Show HN: RigCheck – can your GPU or Mac run this open model? Sourced, open data

New open models ship every week. Pick your graphics card or Mac and RAM, and each model's own checker answers from the developers' published requirements and benchmarks. Short answer: With GeForce RTX 3060 (12 GB) + 32 GB RAM, Gemma 4 12B (Q5_K_M), Gemma 4 E2B (BF16), Gemma 4 E4B (Q8_0), Qwen3.8-27B (UD-IQ2_XXS) fit with every layer in GPU memory; with GeForce RTX 3090 / 3090 Ti (24 GB) + 64 GB RAM, Gemma 4 12B (Q8_0), Gemma 4 26B-A4B (UD-Q4_K_XL), Gemma 4 31B (IQ4_XS), Gemma 4 E2B (BF16), Gemma 4 E4B (BF16), Qwen3.8-27B (UD-Q4_K_XL) fit with every layer in GPU memory. These are RigCheck capacity estimates for llama.cpp at a 32K context in the finder's quick mode, not measured setups; larger models need system RAM or a Mac, and image and video models are judged by planning budgets and published recipes. Every model: the model list.

What can my hardware run? #

[Open in a full page ↗](https://tools.localrigcheck.com/)

 private checker…

13 models · 1 engine · 5 [VRAM pages](https://localrigcheck.com/vram/) · Latest: [LFM2.5-24B-A2B](https://localrigcheck.com/lfm-2-5-24b-a2b/), added 

Start from your hardware #

By VRAM size

With 64 GB of system RAM, in the finder's quick mode. Language models are counted in sizes (Gemma 4 counts five); sizes the checker does not cover and models without released weights are left out. Image and video: Qwen-Image (a planning budget): base model, INT8, 512×512, batch one, no reference images; Kolibri-1: the official FP8 route at a 32K context; MiniMax H3: local H3-Base FL2VA recipes, assuming an NVMe drive; Nemotron 3.5: Bolt and Super VL on one card with the RAM given; FLUX 3 Image Klein: Black Forest Labs' int8 ComfyUI route on the card. Language-model answers are RigCheck capacity estimates (Strata: its published requirements; Nemotron 3.5, GLM-5.3-Flash and DeepSeek V4.1 Flash: published routes and Unsloth memory tables), not measured setups. All graphics cards · Macs.

Fits wholly on one gaming GPU #

Sizes that sit entirely in the memory of a card with 24 GB or less, at the cheapest card in our sample; sizes that need system RAM are left out.

- [Gemma 4 12B](https://localrigcheck.com/gemma-4/) : Q5_K_M wholly in GPU memory with GeForce RTX 3060 (12 GB) + 32 GB RAM (capacity estimate)
- [Gemma 4 E2B](https://localrigcheck.com/gemma-4/) : BF16 wholly in GPU memory with GeForce RTX 3060 (12 GB) + 32 GB RAM (capacity estimate)
- [Gemma 4 E4B](https://localrigcheck.com/gemma-4/) : Q8_0 wholly in GPU memory with GeForce RTX 3060 (12 GB) + 32 GB RAM (capacity estimate)
- [Qwen3.8-27B](https://localrigcheck.com/qwen-3-8-27b/) : UD-IQ2_XXS wholly in GPU memory with GeForce RTX 3060 (12 GB) + 32 GB RAM (capacity estimate)

Newly added #

• LFM2.5-24B-A2B : added Oct 22, 2026. Will Liquid AI's 24B mixture-of-experts model fit your graphics card, RAM or Mac with llama.cpp or Ollama? Real GGUF file sizes, the KV cache for your context and the file to download.
• DeepSeek V4.1 Flash : added Oct 18, 2026. Match your Mac, Spark or PC to the routes DeepSeek V4.1 Flash's sources name: DwarfStar on one or two 128 GB machines (September weights; its new 1017 file is untested), Unsloth's experimental 1017 GGUF, and the real file sizes.
• Nemotron 3.5 : added Oct 17, 2026. Match your GPU, Mac or Spark to NVIDIA's own Nemotron 3.5 Bolt and Super VL recipes and Unsloth's memory budgets, with the launch command.

Image and video #

Judged by planning budgets and published recipes, not by a measured peak.

• FLUX 3 Image Klein : GeForce RTX 3060 (12 GB) + 32 GB RAM: Matches BFL's 8 GB guidance: the int8 files in ComfyUI (published route match). BFL: the int8 ComfyUI files run on 8 GB GPUs such as the RTX 3070. Image model, int8 text encoder and VAE together are 11.0 GB; by our reading, on a card smaller than that ComfyUI keeps them on the GPU one at a time and needs system RAM for the rest. BFL gives no RAM figure. The prompt upsampler BFL says not to skip is a 9B model (6.72 GB w4a8, 9.75 GB int8 or 18.8 GB BF16 in ComfyUI); BFL does not say whether its 8 GB figure includes it. When it does not fit with the image model, run it on its own first (BFL's README) or switch it off in the ComfyUI template, with weaker results.
• MiniMax H3 : GeForce RTX 3060 (12 GB) + 32 GB RAM: Hardware memory matches a published video recipe; exact clip peak unverified (published recipe match). Matching recipes: SGLang Recipe A (lab): one card, DiT, text encoder and VAE streamed from the checkpoint file on an NVMe drive (Recipe A), BF16; measured at 864×480 (at the native 768P the cookbook says to use video_vae=24 if the decode runs out of memory, not measured); lab run with VRAM capped at 12 GiB and a simulated 32 GB host: shows the memory fits, not your card's speed; a real 32 GB machine re-reads the weights from disk on every step. H3-Base FL2VA, source recipe canvases only. Disk-streaming routes assume an NVMe drive. Host-RAM and lab assumptions remain in the full checker; full 2K needs an API.
• Qwen-Image-2.1 : GeForce RTX 3060 (12 GB) + 32 GB RAM: Above the all-components-resident planning budget; offload peak unverified (planning budget). Uncalibrated allowances, not a minimum or a guarantee. Full checkpoint files are counted in host RAM even when mmap or sequential can use less. Actual peak depends on attention, prefix caches, VAE tiling, reference sizes and backend. No speed prediction.

Coming soon #

• Mistral Large 4 : Announced Oct 6, 2026; weights expected Oct 31, 2026 (source , read Oct 8, 2026)

Recently updated #

• Gemma 4 : RigCheck Lab measurements of Gemma 4 26B-A4B: UD-IQ3_XXS on an RTX 4060 Ti 16 GB and UD-Q4_K_XL on an RTX 4090, at about 4K and 32K context, three runs each
• LFM2.5-24B-A2B : Checker launched: will LFM2.5-24B-A2B fit your GPU, RAM or Mac, from the official and Unsloth GGUF file sizes, with the command to run it
• MiniMax H3 : vLLM's recipe now lists single-card RTX 4090 / RTX 5090 / DGX Spark configurations for FastH3, FastVideo's few-step derivative: noted as configuration guidance the recipe has not verified on a GPU, on a vLLM-Omni pull-request branch (#9167), not matched by the checker
• Qwen3.8-27B : RigCheck Lab measurements: UD-IQ3_XXS on an RTX 4060 Ti 16 GB and UD-Q4_K_XL on an RTX 4090, at about 4K and 32K context, three runs each
• DeepSeek V4.1 Flash : DeepInfer, the newly open-sourced framework behind DeepSeek's own services, added to the runtimes: it runs V4.1 Flash (1017 by default) only on NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs under Linux, so it is no route for Macs, and in RigCheck's reading of NVIDIA's list none for gaming cards or the DGX Spark

How we check #

• Numbers come from the model's own repository, model card or announcement, and every one links to its source.
• Official measurements, official estimates, community reports and RigCheck's own estimates are labeled differently.
• Each page shows when it last changed and when its numbers were last checked against the sources.

RigCheck is independent and not affiliated with any model developer. Found a wrong number? Email hello@localrigcheck.com with the source. How RigCheck works.

FAQ #

How do I know if my GPU can run a model? #

Pick your card and RAM in the finder above, or open your graphics card's page. Each answer comes from that model's own checker, and says which kind it is: the llama.cpp checkers add up the file, the context cache and buffers against your memory; the Strata checker applies Strata's published requirements; Kolibri-1's matches official minimum configurations and a community recipe; image models use a planning budget and video models match published recipes. The full checker on each model page takes your exact setup.

What does "with caveats" mean? #

The model fits only with part of it in system RAM (slower), or the memory peak is not measured, or the route is one the checker marks as conditional. The checker says which; RigCheck never shows a caveat as a plain yes.

Can a Mac run these models? #

Many of them: on a Mac the graphics and the processor share one pool of unified memory, so the finder judges a Mac by its total memory. Open the page for your Mac's memory size or pick your Mac in the finder; the Strata engine runs on Apple Silicon only experimentally.

Are these numbers measured? #

Mostly not. The llama.cpp checkers' memory answers are RigCheck capacity estimates from the published file sizes; Strata's follow its published requirements, Kolibri-1's match official minimum configurations or a community recipe, and image and video answers are planning budgets or published recipes. Speeds are labeled as an official measurement, an official estimate or a community report; a measurement names the card and setup it was run on. How RigCheck works.

Which models does RigCheck cover? #

Newly released open-weights models and a few popular mid-size ones, each with its own checker. The model list shows them all with their sizes, weights status, runtimes and license.