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qwen3.5
Alibaba Qwen
Qwen3.5: Qwen's 2026 generation, 2B to 35B-A3B, with a 256K native context and strong coding for a general model.
- size
- 27B
- vram
- ~18 GB
- license
- Apache-2.0
75% of a 24 GB card
#chat #code #reasoning
magistral-small-2509
Mistral AI
24B reasoning model built on Mistral Small 3.2 with SFT from Magistral Medium traces and RL; 128K context window.
- size
- 24B
- vram
- ~16 GB
- license
- Apache-2.0
67% of a 24 GB card
#reasoning
seed-oss-36b-instruct
ByteDance Seed
ByteDance Seed open-source LLM for long-context, reasoning and agent tasks, with flexible thinking-budget control and native 512K context.
- size
- 36B
- vram
- ~23 GB
- license
- Apache-2.0
96% of a 24 GB card
#reasoning #large
nemotron-nano-9b-v2
NVIDIA
9B hybrid Mamba-2/Transformer model for reasoning and non-reasoning tasks; reasoning controllable via system prompt; up to 128K context.
- size
- 9B
- vram
- ~8 GB
- license
- NVIDIA Open Model License
33% of a 24 GB card
#reasoning
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
qwen3-4b-instruct-2507
Alibaba Qwen
Updated Qwen3-4B non-thinking model with better instruction following, reasoning and coding; 256K native context, no think blocks.
- size
- 4B
- vram
- ~4 GB
- license
- Apache-2.0
17% of a 24 GB card
#chat #instruct #small
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
qwen3-30b-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
smollm3
Hugging Face
SmolLM3 3B: a fully open small model with switchable reasoning, multilingual output and long context. Its training recipe is published too.
- size
- 3B
- vram
- ~2.5 GB
- license
- Apache-2.0
10% of a 24 GB card
#small #reasoning #multilingual
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
phi-4-reasoning-plus
Microsoft
14B dense reasoning model fine-tuned from Phi-4 with chain-of-thought SFT plus reinforcement learning; 32K context.
- size
- 14B
- vram
- ~11 GB
- license
- MIT
46% of a 24 GB card
#reasoning
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
seed-coder
ByteDance Seed
Seed-Coder 8B: a strong small coding model, with a separate reasoning variant. MIT licensed.
- size
- 8B
- vram
- ~7 GB
- license
- MIT
29% of a 24 GB card
#code #small #reasoning
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
olympiccoder
Hugging Face (open-r1)
OlympicCoder 32B: a reasoning model trained for competitive programming, from Hugging Face's open-r1 project.
- size
- 32B
- vram
- ~22 GB
- license
- Apache-2.0
92% of a 24 GB card
#code #reasoning #competitive
phi4-mini
Microsoft
Phi-4-mini 3.8B: strong maths and reasoning for its size, 128K context and function calling, under MIT.
- size
- 3.8B
- vram
- ~3 GB
- license
- MIT
13% of a 24 GB card
#small #math #tools
deepseek-r1
DeepSeek
Qwen2.5-Math-7B fine-tuned on reasoning data generated by DeepSeek-R1.
- size
- 7B
- vram
- ~6 GB
- license
- MIT
25% of a 24 GB card
#reasoning
deepseek-r1-distill-qwen-14b
DeepSeek
Qwen2.5-14B fine-tuned on reasoning data generated by DeepSeek-R1.
- size
- 14B
- vram
- ~10 GB
- license
- MIT
42% of a 24 GB card
#reasoning
deepseek-r1-distill-qwen-32b
DeepSeek
Qwen2.5-32B fine-tuned on DeepSeek-R1 reasoning data; card reports it outperforms OpenAI o1-mini across various benchmarks.
- size
- 32B
- vram
- ~21 GB
- license
- MIT
88% of a 24 GB card
#reasoning #large
deepseek-r1-distill-llama-8b
DeepSeek
Llama-3.1-8B fine-tuned on reasoning data generated by DeepSeek-R1.
- size
- 8B
- vram
- ~6 GB
- license
- MIT
25% of a 24 GB card
#reasoning
phi4
Microsoft
14B dense decoder-only Transformer trained on synthetic and filtered public data, built for high-quality reasoning in constrained environments.
- size
- 14B
- vram
- ~10 GB
- license
- MIT
42% of a 24 GB card
#chat #instruct
qwen2.5-coder-32b
Alibaba Qwen
Code-specific instruction-tuned 32B Qwen2.5-Coder model, improved at code generation, code reasoning and code fixing.
- size
- 32B
- vram
- ~21 GB
- license
- Apache-2.0
88% of a 24 GB card
#code #instruct #large
qwen2.5-coder-14b-instruct
Qwen
Instruction-tuned 14B code model with improved code generation, reasoning and fixing, and long-context support up to 128K tokens.
- size
- 14B
- vram
- ~10 GB
- license
- Apache-2.0
42% of a 24 GB card
#code #instruct
qwen2.5-coder
Alibaba Qwen
Code-specific instruction-tuned 7B Qwen2.5-Coder model, improved at code generation, code reasoning and code fixing.
- size
- 7B
- vram
- ~6 GB
- license
- Apache-2.0
25% of a 24 GB card
#code #instruct
hermes-3-llama-3-1-8b
Nous Research
Generalist Llama-3.1 8B fine-tune with ChatML format, function calling, structured output, roleplay and reasoning improvements.
- size
- 8B
- vram
- ~6 GB
- license
- Llama 3
25% of a 24 GB card
#chat #instruct