af | | model-00039-of-00049.safetensors | Weights | 1.0 GB | 77f8638e5188 | | model-00040-of-00049.safetensors | Weights | 1.0 GB | 77f66362e8e8 | | model-00041-of-00049.safetensors | Weights | 1.0 GB | f980377d661d | | model-00042-of-00049.safetensors | Weights | 1.0 GB | 4544852bba17 | | model-00043-of-00049.safetensors | Weights | 1.0 GB | de2f4065573b | | model-00044-of-00049.safetensors | Weights | 1.0 GB | 55b81f64819b | | model-00045-of-00049.safetensors | Weights | 1.0 GB | f4d50b3b932c | | model-00046-of-00049.safetensors | Weights | 1.0 GB | 3640f6005b8c | | model-00047-of-00049.safetensors | Weights | 1.0 GB | a44b00cebe36 | | model-00048-of-00049.safetensors | Weights | 1.0 GB | 6ceab4248dad | | model-00049-of-00049.safetensors | Weights | 1.0 GB | cea0bd4a9540 | | config.json | Configuration | 5.6 KB | — | | generation_config.json | Configuration | 202 B | — | | model.safetensors.index.json | Configuration | 43.4 MB | bcb56056424b | | moe_sq_config.yaml | Configuration | 1.5 KB | — | | preprocessor_config.json | Configuration | 390 B | — | | README.md | Documentation | 10.3 KB | — | | SHARD_HASHES.sha256 | Other | 4.9 KB | — | | chat_template.jinja | Other | 9.0 KB | — | | .gitattributes | Repository | 1.6 KB | — | | merges.txt | Tokenizer | 3.4 MB | — | | tokenizer.json | Tokenizer | 12.8 MB | 0997f410c57a | | tokenizer_config.json | Tokenizer | 17.9 KB | — | | vocab.json | Tokenizer | 6.7 MB | — | ## License and Download License other Access Open weights, no gate Download size 160.0 GB [Download from IST Austria Distributed Algorithms and Systems Lab](https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-P48NVFP4-MoESQ) Released by IST Austria Distributed Algorithms and Systems Lab through its official repository on Hugging Face. [Read the license](https://huggingface.co/Qwen/Qwen3.8-Flash-Next/blob/main/LICENSE). ## Built From - Derived from [Qwen/Qwen3.8-Flash-Next](https://savrn.com/models/qwen3-8-flash-next) - Quantized from [Qwen/Qwen3.8-Flash-Next](https://savrn.com/models/qwen3-8-flash-next) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 160.0 GB | | 16-bit | 269.4 GB | | 8-bit | 134.7 GB | | 4-bit | 67.4 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About Qwen3.8-Flash-Next-P48NVFP4-MoESQ ### How much GPU memory does Qwen3.8-Flash-Next-P48NVFP4-MoESQ need? About 323.3 GB at 16-bit and 80.8 GB at 4-bit: the weights (134.7B parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run Qwen3.8-Flash-Next-P48NVFP4-MoESQ on? At 16-bit, 2x MI300X from $3.70 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### What license is Qwen3.8-Flash-Next-P48NVFP4-MoESQ released under? other, as its publisher declares it. Read the license text before commercial use. ### What is Qwen3.8-Flash-Next-P48NVFP4-MoESQ's context length? 262,144 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Text generation ### [keys-GLM-5.3-EXL3-2.75BPW](https://savrn.com/models/keys-glm-5-3-exl3-2-75bpw) [Keys](https://savrn.com/model-publishers/drowzeys) Full GLM-5.3 (753B, glmmoedsa) quantized to EXL3 with a per-expert mixed bit width averaging 2.75 bpw on the routed experts, sized to serve on four NVIDIA DGX Sparks (GB10, 128 GB unified memory each) with room for a 200K-token KV pool (or 1M tokens with decode-context-parallel 4). 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Do not swap… Access requested at publisher mit 119B parameters vllm [View model](https://savrn.com/models/keys-mimo-v2-6-pro-rl-jarrelscy-arvq-abliterated) ## IST Austria Distributed Algorithms and Systems Lab [All models and datasets](https://savrn.com/model-publishers/ista-daslab) ## Versions - [b25abf193ab0](https://savrn.com/models/qwen3-8-flash-next-p48nvfp4-moesq/versions/b25abf193ab0) · current 2026-10-04 ## Explore More - [All text generation models](https://savrn.com/models/tasks/text-generation) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-04. - [Hugging Face record](https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-P48NVFP4-MoESQ) - [How the hub is built](https://savrn.com/model-hub/methodology)