Size
3B – 13B
15 open-source models in this size bucket.
Joint Mistral × NVIDIA model with 128K context, designed as a drop-in upgrade to Mistral 7B. Trained with NVIDIA's Megatron stack and released under Apache 2.0. Strong multilingual coverage thanks to the Tekken tokenizer.
- Context
- 128K
- License
- apache-2-0
- VRAM Q4
- 7.2 GB
Stability AI's general-purpose 12B model. Apache 2.0. Useful default when you need a permissively-licensed 12B-scale model.
- Context
- 4K
- License
- apache-2-0
- VRAM Q4
- 7.2 GB
Llama 3's first vision-language model. Image understanding via a separately-trained ViT adapter bolted onto Llama 3 weights. Useful for OCR-adjacent workloads, document understanding, and image captioning at a permissive licence. The 11B size makes it cheap to host. Combined with the 128K text context, it handles long PDF-with-images workflows comfortably on a single 4090.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 6.6 GB
Mid-tier Gemma. Strong general-purpose chat model at small scale. The Gemma Terms of Use permit commercial use subject to Google's prohibited-use policy.
- Context
- 8K
- License
- gemma
- VRAM Q4
- 5.4 GB
NousResearch's community-driven fine-tune on the Llama 3.1 8B base. Tuned for strong tool use, function calling and steerable persona behaviour. Inherits Llama 3's community licence and its 128K context.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 4.8 GB
The workhorse 8B instruction-tuned model. Excellent quality-to-cost ratio and the broadest ecosystem support of any open-weights model — every major inference engine, fine-tuning library, and quantization toolchain has a 3.1 8B preset. Fits in 24 GB of VRAM at fp16, ~6 GB at Q4. Strong default for production chat where 70B is overkill, for fine-tuning on a specialist task, and for any workload where you want a known-good baseline.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 4.8 GB
The April 2025 refresh of Qwen at 8B. Native mixed-mode reasoning: the model can 'think' before answering when triggered, or answer directly for simple queries — configurable per request. Apache 2.0. A strong upgrade over Qwen 2.5 7B on math and code, with much better instruction following.
- Context
- 33K
- License
- apache-2-0
- VRAM Q4
- 4.8 GB
The original Mistral 7B refresh with 32K context and extended vocabulary. Permissive Apache 2.0 weights and the first widely-deployed sliding-window-attention model. Still useful in 2026 for very-low-cost inference and as a baseline for fine-tuning experiments.
- Context
- 33K
- License
- apache-2-0
- VRAM Q4
- 4.2 GB
Fully-open 7B model: weights, training data and code all released under permissive licences. Useful as a reference for reproducibility research and for teams that need full transparency on training data provenance.
- Context
- 4K
- License
- apache-2-0
- VRAM Q4
- 4.2 GB
The original 7B RLHF chat model. Historically important — the first widely-adopted commercially-usable open-weights chat model. Still cited as a baseline in most 2024–25 papers.
- Context
- 4K
- License
- llama-2
- VRAM Q4
- 4.2 GB
TII's latest dense 7B from December 2024. Strong scores on commonsense reasoning benchmarks. TII's Falcon licence permits royalty-free commercial use with attribution.
- Context
- 33K
- License
- falcon-2
- VRAM Q4
- 4.2 GB
Apache-2.0-licensed 7B model with surprisingly strong reasoning and multilingual chops. Qwen 2.5 trains on a larger and more carefully filtered corpus than the original Qwen series, and the 7B variant punches well above its weight on coding and math benchmarks. A strong default for cost-sensitive chat workloads and for fine-tuning experiments where the Apache licence simplifies downstream redistribution.
- Context
- 128K
- License
- apache-2-0
- VRAM Q4
- 4.2 GB
The first major open-weights state-space model. Linear-time decoding, no KV cache — memory usage stays flat as context grows, which makes it interesting for very long-context workloads. Falcon licence.
- Context
- 16K
- License
- falcon-2
- VRAM Q4
- 4.2 GB
Microsoft's flagship small-model demonstration: GPT-3.5-class on academic benchmarks at <4B parameters. The 4K context-window variant is the lightest; a 128K variant ships separately. MIT licensed, well-suited to on-device assistants and structured-extraction workloads where compactness matters more than absolute quality.
- Context
- 4K
- License
- mit
- VRAM Q4
- 2.3 GB
Pocket-sized Llama 3 variant for edge deployment. Surprising chat quality after instruction tuning makes it competitive with much larger models from a previous generation. At Q4 it fits in ~2 GB of VRAM and runs on consumer GPUs and recent Apple Silicon. A strong default for on-device chat, summarisation, and structured extraction tasks where the workload doesn't need frontier reasoning quality.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 1.8 GB