--- title: "RTX 5090 (32 GiB): open-weight models that fit · Undominated.ai" canonical: https://undominated.ai/self-host/rtx-5090/ description: "Catalogue open-weight models that fit a RTX 5090. 32 GiB is the vendor-published framebuffer, not free-after-driver. Quality leads. Unrated is not zero. Ranking is arithmetic on sourced parameter counts — NVIDIA does not pick the winner." --- # RTX 5090 (32 GiB): open-weight models that fit · Undominated.ai > Catalogue open-weight models that fit a RTX 5090. 32 GiB is the vendor-published framebuffer, not free-after-driver. Quality leads. Unrated is not zero. Ranking is arithmetic on sourced parameter counts — NVIDIA does not pick the winner. [Self-host](/self-host/) # RTX 5090 32 GiB vendor-published framebuffer. Catalogue open-weight models that fit, ranked by measured quality. NVIDIA does not pick the winner. 32 GiB framebuffer. Source: https://www.nvidia.com/en-us/geforce/graphics-cards/compare/ 7 of 25 profiled open-weight models fit 32 GiB (vram) at Q5_K_M, 8192 context. Best rated that fits: Gemma 4 31B. ** ** ** Weights 21.106 GiB KV not estimated Runtime 1 GiB Headroom 9.9 GiB / 32 GiB rtx-5090: vendor 32 GiB framebuffer, not free-after-driver. ## Among models that fit Highest LMArena Elo among models that fit: Gemma 4 31B 1443.4. Elo is a human-preference scale, not the intelligence index. The arena boards are separate scales and are never blended. A missing cell is unmeasured, not zero. A model’s highest axis is relative to its own scores, not a claim it leads the catalogue. ## Rated models that fit | Model | Human preference | Quant that fits | Estimated GiB | Headroom | | --- | --- | --- | --- | --- | | [Gemma 4 31B](/models/google__gemma-4-31b-it/) vision video reasoning tools Fit assumptions KV heads and head dim are not in the public size table we used. Cache is not estimated. KV cache not estimated — layer/head geometry is not in the sourced profile. Longer context may not fit. | 1443.4 | Q5_K_M | 22.106 | 9.894 | | [Qwen3.8 27B](/models/qwen__qwen3.8-27b/) vision video reasoning tools Fit assumptions Hybrid: only 16 of 64 layers are Gated Attention. KV uses kvLayers=16, not nLayers=64. Measured Q4 is Unsloth UD-Q4_K_M, not a vanilla Q4_K_M. | 1440.7 | Q8_0 | 30.188 | 1.813 | | [Phi 4](/models/microsoft__phi-4/) Fit assumptions Hub safetensors listing says 15B params; the card states 14B. We use the card. KV from config.json: 40 layers, 10 KV heads, hidden 5120 → head dim 128. | 1216.7 | F16 | 30.563 | 1.438 | ## Fits, unrated - [Muse Glimmer 30B](/models/meta__muse-glimmer-30b/) — Q5_K_M, 21.756 GiB unrated - [Ministral 3 14B 2512](/models/mistralai__ministral-14b-2512/) — F16, 30.05 GiB unrated - [Ministral 3 8B 2512](/models/mistralai__ministral-8b-2512/) — F16, 19.663 GiB unrated - [Ministral 3 3B 2512](/models/mistralai__ministral-3b-2512/) — F16, 9.413 GiB unrated ## Does not fit, or not profiled - cohere/command-a-plus — even Q4_K_M wants 132.618 GiB; pool is 32 GiB - deepseek/deepseek-v4-flash-vision-exp — even Q4_K_M wants 184.902 GiB; pool is 32 GiB - z-ai/glm-5.3 — even Q4_K_M wants 455.624 GiB; pool is 32 GiB - qwen/qwen3.8-2.4t-a95b — even Q4_K_M wants 1450.719 GiB; pool is 32 GiB - moonshotai/kimi-k3 — even Q4_K_M wants 1691.5 GiB; pool is 32 GiB - z-ai/glm-5.2 — even Q4_K_M wants 455.624 GiB; pool is 32 GiB - moonshotai/kimi-k2.7-code — even Q4_K_M wants 604.75 GiB; pool is 32 GiB - nvidia/nemotron-3-ultra-550b-a55b — even Q4_K_M wants 333.063 GiB; pool is 32 GiB - minimax/minimax-m3 — even Q4_K_M wants 259.405 GiB; pool is 32 GiB - deepseek/deepseek-v4-pro — even Q4_K_M wants 967 GiB; pool is 32 GiB - deepseek/deepseek-v4-flash — even Q4_K_M wants 172.465 GiB; pool is 32 GiB - mistralai/mistral-small-2603 — even Q4_K_M wants 73.2 GiB; pool is 32 GiB - mistralai/mistral-large-2512 — even Q4_K_M wants 408.531 GiB; pool is 32 GiB - deepseek/deepseek-v3.2 — even Q4_K_M wants 406.116 GiB; pool is 32 GiB - openai/gpt-oss-120b — even Q4_K_M wants 72.201 GiB; pool is 32 GiB - meta-llama/llama-4-maverick — even Q4_K_M wants 242.5 GiB; pool is 32 GiB - meta-llama/llama-4-scout — even Q4_K_M wants 66.809 GiB; pool is 32 GiB - cohere/command-a — even Q4_K_M wants 68.016 GiB; pool is 32 GiB ## GPU [RTX 5000 ADA](/self-host/rtx-5000-ada/) ## How the number is made Weights: a measured GGUF size when we have one, otherwise total parameters × bits per weight ÷ 8. Q4_K_M is treated as 4.83 bits/param. MoE memory uses total parameters, not active parameters. KV cache, when layer and head geometry is sourced: 2 × kvLayers × kvHeads × headDim × context × 2 bytes (FP16, batch 1). Hybrid models use attention-layer count, not every layer. If geometry is missing, cache is omitted and that is stated. 1 GiB is added for CUDA/runtime. Apple unified memory uses RAM as the pool. CPU-only is a memory fit, not a speed claim. DDR generation is ignored for fit — it changes bandwidth, not whether the weights sit in memory. RAM type (DDR4/DDR5/LPDDR) is not a field. It does not change whether a model fits. [Self-host](/self-host/) ## Questions this page answers What LLM can I run on a RTX 5090? 7 of 25 profiled open-weight models fit 32 GiB (vram) at Q5_K_M, 8192 context. Best rated that fits: Gemma 4 31B. How much memory does a RTX 5090 have? 32 GiB vendor-published framebuffer (https://www.nvidia.com/en-us/geforce/graphics-cards/compare/), not free-after-driver. Does NVIDIA pick the winner? No. Ranking is arithmetic on sourced parameter counts. Quality leads. Unrated models that fit are listed separately, never scored zero. ## Continue your investigation - [Inspect open-weight models](/open-weights/) - [Compare model variants](/families/) - [Review requirements](/compare/) - [Find serving tools](/tools/)