Models.
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qwen3-coder-next
Alibaba Qwen
Qwen3-Coder-Next: an 80B MoE with only 3B active, a 256K context, and training aimed at agent harnesses like Claude Code and Cline.
- size
- 80B-A3B
- vram
- ~52 GB
- license
- Apache-2.0
2.2× a 24 GB card
#code #agentic #moe
glm4.7-flash
Z.ai
GLM-4.7-Flash: a 30B-A3B MoE built for agentic coding. Only ~3B parameters active per token, so it runs at small-model speed.
- size
- 30B-A3B
- vram
- ~20 GB
- license
- MIT
83% of a 24 GB card
#code #agentic #moe
lfm2-8b-a1b
Liquid AI
Liquid AI on-device hybrid MoE with 8.3B total and 1.5B active parameters and a 32,768-token context.
- size
- 8B-A1B
- vram
- ~6 GB
- license
- LFM Open License v1.0
25% of a 24 GB card
#chat #instruct #moe
granite-4-0-h-tiny
IBM
IBM 7B-parameter (1B active) hybrid long-context instruct model with improved instruction following and tool calling.
- size
- 7B-A1B
- vram
- ~6 GB
- license
- Apache-2.0
25% of a 24 GB card
#chat #instruct #moe
glm4.6
Z.ai
GLM-4.6: Z.ai's flagship ~357B MoE for coding and agents, with a 200K context. Server- or big-workstation class.
- size
- 357B
- vram
- ~220 GB
- license
- MIT
9.2× a 24 GB card
#code #agentic #moe
gpt-oss
OpenAI
gpt-oss: OpenAI's open-weight reasoning models. 20B runs in ~16 GB, 120B on one 80 GB card. Apache-2.0, adjustable reasoning effort.
- size
- 20B
- vram
- ~14 GB
- license
- Apache-2.0
58% of a 24 GB card
#reasoning #tools #moe
gpt-oss-120b
OpenAI
OpenAI open-weight MoE reasoning model, 117B parameters with 5.1B active, configurable reasoning effort, MXFP4-trained MoE weights.
- size
- 117B-A5B
- vram
- ~64 GB
- license
- Apache-2.0
2.7× a 24 GB card
#reasoning #moe #large
qwen3-coder
Alibaba Qwen
Qwen3-Coder 30B-A3B: an agentic coding MoE with 256K context that runs at small-model speed on one GPU.
- size
- 30B-A3B
- vram
- ~19 GB
- license
- Apache-2.0
79% of a 24 GB card
#code #agentic #moe
qwen3-30b-a3b-thinking-2507
Alibaba Qwen
Qwen3-30B-A3B thinking-only update: 30.5B MoE with 3.3B active, deeper reasoning and 256K native context.
- size
- 30B-A3B
- vram
- ~20 GB
- license
- Apache-2.0
83% of a 24 GB card
#reasoning #moe #large
glm4.5-air
Z.ai
GLM-4.5-Air: a 106B MoE with 12B active, hybrid reasoning and strong agent and coding scores, under MIT. Workstation-class.
- size
- 106B-A12B
- vram
- ~64 GB
- license
- MIT
2.7× a 24 GB card
#moe #agentic #reasoning
qwen3-30b-a3b-instruct-2507
Alibaba Qwen
Qwen3-30B-A3B non-thinking update: 30.5B-parameter MoE with 3.3B active, 128 experts and 256K native context.
- size
- 30B-A3B
- vram
- ~20 GB
- license
- Apache-2.0
83% of a 24 GB card
#chat #instruct #moe
kimi-k2
Moonshot AI
Kimi K2: a 1-trillion-parameter MoE (32B active) tuned for agentic work. The top of the range: big servers, or very aggressive quants.
- size
- 1T-A32B
- vram
- ~250 GB
- license
- Modified MIT
10.4× a 24 GB card
#moe #agentic #code
ernie-4-5-21b-a3b
Baidu
Text MoE post-trained model with 21B total and 3B activated parameters per token and a 131072-token context length.
- size
- 21B-A3B
- vram
- ~15 GB
- license
- Apache-2.0
63% of a 24 GB card
#chat #instruct #moe
deepseek-r1-0528
DeepSeek
DeepSeek-R1-0528: the updated 671B reasoning model, plus an 8B distill on Qwen3 that runs on a gaming GPU. MIT.
- size
- 8B
- vram
- ~6 GB
- license
- MIT
25% of a 24 GB card
#reasoning #math #moe
qwen3
Alibaba Qwen
Qwen3 runs from 0.6B to 235B, dense and MoE, with thinking and non-thinking modes in one model. Apache-2.0 across the board.
- size
- 8B
- vram
- ~6 GB
- license
- Apache-2.0
25% of a 24 GB card
#chat #reasoning #tools
mixtral
Mistral AI
Mixtral 8x7B — a sparse mixture-of-experts model offering strong quality at high inference efficiency.
- size
- 8x7B
- vram
- ~28 GB
- license
- Apache-2.0
1.2× a 24 GB card
#moe #reasoning #instruct
llama4-scout
Meta
Llama 4 Scout: a natively multimodal 109B MoE (17B active, 16 experts) with an extremely long context window.
- size
- 109B-A17B
- vram
- ~64 GB
- license
- Llama 4 Community
2.7× a 24 GB card
#vision #moe #long-context
nomic-embed-text-v2-moe
Nomic AI
Multilingual mixture-of-experts text embedding model (475M total, 305M active) supporting about 100 languages with Matryoshka embeddings.
- size
- 475M
- vram
- ~2 GB
- license
- Apache-2.0
8% of a 24 GB card
#embedding #small
deepseek-coder-v2-lite
DeepSeek
DeepSeek-Coder-V2-Lite: a 16B mixture-of-experts coder with a long context. Cheap to run for its size.
- size
- 16B
- vram
- ~12 GB
- license
- DeepSeek License
50% of a 24 GB card
#code #moe #long-context