Best local AI models for an RTX 5090 32 GB
An NVIDIA GeForce RTX 5090 32 GB card has about 28.8 GB of VRAM free for a model after the driver and context cache. These are the most downloaded open language and vision-language models that fit, for Ollama, llama.cpp, LM Studio or vLLM.
32 GB VRAM · about 28.8 GB usable for a model
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Last updated 3 November 2026 · refreshed after every Hugging Face sync · 15 models
| # | Model | Size | Memory | License | Downloads (30 days) | Likes | Est. speed (Q4) |
|---|---|---|---|---|---|---|---|
| 1 | Llama-3.1-8B-Instruct meta-llama | 8B · FP16 | ~20 GB | Llama 3.1 license | 4.5M | 8.8K | ~222 tok/s |
| 2 | Qwen3-8B Qwen | 8B · FP16 | ~20 GB | Apache 2.0 | 7.5M | 2.2K | ~213 tok/s |
| 3 | Qwen3.5-9B Qwen | 9B · FP16 | ~22 GB | Apache 2.0 | 6.4M | 2.3K | ~225 tok/s |
| 4 | Qwen2.5-7B-Instruct Qwen | 7B · FP16 | ~17 GB | Apache 2.0 | 6.1M | 2.5K | ~240 tok/s |
| 5 | Qwen2.5-VL-7B-Instruct Qwen | 7B · FP16 | ~17 GB | Apache 2.0 | 5.9M | 1.8K | – |
| 6 | Qwen3-VL-8B-Instruct Qwen | 8B · FP16 | ~20 GB | Apache 2.0 | 6.9M | 1.2K | – |
| 7 | Qwen3-4B Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 8.4M | 782 | – |
| 8 | Qwen3.5-4B Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 6.4M | 1.2K | ~327 tok/s |
| 9 | Ornith-1.5-35B-A3B-GGUF ornith-ai | 35B · GGUF | ~21 GB | MIT | 4.1M | 572 | – |
| 10 | Mistral-7B-Instruct-v0.2 mistralai | 7B · FP16 | ~17 GB | Apache 2.0 | 1.7M | 3.3K | ~232 tok/s |
| 11 | Ternary-Bonsai-2-27B-gguf prism-ml | 27B · GGUF | ~17 GB | Apache 2.0 | 1.8M | 2.9K | – |
| 12 | Qwen3-VL-4B-Instruct Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 3.9M | 523 | – |
| 13 | Qwen3-4B-Instruct-2507 Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 2.7M | 1K | – |
| 14 | Qwen3.8-27B-GSQ-RCO-GGUF ISTA-DASLab | 27B · GGUF | ~17 GB | Apache 2.0 | 1.3M | 2.5K | ~89 tok/s |
| 15 | Ornith-1.0-35B-GGUF ornith-ai | 35B · GGUF | ~21 GB | MIT | 1.5M | 1.1K | – |
How we rank
Language and vision-language models in GGUF, FP16, AWQ, GPTQ format whose published weights need about 9–28.8 GB. We include models seen on Hugging Face in the last 14 days with at least 10,000 downloads in the last 30 days. Score = log10(30-day downloads) + ½ × log10(likes + 1), so popularity leads and likes break near-ties. We keep one entry per model name and leave out adult and uncensored builds. Memory is estimated from parameter count and the published file format (16-bit ≈ 2 bytes per parameter, 4-bit ≈ 0.5) plus 20% headroom; long contexts need more. A quantised copy of a bigger model may also fit — check the model page.
Speed data: ModelFit (CC BY 4.0), from its open dataset, refreshed weekly. These are modelled estimates of decode speed for a 4-bit (Q4) build of the same model at a 4K context, not measured benchmarks, and they are not an endorsement by ModelFit. A dash means ModelFit has no estimate for that model.