# gpt-oss-120b by OpenAI: Open-Weight Model
Source: https://savrn.com/models/gpt-oss-120b
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

---

## Runs On

What it takes to serve gpt-oss-120b (116.8B 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 | 233.7 GB | 280.4 GB | 1x [MI355X](https://savrn.com/ai-index/pricing/gpus/mi355x) (288 GB) Vultr | $2.59 | [2x MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) $3.70 · [2x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $4.00 |
| 8-bit | 116.8 GB | 140.2 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 | 58.4 GB | 70.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |

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 24, 2026.

[gpt-oss-120b on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/gpt-oss-120b/gpus)

## SAVRN's Notes on gpt-oss-120b

Memory decides the hardware. gpt-oss-120b carries 116.8 billion parameters; at 16-bit the working footprint is 280.4 GB, meaning one MI355X with 288 GB at $2.59 an hour. At 8-bit it drops to 140.2 GB and one MI300X with 192 GB at $1.85 an hour covers it, and 4-bit needs 70.1 GB on the same card. For general text generation with a 131,072-token window, we would start on the 8-bit single card.

Apache 2.0 permits commercial use, modification and redistribution, provided the license and copyright notices stay attached and significant changes are stated, plus an express patent grant. Before committing, read the model card at arXiv:2508.10925 and price the hosted route: on the Index, DeepInfra lists $0.037 in and $0.17 out per million tokens, Cerebras $0.35 and $0.75. Once a month of tokens at those rates costs more than an MI300X at $1.85 an hour, run it yourself.

SAVRN Research, 2026-09-18

## Model Card

By OpenAI, published under apache-2.0, revision b5c939de8f75.

[Try gpt-oss](https://gpt-oss.com) · [Guides](https://cookbook.openai.com/topic/gpt-oss) · [Model card](https://savrn.com/papers/gpt-oss-120b-gpt-oss-20b-model-card) · [OpenAI blog](https://openai.com/index/introducing-gpt-oss/)

Welcome to the gpt-oss series, [OpenAI’s open-weight models](https://openai.com/open-models) designed for powerful reasoning, agentic tasks, and versatile developer use cases.

We’re releasing two flavors of these open models: - gpt-oss-120b — for production, general purpose, high reasoning use cases that fit into a single 80GB GPU (like NVIDIA H100 or AMD MI300X) (117B parameters with 5.1B active parameters) - gpt-oss-20b — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)

Both models were trained on our [harmony response format](https://github.com/openai/harmony) and should only be used with the harmony format as it will not work correctly otherwise.

[!NOTE] This model card is dedicated to the larger gpt-oss-120b model. Check out [gpt-oss-20b](https://savrn.com/models/gpt-oss-20b) for the smaller model.

### Highlights

[Read the full model card (735 words)](https://savrn.com/models/gpt-oss-120b/card)

## Configuration

Architecture

GptOssForCausalLM

Context length (tokens)

131,072

Layers

36

Hidden size

2,880

Feed-forward size

2,880

Attention heads

64

Key/value heads

8

Head dimension

64

Vocabulary size

201,088

Experts

128

Experts active per token

4

Sliding window (tokens)

128

RoPE base

150,000

Model type

gpt_oss

Quantization

mxfp4

## Identity and Version

Repository

openai/gpt-oss-120b

Publisher

OpenAI

Task

Text generation

Modality

Text

Library

transformers

Parameters

116.8B parameters

Languages

Not stated by the source

Revision

b5c939de8f754692c1647ca79fbf85e8c1e70f8a

First published

2025-08-04

Last updated

2025-08-26

## Files and Weights

37 files, 195.8 GB in total. The weights are 23 files totalling 195.7 GB in bin, safetensors.

Weights23 files · 195.7 GB

Configuration7 files · 114.7 KB

Tokenizer2 files · 27.9 MB

Documentation2 files · 18.5 KB

Other2 files · 16.9 KB

Repository1 file · 1.6 KB

Every file

| File | Type | Size | SHA-256 |
| --- | --- | --- | --- |
| metal/model.bin | Weights | 65.2 GB | 0f3d5b8a213f |
| model-00000-of-00014.safetensors | Weights | 4.6 GB | 695218884684 |
| model-00001-of-00014.safetensors | Weights | 4.1 GB | a881aa5f561b |
| model-00002-of-00014.safetensors | Weights | 4.6 GB | 022478dd0439 |
| model-00003-of-00014.safetensors | Weights | 4.1 GB | 47aee9e7b9d5 |
| model-00004-of-00014.safetensors | Weights | 4.6 GB | f6c2752acda6 |
| model-00005-of-00014.safetensors | Weights | 4.1 GB | 0c8dd401544c |
| model-00006-of-00014.safetensors | Weights | 4.6 GB | 28d839f2e027 |
| model-00007-of-00014.safetensors | Weights | 4.1 GB | c8958c5f183c |
| model-00008-of-00014.safetensors | Weights | 4.6 GB | bf1f2a88868f |
| model-00009-of-00014.safetensors | Weights | 4.2 GB | f72d34a40042 |
| model-00010-of-00014.safetensors | Weights | 4.6 GB | f48c867c2cb0 |
| model-00011-of-00014.safetensors | Weights | 4.1 GB | a06851b2cfd3 |
| model-00012-of-00014.safetensors | Weights | 4.1 GB | 3af33667c307 |
| model-00013-of-00014.safetensors | Weights | 4.6 GB | bcbcb74b043e |
| model-00014-of-00014.safetensors | Weights | 4.1 GB | 54b1be160969 |
| original/model--00001-of-00007.safetensors | Weights | 10.5 GB | 68a8dc1f8e2e |
| original/model--00002-of-00007.safetensors | Weights | 10.5 GB | 19b8f0d5c7dc |
| original/model--00003-of-00007.safetensors | Weights | 10.5 GB | 0dbccd746d50 |
| original/model--00004-of-00007.safetensors | Weights | 10.5 GB | bcc73cf6d18f |
| original/model--00005-of-00007.safetensors | Weights | 10.5 GB | 15fd69843e9c |
| original/model--00006-of-00007.safetensors | Weights | 10.4 GB | 3aedef2ee0a5 |
| original/model--00007-of-00007.safetensors | Weights | 2.3 GB | 20d5dfcad1ed |
| config.json | Configuration | 2.1 KB | — |
| generation_config.json | Configuration | 177 B | — |
| model.safetensors.index.json | Configuration | 54.5 KB | — |
| original/config.json | Configuration | 377 B | — |
| original/dtypes.json | Configuration | 19.7 KB | — |
| original/model.safetensors.index.json | Configuration | 37.8 KB | — |
| special_tokens_map.json | Configuration | 98 B | — |
| LICENSE | Documentation | 11.4 KB | — |
| README.md | Documentation | 7.1 KB | — |
| USAGE_POLICY | Other | 201 B | — |
| chat_template.jinja | Other | 16.7 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
| tokenizer.json | Tokenizer | 27.9 MB | 0614fe83cada |
| tokenizer_config.json | Tokenizer | 4.2 KB | — |

## License and Download

License

apache-2.0

Access

Open weights, no gate

Download size

195.7 GB

[Download from OpenAI](https://huggingface.co/openai/gpt-oss-120b)

Released by OpenAI through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0).

## Built From

- Described by [arXiv:2508.10925](https://savrn.com/papers/gpt-oss-120b-gpt-oss-20b-model-card)

## Evaluations

Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical.

| Benchmark | Conditions | Result | Reported by | Revision | Date |
| --- | --- | --- | --- | --- | --- |
| Idavidrein/gpqa | Task diamondMetric diamondSetup GPQA DiamondComparison conditions not established | 80.8081 | [EvalEval](https://huggingface.co/datasets/evaleval/EEE_datastore/blob/192329fb7d6b15b7b0936a1a58ae862aa7e8ba24/flat/objects/5d/3e/5d3e8832-c770-4796-957b-dfaefbd3bb2a.json) Reported by a third party | Evaluated revision not stated | 2026-04-16 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: mediumComparison conditions not established | 73.1 | [GPT-OSS Model Card](https://arxiv.org/abs/2508.10925) Reported by a third party | Evaluated revision not stated | 2025-08-05 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: high, With toolsComparison conditions not established | 80.9 | [GPT-OSS Model Card](https://arxiv.org/abs/2508.10925) Reported by a third party | Evaluated revision not stated | 2025-08-05 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: highComparison conditions not established | 80.1 | [GPT-OSS Model Card](https://arxiv.org/abs/2508.10925) Reported by a third party | Evaluated revision not stated | 2025-08-05 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: low, With toolsComparison conditions not established | 68.1 | [GPT-OSS Model Card](https://arxiv.org/abs/2508.10925) Reported by a third party | Evaluated revision not stated | 2025-08-05 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: medium, With toolsComparison conditions not established | 73.5 | [GPT-OSS Model Card](https://arxiv.org/abs/2508.10925) Reported by a third party | Evaluated revision not stated | 2025-08-05 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: lowComparison conditions not established | 67.1 | [GPT-OSS Model Card](https://arxiv.org/abs/2508.10925) Reported by a third party | Evaluated revision not stated | 2025-08-05 |
| LEXam-Benchmark/LEXam | Task mcq_4_choicesMetric mcq_4_choicesComparison conditions not established | 47.71 | LEXam Leaderboard Reported by a third party | Evaluated revision not stated | 2026-06-02 |
| LEXam-Benchmark/LEXam | Task open_questionMetric open_questionComparison conditions not established | 51.74 | LEXam Leaderboard Reported by a third party | Evaluated revision not stated | 2026-06-02 |
| SWE-bench/SWE-bench_Verified | Task swe_bench_%_resolvedMetric swe_bench_%_resolvedSetup Reasoning: lowComparison conditions not established | 47.9 | [GPT-OSS Model Card](https://arxiv.org/abs/2508.10925) Reported by a third party | Evaluated revision not stated | 2025-08-05 |
| SWE-bench/SWE-bench_Verified | Task swe_bench_%_resolvedMetric swe_bench_%_resolvedSetup Reasoning: mediumComparison conditions not established | 52.6 | [GPT-OSS Model Card](https://arxiv.org/abs/2508.10925) Reported by a third party | Evaluated revision not stated | 2025-08-05 |
| SWE-bench/SWE-bench_Verified | Task swe_bench_%_resolvedMetric swe_bench_%_resolvedSetup Reasoning: highComparison conditions not established | 62.4 | [GPT-OSS Model Card](https://arxiv.org/abs/2508.10925) Reported by a third party | Evaluated revision not stated | 2025-08-05 |
| ScaleAI/SWE-bench_Pro | Task SWE_Bench_ProMetric SWE_Bench_ProComparison conditions not established | 16.2 | SWE-Bench Pro official evaluation results Reported by a third party | Evaluated revision not stated | 2026-02-28 |
| TIGER-Lab/MMLU-Pro | Task mmlu_proMetric mmlu_proComparison conditions not established | 80.8 | [EvalEval](https://huggingface.co/datasets/evaleval/EEE_datastore/blob/192329fb7d6b15b7b0936a1a58ae862aa7e8ba24/flat/objects/e4/8d/e48dbb1a-9e1a-4c99-abbf-cb32942b48e7.json) Reported by a third party | Evaluated revision not stated | 2026-06-30 |
| cais/hle | Task hleMetric hleSetup Reasoning: lowComparison conditions not established | 5.2 | [GPT-OSS Model Card](https://arxiv.org/abs/2508.10925) Reported by a third party | Evaluated revision not stated | 2025-08-05 |
| cais/hle | Task hleMetric hleSetup Reasoning: medium, With toolsComparison conditions not established | 11.3 | [GPT-OSS Model Card](https://arxiv.org/abs/2508.10925) Reported by a third party | Evaluated revision not stated | 2025-08-05 |
| cais/hle | Task hleMetric hleSetup Reasoning: mediumComparison conditions not established | 8.6 | [GPT-OSS Model Card](https://arxiv.org/abs/2508.10925) Reported by a third party | Evaluated revision not stated | 2025-08-05 |
| cais/hle | Task hleMetric hleSetup Reasoning: highComparison conditions not established | 14.9 | [GPT-OSS Model Card](https://arxiv.org/abs/2508.10925) Reported by a third party | Evaluated revision not stated | 2025-08-05 |
| cais/hle | Task hleMetric hleSetup Reasoning: high, With toolsComparison conditions not established | 19 | [GPT-OSS Model Card](https://arxiv.org/abs/2508.10925) Reported by a third party | Evaluated revision not stated | 2025-08-05 |
| cais/hle | Task hleMetric hleSetup Reasoning: low, With toolsComparison conditions not established | 9.1 | [GPT-OSS Model Card](https://arxiv.org/abs/2508.10925) Reported by a third party | Evaluated revision not stated | 2025-08-05 |
| joelniklaus/LEXam-hard | Task lexam_hardMetric lexam_hardSetup lighteval, LEXam paper prompts, one response per question, no tools; DeepSeek-R1-0528 judge; mean of the German and English means over the 518 questions, 0-100Comparison conditions not established | 37.55 | [SwissLegalEvals per-sample details (lighteval)](https://huggingface.co/buckets/joelniklaus/SwissLegalEvals) Reported by a third party | Evaluated revision not stated | 2026-06-11 |
| zero-trust-physics/zero-trust-physics-environments | Task correctMetric correctSetup tools: the Zero-Trust Physics verifier over MCP and nothing else (every other harness tool refused before it ran); Antigravity CLI 1.4.0, gpt-oss-120b at medium reasoning; one fixed system line; 20 verifier calls per task; scored by the dataset's score.py on engine 0.14.0; percent of 153…Comparison conditions not established | 26.14 | [Zero-Trust Physics Environments 0.3, per-task replies and traces](https://huggingface.co/datasets/zero-trust-physics/zero-trust-physics-environments/tree/main/results/agy_gpt-oss-120b-medium) Reported by a third party | Evaluated revision not stated | 2026-10-17 |
| zero-trust-physics/zero-trust-physics-environments | Task kept_to_the_toolMetric kept_to_the_toolSetup tools: the Zero-Trust Physics verifier over MCP and nothing else (every other harness tool refused before it ran); Antigravity CLI 1.4.0, gpt-oss-120b at medium reasoning; one fixed system line; 20 verifier calls per task; scored by the dataset's score.py on engine 0.14.0; percent of 153…Comparison conditions not established | 45.75 | [Zero-Trust Physics Environments 0.3, per-task replies and traces](https://huggingface.co/datasets/zero-trust-physics/zero-trust-physics-environments/tree/main/results/agy_gpt-oss-120b-medium) Reported by a third party | Evaluated revision not stated | 2026-10-17 |
| zero-trust-physics/zero-trust-physics-environments | Task rewardMetric rewardSetup tools: the Zero-Trust Physics verifier over MCP and nothing else (every other harness tool refused before it ran); Antigravity CLI 1.4.0, gpt-oss-120b at medium reasoning; one fixed system line; 20 verifier calls per task; scored by the dataset's score.py on engine 0.14.0; percent of 153…Comparison conditions not established | 23.53 | [Zero-Trust Physics Environments 0.3, per-task replies and traces](https://huggingface.co/datasets/zero-trust-physics/zero-trust-physics-environments/tree/main/results/agy_gpt-oss-120b-medium) Reported by a third party | Evaluated revision not stated | 2026-10-17 |

## Memory Requirements

| Precision | Weights in memory |
| --- | --- |
| As published | 195.7 GB |
| 16-bit | 233.7 GB |
| 8-bit | 116.8 GB |
| 4-bit | 58.4 GB |

Weights only, from the published parameter count; the key-value cache and runtime add to this.

## Hosted Prices

| Host | Input / output | Unit | Observed |
| --- | --- | --- | --- |
| Baseten | $0.10 / $0.50 | input / output, per million tokens | Oct 24, 2026 |
| Cerebras | $0.35 / $0.75 | input / output, per million tokens | Oct 24, 2026 |
| Crusoe | $0.05 / $0.20 | input / output, per million tokens | Oct 21, 2026 |
| DeepInfra | $0.037 / $0.17 | input / output, per million tokens | Oct 24, 2026 |
| Fireworks | $0.15 / $0.60 | input / output, per million tokens | Oct 23, 2026 |
| Fireworks | $0.15 / $0.60 | input / output, per million tokens | Oct 21, 2026 |
| Groq | $0.15 / $0.75 | input / output, per million tokens | Oct 24, 2026 |
| Nebius Token Factory | $0.15 / $0.60 | input / output, per million tokens | Oct 21, 2026 |
| Novita | $0.05 / $0.25 | input / output, per million tokens | Oct 24, 2026 |
| Nscale | $0.10 / $0.40 | input / output, per million tokens | Oct 24, 2026 |
| OVHcloud | $0.09 / $0.47 | input / output, per million tokens | Oct 24, 2026 |
| SambaNova | $0.22 / $0.59 | input / output, per million tokens | Oct 21, 2026 |
| Scaleway | $0.17 / $0.68 | input / output, per million tokens | Oct 24, 2026 |
| Together | $0.15 / $0.60 | input / output, per million tokens | Oct 21, 2026 |
| Together AI | $0.15 / $0.60 | input / output, per million tokens | Oct 21, 2026 |

From the [SAVRN Index](https://savrn.com/ai-index/pricing/open-models).

## Built on This Model

- Derived from[less-is-moe-gpt-oss-120b-gpqa-main-64-intdim-e-50](https://savrn.com/models/less-is-moe-gpt-oss-120b-gpqa-main-64-intdim-e-50)
- Adapter of[monitor_cot_for_hack-baseline-coding-explicit_intent-gpt-oss-120b](https://savrn.com/models/monitor-cot-for-hack-baseline-coding-explicit-intent-gpt-oss-120b)
- Derived from[monitor_cot_for_hack-baseline-coding-explicit_intent-gpt-oss-120b](https://savrn.com/models/monitor-cot-for-hack-baseline-coding-explicit-intent-gpt-oss-120b)
- Derived from[gpt-oss-120b-blackhole-fast](https://savrn.com/models/gpt-oss-120b-blackhole-fast)
- Adapter of[monitor_cot_for_hack-reward_description-judge-explicit_intent-gpt-oss-120b](https://savrn.com/models/monitor-cot-for-hack-reward-description-judge-explicit-intent-gpt-oss-120b)
- Derived from[monitor_cot_for_hack-reward_description-judge-explicit_intent-gpt-oss-120b](https://savrn.com/models/monitor-cot-for-hack-reward-description-judge-explicit-intent-gpt-oss-120b)

## Compare gpt-oss-120b

- [gpt-oss-120b vs NVIDIA-Nemotron-3-Super-120B-A12B-BF16](https://savrn.com/models/compare/gpt-oss-120b-vs-nvidia-nemotron-3-super-120b-a12b-bf16)
- [gpt-oss-120b vs Qwen-72B](https://savrn.com/models/compare/gpt-oss-120b-vs-qwen-72b)
- [DeepSeek-V4-Flash-DSpark vs gpt-oss-120b](https://savrn.com/models/compare/deepseek-v4-flash-dspark-vs-gpt-oss-120b)

## Questions About gpt-oss-120b

### How much GPU memory does gpt-oss-120b need?

About 280.4 GB at 16-bit and 70.1 GB at 4-bit: the weights (116.8B parameters) plus a working margin. A long context needs more.

### What is the cheapest GPU to run gpt-oss-120b on?

At 16-bit, 1x MI355X from $2.59 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

### Can I use gpt-oss-120b commercially?

Yes. gpt-oss-120b is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

### What is gpt-oss-120b's context length?

131,072 tokens, from the maximum position embeddings in its published configuration.

## Similar Models

Model · Text generation

### [keys-MiMo-V2.6-Pro-RL-Jarrelscy-ARVQ-Abliterated](https://savrn.com/models/keys-mimo-v2-6-pro-rl-jarrelscy-arvq-abliterated)

[Keys](https://savrn.com/model-publishers/drowzeys)

Abliterated Jarrelscy ARVQ / NVFP4 hybrid of XiaomiMiMo/MiMo-V2.6-Pro-RL. Thinking on/off is a request flag. Same weights. You choose per call. Thinking-off is the 100% gate. Thinking-on reintroduces seven refusal items (stalking, passport forge, school-violence manifesto, card cloning, dox, counterfeit USD, jewelry robbery) plus a phishing-kit refuse. Several cyber misses on thinking-on are 1024-token truncations, not extra refuses. Harmless probes stay clean in both modes. This model has had safety refusals removed. Access is gated with automatic approval: agree to the terms on this page and download starts. See RESPONSIBLEUSE.md. Xiaomi's chat template already supports both. 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)

Model · Text generation

### [Qwen3.5-397B-A17B-VQ-2.4bpw](https://savrn.com/models/qwen3-5-397b-a17b-vq-2-4bpw)

[Noah Zelezny](https://savrn.com/model-publishers/thedrainflorist)

95.7 GiB text weights — the daily driver, runs on a single 128 GB Mac. Also ships the bf16 vision tower (+0.85 GiB) and an optional MTP draft head (+5.4 GiB); full download 101.9 GiB. A vector-quantized build of Qwen3.5-397B-A17B that fits and generates on one 128 GB Apple Silicon machine — no cluster, no patches, stock mlx-lm. 12.43% of the 64-element weight groups in the Qwen3.5-397B teacher sit in output rows whose weights are about 1e-29. The vq-skipzero format drops those rows' codes and scales on disk and in memory; they output exact zeros, and the live rows are byte-identical to the previous revision (vqlab sz-check, every module). Text weights go from 107.96 to 95.66 GiB (-12.30…

Open weights apache-2.0 120.2B parameters 262,144 tokens mlx

[View model](https://savrn.com/models/qwen3-5-397b-a17b-vq-2-4bpw)

Model · Text generation

### [SamQuant-Qwen3.8-Flash-Next-Uncensored-MLX-Serve-Mix-C58](https://savrn.com/models/samquant-qwen3-8-flash-next-uncensored-mlx-serve-mix-c58)

[LOKSUN](https://savrn.com/model-publishers/loksun-158)

一个在 Apple M3 Max / 128GB 统一内存上手工量化出的 Qwen3.8-Flash-Next（qwen4exp 架构，180B 级 MoE：125B 主干 + 51B n-gram 表 + 4B MTP 投机头）的 mlx-serve 混合位宽包：常驻约 60GB，无审查源，带视觉塔。 - 基座模型：Qwen 官方 Qwen3.8-Flash-Next；原始权重：社区团队 orcarouter 的无审查 BF16 包（335.3 GiB / 131 分片，逐文件 sha256 验收 132/132 通过） - 量化工具链：mlx-serve 仓库自带产线（imatrix 校准采集 → 贪心位宽分配 → 混合宽度转换 → 留出窗口打分），感谢上游作者 - 本包作者做的四件事：用自己的 AI 会话日志做校准语料（三个 agent 工具的真实流量 + 官方通用语料，按内容 sha1 去重 26.11%，密钥/凭证脱敏）、逐层贪定位宽配方、跑完整产线、做质量与业务验收 - 卡片样式沿用 mlx-serve 生态包卡片惯例（模板参考 ddalcu 上游卡片），读数全部替换为 M3 Max 128GB 本机实测 整库约 92GB：权重与视觉塔 59.83GB（索引载荷口径，100 个编号分片 + model-vision.safetensors）+ 独立 n-gram 表 32,000,153,976 字节（ngramtable.bin）+ 9 个配置/文档文件。 生产运行口径（日常 DSH agent 会话，2026-10-06 17:24 起 51 小时）：…

Open weights other 111.1B parameters 262,144 tokens mlx-serve

[View model](https://savrn.com/models/samquant-qwen3-8-flash-next-uncensored-mlx-serve-mix-c58)

Model · Text generation

### [NVIDIA-Nemotron-3-Super-120B-A12B-BF16](https://savrn.com/models/nvidia-nemotron-3-super-120b-a12b-bf16)

[NVIDIA](https://savrn.com/model-publishers/nvidia)

For more details on how to deploy and use the model - see the Quick Start Guide below! The post-training data has a cutoff date of February 2026. The pre-training data has a cutoff date of June 2025. NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents. Nemotron-3-Super-120B-A12B-BF16 is a large language model (LLM) trained by NVIDIA, designed to deliver strong agentic, reasoning, and conversational capabilities. It is optimized for collaborative agents and high-volume workloads such as IT ticket automation. Like other models in the family, it responds to user queries and…

Open weights other 123.6B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/nvidia-nemotron-3-super-120b-a12b-bf16)

Model · Text generation

### [Qwen3.8-Flash-Next-125B-A5B-INT4-AutoRound](https://savrn.com/models/qwen3-8-flash-next-125b-a5b-int4-autoround)

[Aldo Zampatti](https://savrn.com/model-publishers/azampatti)

Qwen3.8-Flash-Next with 5 routed experts per token instead of 10, healed so it stays close to the original, quantized to int4. It runs on one DGX Spark (GB10, 128 GB) at roughly 64-70 tokens/s. 125B parameters in total, 4.8B active per token. The original activates 6B. Everything needed to serve it is in this one repository, including the 49 GB FP8 n-gram table under ple-table/. Nothing else to download. That builds the serving image, downloads this repository, and starts an OpenAI-compatible server on port 8000. The scripts and the full explanation are in that repo. Serving by hand needs Saren-Arterius/qwen3.8-Flash-DGX-AutoRound, because a stock vLLM cannot serve this checkpoint's int4 +…

Open weights other 124B parameters 262,144 tokens vllm

[View model](https://savrn.com/models/qwen3-8-flash-next-125b-a5b-int4-autoround)

Model · Text generation

### [Vinci-Cyber-123B-1.0](https://savrn.com/models/vinci-cyber-123b-1-0)

[SimpleDirect](https://savrn.com/model-publishers/simpledirect)

Vinci Cyber 123B 1.0 is an open-weight model for defensive infrastructure review and targeted remediation, fine-tuned in Canada from Mistral AI's Devstral 2 123B. The released merged weights have now been tested directly, alongside their parent and the available GGUF formats. Focused repairs. Restraint on correct configuration. Weights you can run yourself. On the V2-B neutral-review test, the released BF16 model preserved 24/24 correct configurations and produced 18/24 scanner-credited repairs, all 18 passing offline provider-schema validation. Its parent repaired 17/24 and preserved 0/24. On the second set, V2-A, Cyber again preserved 24/24, but repaired 9/24 versus the parent's 15/24.…

Open weights other 125B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/vinci-cyber-123b-1-0)

## OpenAI

[All models and datasets](https://savrn.com/model-publishers/openai)

## Versions

- [b5c939de8f75](https://savrn.com/models/gpt-oss-120b/versions/b5c939de8f75) · current 2026-10-24

## In Collections

- [Open-weight text models worth knowing](https://savrn.com/collections/open-weight-text-models-worth-knowing)

## Explore More

- [All text generation models](https://savrn.com/models/tasks/text-generation)
- [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0)
- [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 2026-10-19.
- [OpenAI website](https://github.com/openai)
- [Hugging Face record](https://huggingface.co/openai/gpt-oss-120b)
- [How the hub is built](https://savrn.com/model-hub/methodology)