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Meta Llama 3.1

Meta's flagship 8B model with 128K context. Best-in-class for local deployment.

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Meta Llama 3.1

Meta's frontier 70B model. Requires 40GB+ VRAM; dual 3090 or M2 Ultra.

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Meta Llama 3.2

Tiny but capable. Runs on 4GB VRAM or 8GB RAM, even on phones via llama.cpp.

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Alibaba Qwen2.5

Alibaba's highly optimized 7B. Punches well above its weight, especially in coding.

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Alibaba Qwen2.5

The sweet spot between performance and resource usage. 16GB VRAM with Q4.

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Alibaba Qwen2.5

Near-GPT-4 reasoning on a 24GB VRAM card (Q4_K_S). Groundbreaking value.

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DeepSeek

MoE architecture coding model. Active params ~2.4B, total ~16B. Exceptional code quality.

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Microsoft Phi

Microsoft's tiny powerhouse. Best 4B model for on-device deployment.

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Mistral AI

Mistral + NVIDIA collaboration. 128K context, excellent multilingual support.

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Google Gemma 2

Google's compact Gemma 2 with sliding window attention. Punches above 9B.

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Alibaba Qwen2.5

Flagship Qwen2.5. Requires dual 4090 or A100 80G. Exceptional reasoning at scale.

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DeepSeek

R1 reasoning distilled into 14B. Huge community interest; excellent chain-of-thought.

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Meta Llama 3.3

Latest Meta 70B with improved multilingual. Drop-in upgrade from Llama 3.1 70B.

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Mistral AI

Mistral's efficient 24B. Strong multilingual; fits on 24GB with Q4.

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Alibaba Qwen2.5

Top-tier open coding model. HumanEval competitive with GPT-4o on 32B scale.

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Alibaba Qwen2.5

Best 7B coding model. Ideal for local dev assistants on 8–16GB VRAM.

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Alibaba Qwen2.5

Tiny Qwen2.5 for edge devices. Runs on 4GB VRAM or Raspberry Pi class hardware.

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Meta Llama 3.2

Ultra-light Llama for mobile and embedded. Sub-2GB VRAM with Q4.

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DeepSeek

R1 reasoning in Llama 70B architecture. Top open reasoning model for dual-GPU setups.

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Mistral AI

Mistral's dedicated code model. 80+ language support, Fill-in-the-Middle capable.

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Mistral AI

Classic MoE model. ~13B active params per token; needs 32GB+ VRAM for Q4.

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Cohere

Cohere's RAG-optimised model. Excellent retrieval-augmented generation.

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01.AI Yi

Prefer Qwen3 32B Instruct

01.AI's strong bilingual (EN/ZH) model. Competitive with Qwen 32B.

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Upstage

Prefer Qwen3 14B Instruct

Depth-upscaled 10.7B punching above weight. Strong on reasoning benchmarks.

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BigCode

BigCode's open code model trained on 600+ languages. Great for polyglot dev.

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Meta Llama 3.2

Multimodal Llama with image understanding. Vision encoder adds ~2GB VRAM overhead.

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Alibaba Qwen2

Prefer Qwen3-VL 8B Instruct

Vision-language model with video understanding. Strong OCR and chart reading.

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NousResearch

Fine-tuned Llama 3.1 8B with improved roleplay and instruction following.

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Microsoft / WizardLM

Prefer Qwen3 8B Instruct

Evol-Instruct fine-tuned Mistral-based 7B. Strong complex instruction handling.

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IBM Granite

IBM's enterprise-grade 8B. Strong RAG and tool-use; permissive Apache 2.0 license.

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Google Gemma 2

Ultra-compact Gemma 2. Runs on 4GB VRAM; great for edge prototyping.

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Google Gemma 2

Largest open Gemma 2. Strong reasoning; needs 24GB+ VRAM at Q4.

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Alibaba Qwen2.5

Smallest Qwen2.5. Ideal for Raspberry Pi, phones, and ultra-low-latency demos.

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Alibaba Qwen2.5

Tiny Qwen with 128K context. Surprisingly capable for summarisation and chat.

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Microsoft Phi

Microsoft's mid-size Phi-3. Excellent quality-per-GB on 16GB cards.

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Microsoft Phi

Latest Phi mini with improved math and code. Strong 4B-class performer.

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Mistral AI

Classic Mistral 7B v0.3. Still a reliable baseline for local chat APIs.

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DeepSeek

MoE general model (~2.4B active). Long context and strong multilingual chat.

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DeepSeek

R1 reasoning in a 7B footprint. Best value for 8–12GB VRAM CoT experiments.

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DeepSeek

R1 distilled to 32B. Near-frontier reasoning on a single 24GB card (Q3/Q4).

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Meta Llama 3.2

Flagship multimodal Llama. Requires dual 4090 or A100; vision adds ~3GB overhead.

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Allen AI OLMo

Fully open training pipeline from Allen AI. Great for reproducibility research.

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Shanghai AI Lab

Strong bilingual (EN/ZH) 7B from Shanghai AI Lab. Competitive with Qwen 7B.

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Shanghai AI Lab

Mid-size InternLM2 with excellent Chinese comprehension. Fits 24GB at Q4.

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Cohere For AI

Multilingual specialist covering 23 languages. Strong for non-English local apps.

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OpenChat

Prefer Qwen3 8B Instruct

C-RLFT fine-tuned Llama 3.1 8B. Known for natural conversational tone.

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HuggingFaceH4

Prefer Qwen3 8B Instruct

DPO-aligned Mistral 7B. Classic choice for helpful, harmless chat baselines.

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Stability AI

Stability AI's 12B chat model. Solid general-purpose option for 16GB GPUs.

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TII UAE

Technology Innovation Institute's latest Falcon. Good multilingual and code mix.

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AI21 Labs

Hybrid SSM-Transformer with 256K context. Efficient long-document QA on 16GB.

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Databricks

MoE flagship (~36B active). Needs multi-GPU; strong code and reasoning at scale.

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Alibaba Qwen3

Latest Qwen3 dense 8B with thinking mode. Strong upgrade from Qwen2.5 7B for local deploy.

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Alibaba Qwen3

Qwen3 14B — best balance of reasoning and VRAM in the 2026 Qwen lineup.

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Google Gemma 3

Google Gemma 3 multimodal 4B. 128K context; strong vision + text on 8GB cards.

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Google Gemma 3

Mid-size Gemma 3 with vision. Fits 16GB at Q4; excellent multilingual chat.

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Meta Llama 4

Meta Llama 4 Scout MoE (17B active / 109B total). Multimodal; needs ~68GB VRAM at Q4_K_M.

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Meta Llama 4

Llama 4 Maverick flagship MoE (17B active / 400B total). Multi-GPU or H100 cluster territory.

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Meta Llama 3.1

Meta frontier dense 405B. Q4 needs ~230GB+ VRAM; dual H100 80G or 8× consumer GPU.

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Alibaba Qwen3

Qwen3 dense 32B — successor to Qwen2.5-32B with stronger reasoning and thinking mode.

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Alibaba Qwen3

Qwen3 MoE with only 3B active params. Q4 ~19GB file; outperforms QwQ-32B on 16GB cards.

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Alibaba Qwen3

Qwen3 flagship MoE (22B active / 235B total). Q4_K_M ~142GB; rivals DeepSeek-R1 class models.

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DeepSeek

DeepSeek-V3 frontier MoE (~37B active / 671B total). MLA + FP8; multi-node GPU cluster required at Q4.

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DeepSeek

DeepSeek-R1 reasoning model built on V3 MoE. Chain-of-thought at frontier scale — use distill variants for local GPUs.

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Alibaba Qwen3

Smallest Qwen3 dense with thinking mode. Q4 ~3.2GB — ideal for 8GB GPUs and edge devices.

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Alibaba Qwen3

Agentic coding MoE with 3.3B active params and 256K native context. Top open coder for 16–24GB cards.

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Mistral AI

Mistral 3 flagship MoE (41B active / 675B total) with vision encoder. FP8 on 8×H200; GGUF quant for research clusters only.

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Zhipu GLM-4

Zhipu GLM-4 open 9B with 128K context, tool calling, and strong bilingual (EN/ZH) performance.

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Google Gemma 3

Gemma 3 large instruct with long context and multimodal support. Q4 ~16GB — dual-GPU or 24GB card with short ctx.

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DeepSeek

R1 reasoning distilled into Llama 3.1 8B. Best chain-of-thought for 8–12GB cards; huge community GGUF support.

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Microsoft Phi

Microsoft Phi-4 dense 14B — strong reasoning for size. Q4 ~9GB fits 12GB cards with moderate context.

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Alibaba Qwen3

Tiny Qwen3 with thinking mode. Q4 ~1.4GB — phones, NUC, and always-on local agents.

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OpenAI GPT-OSS

OpenAI open-weight MoE (21B total / 3.6B active), shipped natively in MXFP4 — ~12.8GB runs on a 16GB card with no quality tax. Only 3.6B active params means CPU-offload stays usable.

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OpenAI GPT-OSS

The big GPT-OSS (117B total / 5.1B active). Native MXFP4 checkpoint is ~61GB — fits one 80GB card or a 128GB unified-memory Mac. Partial offload on 24GB consumer cards is slow but works.

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Zhipu GLM-4.5

Zhipu's agentic/reasoning MoE (106B total / 12B active). Q4 ~64GB — the sweet spot is a 96GB+ unified Mac or 2× 48GB cards. Strong tool-calling for its class.

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Mistral Devstral

Mistral + All Hands agentic coding model on a Mistral Small 3.1 base. Built for repo-scale tool use rather than single-file completion. Q4 ~14GB fits a 16GB card.

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Alibaba Qwen3-VL

Current-generation vision-language model that still fits a single 8–12GB card at Q4 (~5.9GB). The realistic multimodal option for people without a 24GB GPU — note the vision encoder adds VRAM the KV-cache math below does not model.

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Alibaba Qwen3-VL

Multimodal MoE with only ~3B active parameters, so it stays responsive on Apple unified memory and survives CPU offload far better than a dense 30B. Q4 ~19GB — a 24GB card holds it outright.

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Mistral Magistral

Mistral's reasoning model on a Mistral Small 3.2 base, with reasoning wrapped in [THINK] tags. Q4 ~14GB puts explicit chain-of-thought on a 16GB card — the level below the 70B-class reasoners most guides assume.

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ByteDance Seed

Dense 36B with a native 512K context. Q4 weights are ~22GB, which lands on a 24GB card or a 2×16GB split — but the context is the real cost: 512K of KV cache is roughly 128GB on its own, so budget context first and weights second.

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