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Models (11)

Reasoning model trained with reinforcement learning on top of DeepSeek V3-Base. MIT licence — even the weights are unrestricted, making R1 the most permissively-licensed frontier reasoning model. Generates long internal chains-of-thought before answering, trading latency for accuracy on math, code, and reasoning benchmarks. Distilled variants (e.g. R1 Distill Llama 70B) recover most of the quality at much smaller scales.

Context: 128K
License: mit
VRAM Q4: 402.6 GB

Llama 3.3 70B Instruct

70B

Meta's December 2024 refresh of Llama 3 70B that closes most of the gap with Llama 3.1 405B for chat workloads while remaining tractable on a single H100. Strong instruction following, robust tool-use behaviour, and a 128K context window make it the default choice for production chat at 70B scale. The 3.3 release was trained on a refreshed instruction-tuning data mix and benefits from Meta's most recent alignment work. It outperforms the much larger 3.1 405B on several reasoning benchmarks at a fraction of inference cost. The licence is the Llama 3 Community License, which permits commercial use unless your service exceeds 700M monthly active users. Good pick for: production chat at scale, RAG over long documents, agentic workflows where tool use matters, and any 70B-tier replacement for closed proprietary models.

Context: 128K
License: llama-3
VRAM Q4: 42 GB

DeepSeek R1 Distill Llama 70B

70B

R1 reasoning capabilities distilled into a Llama 3.3 70B base. The most accessible way to run R1-class reasoning locally — fits on a single H100 in fp16 or on a 4090 at Q4. Inherits Llama 3's community licence (commercial use under 700M MAU). Great pick for production reasoning workloads where the full R1 is too expensive to host but o1/R1-style quality is required.

Context: 128K
License: llama-3
VRAM Q4: 42 GB

Yi VL 34B

34B

Vision-language variant of Yi 34B. Image-text reasoning via an MLP adapter on a CLIP encoder. Useful for bilingual EN/中 multimodal workloads where the major Western vision-language models underperform on Chinese text in images.

Context: 4K
License: apache-2-0
VRAM Q4: 20.4 GB

QwQ 32B Preview

32B

Qwen's reasoning-focused 'thinking' model. Generates long chains-of-thought before answering, similar to OpenAI's o1 and DeepSeek R1 lineage. Optimised for math and competition-style problem solving. The Preview tag means Qwen is iterating quickly; later versions may obsolete this one. Useful today for math-heavy workloads where a slow, careful answer is preferred to a fast wrong one.

Context: 33K
License: apache-2-0
VRAM Q4: 19.2 GB

Qwen2.5 32B Instruct

32B

32B sweet-spot model: strong reasoning, fits on one H100 in fp16, on a 4090 at Q4. The 32B size in particular hits a quality/cost knee — quality scales with parameters faster than cost up to ~32B, and slower afterwards. Favoured for production chat where 7B isn't sharp enough and where 70B+ would over-spec the hardware budget. Apache 2.0 licence.

Context: 128K
License: apache-2-0
VRAM Q4: 19.2 GB

Phi-4 14B

14B

14B model trained primarily on synthetic data. Punches above its weight on reasoning, especially MATH and GPQA. MIT licensed. A standout choice when you want strong reasoning quality without paying 70B-tier hardware costs. Phi-4 in particular demonstrated that careful synthetic-data curation can extract frontier-class reasoning from a relatively small dense model.

Context: 16K
License: mit
VRAM Q4: 8.4 GB

Phi-3 Medium 14B

14B

Phi-3's mid-tier model with extended 128K context. MIT licence. Strong reasoning relative to its parameter count thanks to Microsoft's heavy investment in synthetic training data.

Context: 128K
License: mit
VRAM Q4: 8.4 GB

Falcon 3 7B Instruct

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

Qwen2.5 7B Instruct

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

Llama 3.2 3B

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

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