Can your GPU run it?

The current default answer to "what should I run on a 24GB card?" Native vision, 3B-active MoE speed, and quality that embarrassed models twice its size at launch. This is the model selling RTX 3090s in 2026.

Alibaba · 35B MoE (3B active)

The newest dense Qwen — 262k context, "thinking preservation" across turns, and agentic-coding chops. Dense means predictable quality at long context where sparse models sometimes wobble.

Alibaba · 27B params

The 8GB-card staple: hybrid thinking/non-thinking modes and quality that made Llama-class small models look old. The Qwen3.5 small line (9B/4B/2B) is newer, but this remains the proven default.

Alibaba · 8.2B params

The sweet spot for 12GB GPUs: noticeably smarter than 8B models while still fitting a Q4 quant on an RTX 3060 or 5070 with room for context.

Alibaba · 14.8B params

The model that made 24GB the enthusiast standard. Newer Qwen3.5/3.6 releases have taken the crown, but the dense 32B remains a rock-solid choice with a massive fine-tune ecosystem.

Alibaba · 32.8B params

The MoE that started the "30B quality at 3B speed" era and is still one of Ollama's most-pulled models. Qwen3.5-35B-A3B is its direct upgrade, but this one has a year of community tooling behind it.

Alibaba · 30.5B MoE (3.3B active)

Google's biggest open model yet, and its first under Apache 2.0. Vision and video understanding built in, 256k context, and a top-3 LMArena debut. On a 24GB card this is the premium dense choice.

Google · 31B params

The 16GB-card favorite of 2026: Google's first MoE Gemma pairs 26B of knowledge with 4B-active speed. If you own an RTX 5060 Ti 16GB or RX 9070 XT, this is probably your daily driver.

Google · 26B MoE (4B active)

The previous-gen Google mid-sizer with vision built in. Gemma 4 supersedes it, but this remains a well-tested, friendly assistant model that fits 12GB cards with ease.

Google · 12B params

The 2025 Gemma flagship — exceptionally natural writing tone and vision input on a 24GB card. Gemma 4 31B is the upgrade path, but the 27B's prose style still has fans.

Google · 27B params

Still the #1 most-pulled model on Ollama by sheer inertia — every tutorial, every tool, every fine-tune pipeline supports it. Newer 8B-class models beat it on quality, but nothing beats its ecosystem.

Meta · 8B params

GPT-4-class quality in a dense 70B you can own — if you have ~44GB of VRAM. Modern MoE models deliver similar quality in a third of the memory, but the 70B remains the classic dual-3090 flex.

Meta · 70B params

Meta's 109B MoE with an extreme context window. The community has largely moved to Qwen and GLM — and Meta has pivoted away from open releases — but Scout still earns its keep on unified-memory boxes.

Meta · 109B MoE (17B active)

Mistral Small 4 (a 119B MoE) has technically replaced it, but the dense 24B still fits 16GB cards far better — which keeps it the pragmatic European pick for local business use. Vision and function calling included.

Mistral AI · 24B params

Mistral's one-model-to-rule-them-all for 2026: chat, reasoning with configurable effort, vision, and coding folded into a 119B MoE with just 6B active. "Small" is marketing — this wants unified memory or dual GPUs.

Mistral AI · 119B MoE (6B active)

A year old and still the mid-size local favorite — because Zhipu never shipped an Air variant of GLM-5. Agent-first training, MIT license, and 3-bit quants that squeeze onto 64GB Macs keep it beloved.

Zhipu AI · 106B MoE (12B active)

A trillion-parameter open model with native multimodality and agent-swarm tricks (its K2.6 sibling ties frontier closed models on SWE-Bench). Consumer hardware need not apply — but the weights are right there.

Moonshot AI · 1000B MoE (32B active)

The 2025 open flagship, still run on 192GB Macs and multi-3090 rigs. Included so you can see exactly why it doesn't fit your GPU — and what class of hardware it actually wants.

Alibaba · 235B MoE (22B active)

OpenAI's open-weight reasoner, shipped natively in 4-bit (MXFP4) so the whole thing fits in ~13GB. Adjustable reasoning effort and real tool-use training make it a superb local agent brain on 16GB cards.

OpenAI · 21B MoE (3.6B active)

o4-mini-class reasoning you can self-host. Ships in MXFP4 at ~62GB — beyond any single consumer GPU, but only 5.1B active params make it the staple workload for 128GB Strix Halo mini-PCs and Macs.

OpenAI · 117B MoE (5.1B active)

Microsoft's synthetic-data special: 14B parameters that still punch above their weight on math and STEM. Aging now (and no Phi-5 has shipped), but MIT licensing and 12GB-friendliness keep it relevant.

Microsoft · 14.7B params

R1's chain-of-thought distilled into a dense 32B that fits a 24GB card — still the #2 most-pulled family on Ollama. Newer reasoners exist, but "deepseek-r1:32b" remains the reflex install for local reasoning.

DeepSeek · 32.8B params

The model that made reasoning open. The full 671B needs server hardware (~400GB+), and its lineage now lives on in DeepSeek V4 — but this page exists because people still ask. Run a distill instead.

DeepSeek · 671B MoE (37B active)

The "small" half of DeepSeek's V4 launch (R2 never shipped — this is what became of it): 284B MoE with 1M context under MIT. Local runner support is still maturing; today it's a 128GB-unified-memory-and-up workload.

DeepSeek · 284B MoE (13B active)

The #1-ranked open-weight model in the world as of mid-2026 (its 5.2 refresh tops the Artificial Analysis index). At 744B parameters it is emphatically not a consumer workload — this page shows what it would take.

Zhipu AI · 744B MoE (40B active)

Still the local-coding default a year after release: agentic-coding trained, 256k context for real repos, and MoE speed that keeps autocomplete snappy. No Qwen3.5/3.6 Coder exists yet — this is current.

Alibaba · 30.5B MoE (3.3B active)

The new hotness for serious local coding rigs: 80B of coding knowledge routed through 3B active params and hybrid attention that keeps the KV cache tiny. Built for dual-24GB and 128GB-unified-memory boxes.

Alibaba · 80B MoE (3B active)

Purpose-built for agentic coding — exploring repos, editing multiple files, driving tools — in a dense 24B that fits a single 16–24GB card. The open coding agent you can actually afford to run.

Mistral AI · 24B params

The open image-quality king: 32B parameters, native 4-megapixel output, and prompt adherence that rivals closed services. It demands a 24GB card for comfort — the model that makes RTX 5090s tempting.

Black Forest Labs · 32B params

The commercial-safe FLUX: Apache 2.0, 4-step distilled speed, image editing and multi-reference support — on an 8GB card. For most people this is the image model to actually use in 2026.

Black Forest Labs · 4B params

The speed king of local image generation: 1024px images in ~2–3 seconds on a good card, quality far above its 6B size, and Apache 2.0. The best thing to happen to low-VRAM image gen since SDXL.

Tongyi (Alibaba) · 6B params

The text-rendering champion — no open model draws signage, UI mockups, or CJK typography better. 20B MMDiT with a serious editing variant, all under Apache 2.0.

Alibaba · 20B params

The model that dethroned Stable Diffusion, now succeeded by FLUX.2 — but its enormous LoRA and workflow ecosystem keeps it the most-used FLUX in ComfyUI. Still a great reason to own 16GB.

Black Forest Labs · 12B params

Three years old and still the ecosystem king for stylized art: Illustrious, Pony, NoobAI and thousands of other fine-tunes run on hardware everyone already owns. Anime and character art still lives here.

Stability AI · 3.5B params

Still the local video workhorse: cinematic text-to-video and image-to-video with an Apache license and unmatched ComfyUI tooling. Its 5B variant is what made home video generation real on mid-range cards.

Alibaba · 27B MoE (14B active)

The best quality-per-gigabyte in local video: an 8.3B DiT that produces photoreal motion on a 12–16GB card. If Wan is the workhorse, this is the efficiency pick.

Tencent · 8.3B params

The first open model that generates synchronized audio AND video — up to 4K at 50fps — and among the fastest per clip. The catch: it genuinely wants a 32GB flagship to shine.

Lightricks · 19B params

The default speech-to-text model for the entire industry. 99-language transcription and translation, MIT-licensed, and light enough that any modern GPU handles it in real time.

OpenAI · 1.55B params

The 2026 speech-to-text pick for English and European languages: more accurate than Whisper large on English, a fraction of the size, and absurdly fast. Whisper still wins for the long tail of languages.

NVIDIA · 0.6B params