--- title: "Which open-weight model fits this machine · Undominated.ai" canonical: https://undominated.ai/self-host/ description: "Open-weight models that actually fit this machine, ranked from RAM and GPU memory. Quality leads. Unrated is not zero. A missing parameter count is not a guess." --- # Which open-weight model fits this machine · Undominated.ai > Open-weight models that actually fit this machine, ranked from RAM and GPU memory. Quality leads. Unrated is not zero. A missing parameter count is not a guess. # Self-host RAM and whether you have a GPU are enough. Everything else is optional. The ranking is arithmetic on sourced parameter counts — NVIDIA does not pick the winner. 25 catalogue rows marked open-weight. Nothing else is a candidate. 25 of those have a sourced parameter count. The rest are listed as unknown, not guessed. 1 GiB runtime allowance, named, not measured on your machine. [CPU 16 GiB](/self-host/cpu-16gb/)[8 GiB GPU](/self-host/8gb/)[10 GiB GPU](/self-host/10gb/)[12 GiB GPU](/self-host/12gb/)[16 GiB GPU](/self-host/16gb/)[20 GiB GPU](/self-host/20gb/)[24 GiB GPU](/self-host/24gb/)[32 GiB GPU](/self-host/32gb/)[40 GiB GPU](/self-host/40gb/)[48 GiB GPU](/self-host/48gb/)[80 GiB GPU](/self-host/80gb/)[94 GiB GPU](/self-host/94gb/)[96 GiB GPU](/self-host/96gb/)[192 GiB GPU](/self-host/192gb/) [RTX 5090](/self-host/rtx-5090/)[RTX 5080](/self-host/rtx-5080/)[RTX 5070 Ti](/self-host/rtx-5070-ti/)[RTX 5070](/self-host/rtx-5070/)[RTX 5060 Ti 16](/self-host/rtx-5060-ti-16/)[RTX 5060 Ti 8](/self-host/rtx-5060-ti-8/)[RTX 5060](/self-host/rtx-5060/)[RTX 5050](/self-host/rtx-5050/)[RTX 4090](/self-host/rtx-4090/)[RTX 4080](/self-host/rtx-4080/)[RTX 4070 Ti](/self-host/rtx-4070-ti/)[RTX 4070](/self-host/rtx-4070/)[RTX 4060 Ti 16](/self-host/rtx-4060-ti-16/)[RTX 4060 Ti 8](/self-host/rtx-4060-ti-8/)[RTX 4060](/self-host/rtx-4060/)[RTX 3090](/self-host/rtx-3090/)[RTX 3080 Ti](/self-host/rtx-3080-ti/)[RTX 3080 12](/self-host/rtx-3080-12/)[RTX 3080](/self-host/rtx-3080/)[RTX 3070 Ti](/self-host/rtx-3070-ti/)[RTX 3070](/self-host/rtx-3070/)[RTX 3060 Ti](/self-host/rtx-3060-ti/)[RTX 3060 8](/self-host/rtx-3060-8/)[RTX 3060](/self-host/rtx-3060/)[RTX A6000](/self-host/rtx-a6000/)[RTX A5000](/self-host/rtx-a5000/)[RTX A4000](/self-host/rtx-a4000/)[RTX 6000 ADA](/self-host/rtx-6000-ada/)[RTX 5000 ADA](/self-host/rtx-5000-ada/)[RTX PRO 6000 Blackwell](/self-host/rtx-pro-6000-blackwell/)[RTX PRO 5000 Blackwell](/self-host/rtx-pro-5000-blackwell/)[A100 80](/self-host/a100-80/)[A100 40](/self-host/a100-40/)[H100 NVL](/self-host/h100-nvl/)[H100](/self-host/h100/)[L40S](/self-host/l40s/)[T4](/self-host/t4/)[RX 7900 XTX](/self-host/rx-7900-xtx/)[RX 7900 XT](/self-host/rx-7900-xt/)[RX 6900 XT](/self-host/rx-6900-xt/)[RX 6800 XT](/self-host/rx-6800-xt/)[MI300X](/self-host/mi300x/) System RAM GiB GPU None (CPU) NVIDIA AMD Apple Intel GPU memory GiB Required unless you name a card we list. Apple: leave this; RAM is the pool. GPU name (optional) GPU count OS (optional — software only) Skip Linux Windows macOS Context to budget 4K 8K 16K 32K KV cache grows with this. We do not assume the model card maximum. 7 of 25 profiled open-weight models fit 24 GiB (vram) at 8192 context. Best rated that fits: Gemma 4 31B (Q5_K_M). ** ** ** Weights 21.106 GiB KV not estimated Runtime 1 GiB Headroom 1.9 GiB / 24 GiB VRAM as entered. Driver reservation is inside the 1 GiB overhead, not extra. 48 GiB raises the quant of Gemma 4 31B to Q8_0. Same model, not a higher intelligence score. ## 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 | 1443.4 | Q5_K_M | 22.106 | 1.894 | | [Qwen3.8 27B](/models/qwen__qwen3.8-27b/) vision video reasoning tools | 1440.7 | Q5_K_M | 20.063 | 3.938 | | [Phi 4](/models/microsoft__phi-4/) | 1216.7 | Q8_0 | 17.438 | 6.563 | - 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. ## Fits, unrated - [Muse Glimmer 30B](/models/meta__muse-glimmer-30b/) — Q5_K_M, 21.756 GiB unrated vision reasoning tools - [Ministral 3 14B 2512](/models/mistralai__ministral-14b-2512/) — Q8_0, 17.019 GiB unrated vision tools - [Ministral 3 8B 2512](/models/mistralai__ministral-8b-2512/) — F16, 19.663 GiB unrated vision tools - [Ministral 3 3B 2512](/models/mistralai__ministral-3b-2512/) — F16, 9.413 GiB unrated vision tools ## What an upgrade actually buys The fit bottleneck is GPU memory. More system RAM will not load extra weights onto this card. | Estimated GiB | Unlocks | Example | List price | | --- | --- | --- | --- | | 48 GiB | [Gemma 4 31B](/models/google__gemma-4-31b-it/) — Q8_0 same model, higher quant | rtx a6000, rtx 6000 ada, rtx pro 6000 blackwell | No consumer list in this file | The next higher-rated catalogue model is Kimi K3, and even Q4_K_M wants 1691.5 GiB. That is not a desktop card. NVIDIA 'Starting at' list prices as published 2026-08-25, not a store quote and not a used-market index. Street prices in August 2026 are often much higher (Tom's Hardware Newegg median RTX 5090 $4,699 vs $1,999 list). Last-gen 24 GB cards (4090, 3090) have no current NVIDIA starting-at in this file. Datacenter cards have no consumer list here. RAM DIMMs are not priced — they move weekly. Upgrades use the same fit formula as the ranking. List prices are NVIDIA starting-at, dated 2026-08-25. Street prices are often higher. Used 24 GB cards and datacenter GPUs have no list here. RAM DIMMs are not priced. ## Does not fit, or not profiled - cohere/command-a-plus — even Q4_K_M wants 132.618 GiB; pool is 24 GiB - deepseek/deepseek-v4-flash-vision-exp — even Q4_K_M wants 184.902 GiB; pool is 24 GiB - z-ai/glm-5.3 — even Q4_K_M wants 455.624 GiB; pool is 24 GiB - qwen/qwen3.8-2.4t-a95b — even Q4_K_M wants 1450.719 GiB; pool is 24 GiB - moonshotai/kimi-k3 — even Q4_K_M wants 1691.5 GiB; pool is 24 GiB - z-ai/glm-5.2 — even Q4_K_M wants 455.624 GiB; pool is 24 GiB - moonshotai/kimi-k2.7-code — even Q4_K_M wants 604.75 GiB; pool is 24 GiB - nvidia/nemotron-3-ultra-550b-a55b — even Q4_K_M wants 333.063 GiB; pool is 24 GiB - minimax/minimax-m3 — even Q4_K_M wants 259.405 GiB; pool is 24 GiB - deepseek/deepseek-v4-pro — even Q4_K_M wants 967 GiB; pool is 24 GiB - deepseek/deepseek-v4-flash — even Q4_K_M wants 172.465 GiB; pool is 24 GiB - mistralai/mistral-small-2603 — even Q4_K_M wants 73.2 GiB; pool is 24 GiB - mistralai/mistral-large-2512 — even Q4_K_M wants 408.531 GiB; pool is 24 GiB - deepseek/deepseek-v3.2 — even Q4_K_M wants 406.116 GiB; pool is 24 GiB - openai/gpt-oss-120b — even Q4_K_M wants 72.201 GiB; pool is 24 GiB - meta-llama/llama-4-maverick — even Q4_K_M wants 242.5 GiB; pool is 24 GiB - meta-llama/llama-4-scout — even Q4_K_M wants 66.809 GiB; pool is 24 GiB - cohere/command-a — even Q4_K_M wants 68.016 GiB; pool is 24 GiB ## Extra details (optional) Does not change the ranking. Optional AI analysis of your notes against the numbers above, using NVIDIA NIM’s strongest models. Analyze with AI ## 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. Only catalogue rows with openWeights=true are candidates. 25 such rows exist; 25 have sourced sizes. Unrated models that fit are listed separately. A higher score does not mean a drop-in replacement. RAM type (DDR4/DDR5/LPDDR) is not a field. It does not change whether a model fits. ## Continue your investigation - [Inspect open-weight models](/open-weights/) - [Compare model variants](/families/) - [Review requirements](/compare/) - [Find serving tools](/tools/)