What local AI models can the NVIDIA GeForce RTX 2080 SUPER run?
37 of our 115 models, up to NVIDIA Nemotron 3.5 Bolt E4B. That is the largest current model that fits the GeForce RTX 2080 SUPER's 8 GB at Q4 with room for about 46K tokens of context (its full 64K with 8-bit KV cache), at about 65 tok/s estimate.
Memory type: GDDR6 (source) · Bus width: 256-bit (source) · Released: 2019 (source)
- Turing: no calibration point; uses the CUDA constants fitted on Ampere/Ada/Blackwell.
Specs from first-party pages (NVIDIA), accessed 2026-10-18. Speeds use the NVIDIA (CUDA) constants (how).
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Every model, largest that fits first
| Model | Quant | File | Fits? | Speed estimate | Max context estimate |
|---|---|---|---|---|---|
| NVIDIA Nemotron 3.5 Bolt E4B | Q4_K_M | 6.18 GB | Fits | ~65 tok/s | 46K · 64K (8-bit) |
| Qwen3.5-9B | Q4_K_M | 6.17 GB | Fits | ~56 tok/s | 24K · 46K (8-bit) |
| Ternary Bonsai 2 27B | PTQ1_0 ternary | 5.95 GB | Fits | ~58 tok/s | 15K · 29K (8-bit) |
| Microsoft Fara1.5 9B | Q4_K_M | 5.91 GB | Fits | ~58 tok/s | 32K · 61K (8-bit) |
| Xiaomi MiMo-V2.6-Distill-Qwen-9B | Q4_K_M | 5.84 GB | Fits | ~59 tok/s | 34K · 65K (8-bit) |
| ZoomGain-9B | Q4_K_M | 5.63 GB | Fits | ~61 tok/s | 40K · 76K (8-bit) |
| K2-Horizon-7B | Q4_K_M | 5.59 GB | Fits | ~71 tok/s | 9K · 17K (8-bit) |
| webAI TwIL-8B Math and code reasoning | Q4_K_M | 5.35 GB | Fits | ~74 tok/s | 9K · 18K (8-bit) |
| IBM Granite 4.2 8B | Q4_K_M | 5.35 GB | Fits | ~74 tok/s | 9K · 18K (8-bit) |
| PLaMo 3 Translate 8B Translation only | Q4_K_M | 5.28 GB | Fits | ~75 tok/s | 32K (full) |
| LFM2.5-8B-A1B | Q4_K_M | 5.16 GB | Fits | ~161 tok/s | 128K (full) |
| Gemma 4 E4B | Q4_K_M | 4.98 GB | Fits | ~79 tok/s | 117K · 128K (8-bit) |
| Spark-X2.6-4B | Q4_K_M | 3.54 GB | Fits | ~107 tok/s | 88K · 169K (8-bit) |
| Gemma 4 E2B | Q4_K_M | 3.46 GB | Fits | ~110 tok/s | 128K (full) |
| Qwen3.5-4B | Q4_K_M | 3.01 GB | Fits | ~95 tok/s | 118K · 223K (8-bit) |
| NVIDIA Nemotron 3 Nano 4B | Q4_K_M | 2.9 GB | Fits | ~98 tok/s | 244K · 256K (8-bit) |
| Microsoft Fara1.5 4B | Q4_K_M | 2.88 GB | Fits | ~98 tok/s | 122K · 231K (8-bit) |
| ZoomGain-4B | Q4_K_M | 2.71 GB | Fits | ~102 tok/s | 127K · 240K (8-bit) |
| IBM Granite 4.2 3B | Q4_K_M | 2.24 GB | Fits | ~158 tok/s | 56K · 106K (8-bit) |
| Raon-1 nano Early support | Q4_K_M | 2.08 GB | Weights fit context unknown | ~120 tok/s | Unknown (no KV data) |
| Ternary Bonsai 2 9B | PTQ1_0 ternary | 2 GB | Fits | ~122 tok/s | 149K · 256K (8-bit) |
| PLaMo 3 Translate 2B Translation only | Q4_K_M | 1.81 GB | Fits | ~187 tok/s | 32K (full) |
| LFM3-1.7B | Q4_K_M | 1.1 GB | Fits | ~163 tok/s | 256K (full) |
| LFM3-VL-2B | Q4_K_M | 1.1 GB | Fits | ~163 tok/s | 128K (full) |
| Ternary Bonsai 2 4B | PTQ1_0 ternary | 0.95 GB | Fits | ~173 tok/s | 180K · 256K (8-bit) |
| MiniCPM-V 4.7 1B | Q4_K_M | 0.67 GB | Fits | ~195 tok/s | 256K (full) |
| PLaMo 3 Translate 0.8B Translation only | Q4_K_M | 0.57 GB | Fits | ~399 tok/s | 32K (full) |
| BotPsy-0.8B | Q4_K_M | 0.53 GB | Fits | ~208 tok/s | 256K (full) |
| Ternary Bonsai 2 2B | PTQ1_0 ternary | 0.43 GB | Fits | ~218 tok/s | 256K (full) |
| Qwen3-8B Older | Q4_K_M | 5.03 GB | Fits | ~79 tok/s | 13K · 24K (8-bit) |
| Llama-3.1-8B-Instruct Older | Q4_K_M | 4.92 GB | Fits | ~80 tok/s | 15K · 29K (8-bit) |
| Qwen2.5-Coder-7B-Instruct Older | Q4_K_M | 4.68 GB | Fits | ~84 tok/s | 32K (full) |
| Mistral-7B-Instruct-v0.3 Older | Q4_K_M | 4.37 GB | Fits | ~89 tok/s | 19K · 32K (8-bit) |
| Qwen3-4B Older | Q4_K_M | 2.5 GB | Fits | ~144 tok/s | 29K · 40K (8-bit) |
| gemma-3-4b-it Older | Q4_K_M | 2.49 GB | Fits | ~145 tok/s | 128K (full) |
| Llama-3.2-3B-Instruct Older | Q4_K_M | 2.02 GB | Fits | ~172 tok/s | 42K · 79K (8-bit) |
| Qwen3-0.6B Older | Q4_K_M | 0.48 GB | Fits | ~435 tok/s | 40K (full) |
| NVIDIA-Nemotron-Nano-9B-v2 Older | Q4_K_M | 6.53 GB | No | — | — |
| Gemma 4 12B | Q4_K_M | 7.12 GB | No | — | — |
| gemma-3-12b-it Older | Q4_K_M | 7.3 GB | No | — | — |
| Mistral-Nemo-Instruct-2407 Older | Q4_K_M | 7.48 GB | No | — | — |
| JetBrains Mellum2.1 12B-A2.5B Thinking | Q4_K_M | 8.07 GB | No | — | — |
| Qwen3-14B Older | Q4_K_M | 9 GB | No | — | — |
| Qwen2.5-Coder-14B-Instruct Older | Q4_K_M | 8.99 GB | No | — | — |
| DeepSeek-R1-Distill-Qwen-14B Older | Q4_K_M | 8.99 GB | No | — | — |
| phi-4 | Q4_K_M | 9.05 GB | No | — | — |
| gpt-oss-20b | Q4_K_M | 11.67 GB | No | — | — |
| Mistral-Small-3.2-24B-Instruct-2506 | Q4_K_M | 14.33 GB | No | — | — |
| Devstral-Small-2505 | Q4_K_M | 14.33 GB | No | — | — |
| LFM2-24B-A2B Older | Q4_K_M | 14.42 GB | No | — | — |
| LFM2.5-24B-A2B | Q4_K_M | 14.53 GB | No | — | — |
| gemma-3-27b-it Older | Q4_K_M | 16.55 GB | No | — | — |
| Kvark Sparkle 1.1 27B | Q4_K_M | 16.81 GB | No | — | — |
| Gemma 4 26B A4B | Q4_K_M | 17.04 GB | No | — | — |
| Qwen3.8-27B | Q4_K_M | 17.44 GB | No | — | — |
| Microsoft Fara1.5 27B | Q4_K_M | 17.53 GB | No | — | — |
| IBM Granite 4.2 30B | Q4_K_M | 17.72 GB | No | — | — |
| Qwen3.6-27B | Q4_K_M | 17.98 GB | No | — | — |
| Qwen3.5-27B | Q4_K_M | 17.98 GB | No | — | — |
| Meta Muse Glimmer 30B | Q4_K_M | 18.39 GB | No | — | — |
| GLM-4.7-Flash | Q4_K_M | 18.31 GB | No | — | — |
| Qwen3-Coder-30B-A3B-Instruct Older | Q4_K_M | 18.56 GB | No | — | — |
| Gemma 4 31B | Q4_K_M | 18.32 GB | No | — | — |
| Qwen3-30B-A3B Older | Q4_K_M | 18.63 GB | No | — | — |
| PLaMo 3 Translate 31B Translation only | Q4_K_M | 19.53 GB | No | — | — |
| Raon-1 mini preview Early support | Q4_K_M | 19.69 GB | No | — | — |
| Qwen3-32B Older | Q4_K_M | 19.76 GB | No | — | — |
| Qwen2.5-Coder-32B-Instruct Older | Q4_K_M | 19.85 GB | No | — | — |
| DeepSeek-R1-Distill-Qwen-32B Older | Q4_K_M | 19.85 GB | No | — | — |
| Xing4.0-29B-A4B | Q4_K_M | 20.06 GB | No | — | — |
| Nanbeige4.3-33B | Q4_K_M | 20.79 GB | No | — | — |
| MiniCPM-V 4.7 35B-A3B | Q4_K_M | 21.17 GB | No | — | — |
| K2-Horizon-32B | Q4_K_M | 21.08 GB | No | — | — |
| NVIDIA Nemotron 3.5 Bolt 74B-A9B 24GB (2-bit) | native 2-bit | 22.02 GB | No | — | — |
| Qwen3.6-35B-A3B | Q4_K_M | 22.29 GB | No | — | — |
| Qwen3.5-35B-A3B | Q4_K_M | 22.29 GB | No | — | — |
| K2-Horizon-MoVA-36B-A4B | Q4_K_M | 22.37 GB | No | — | — |
| NVIDIA Nemotron 3.5 Lightning 30B-A3B | Q4_K_M | 25.48 GB | No | — | — |
| Recursive Flint 40B-A2.5B | Q4_1 | 25.99 GB | No | — | — |
| Ripple-65B Preview | Q4_K_M | 38.8 GB | No | — | — |
| Llama-3.3-70B-Instruct Older | Q4_K_M | 42.52 GB | No | — | — |
| DeepSeek-R1-Distill-Llama-70B Older | Q4_K_M | 42.52 GB | No | — | — |
| Qwen2.5-72B-Instruct Older | Q4_K_M | 47.42 GB | No | — | — |
| Qwen3-Coder-Next | Q4_K_M | 48.53 GB | No | — | — |
| Aleph Alpha Kolibri-1 78B-A3.5B | Q4_K_M | 49.76 GB | No | — | — |
| NVIDIA Nemotron 3.5 Bolt 74B-A9B | UD-Q4_K_XL | 50.22 GB | No | — | — |
| gpt-oss-120b | Q4_K_M | 62.84 GB | No | — | — |
| NVIDIA Nemotron 3.5 Super VL 120B-A12B | Q4_K_M | 70.1 GB | No | — | — |
| Mistral Small 4 119B | Q4_K_M | 72.64 GB | No | — | — |
| GLM-4.5-Air Older | Q4_K_M | 73.5 GB | No | — | — |
| Qwen3.5-122B-A10B | Q4_K_M | 77.62 GB | No | — | — |
| Mistral Medium 3.5 128B | Q4_K_M | 78.41 GB | No | — | — |
| Ornith-1.5-122B | Q4_K_M | 78.92 GB | No | — | — |
| NVIDIA Nemotron 3 Super 120B-A12B Older | UD-Q4_K_M | 82.54 GB | No | — | — |
| Swift 1.5 Qwen3.8-Flash-Next | Q4_K_M | 119.6 GB | No | — | — |
| Qwen3.8-Flash-Next | Q4_K_M | 119.6 GB | No | — | — |
| StepFun Step 3.7 Flash | UD-Q4_K_M | 122.09 GB | No | — | — |
| Qwen3-235B-A22B | Q4_K_M | 142.15 GB | No | — | — |
| MiniMax-M3.1-Flash Unsloth 2-bit | UD-Q2_K_XL | 143.91 GB | No | — | — |
| DeepSeek-V4-Flash | UD-Q4_K_XL | 155.1 GB | No | — | — |
| MiniMax-M3.1-Flash Q2_K | Q2_K | 155.91 GB | No | — | — |
| Thinking Machines Inkling-Small Older | Q4_K_M | 159.34 GB | No | — | — |
| Xiaomi MiMo-V2.6-Flash 309B-A15B | MXFP4 | 167.37 GB | No | — | — |
| GLM-5.3-Flash | UD-Q4_K_XL | 199.71 GB | No | — | — |
| MiniMax-M3.1-Flash | Q4_K_M | 258.01 GB | No | — | — |
| Reflection Beam 1.0 501B-A23B | Q4_K_M | 302.41 GB | No | — | — |
| Thinking Machines Inkling 2 Small Early support | UD-Q4_K_XL | 354.15 GB | No | — | — |
| inclusionAI Ling-3.1-flash 560B-A25B | Q4_K_M | 357.97 GB | No | — | — |
| DeepSeek-V4.1-Flash (1017) | UD-Q4_K_XL | 500.71 GB | No | — | — |
| Meta Muse Spark Open 816B-A42B | UD-Q4_K_XL | 505.5 GB | No | — | — |
| Mistral Large 4 | UD-Q4_K_XL | 605 GB | No | — | — |
Q4_K_M unless the row says otherwise; UD- sizes are unsloth dynamic quants, the only Q4 build published for those models. Max context is an estimate of the longest context whose KV cache fits beside the model: default FP16 cache, then 8-bit (q8_0).
Speed calculator
estimateEstimate = bandwidth ÷ GB read per token (the model's listed GGUF file: Q4_K_M for most models, otherwise the quant named in the list; scaled by active/total parameters for MoE), scaled by a per-platform efficiency and fixed per-token costs calibrated to 45 measured llama.cpp benchmarks (2025–26). Fit = file size + 1.5 GB headroom ≤ total VRAM (on Macs, the part of unified memory macOS lets the GPU use by default), and room for at least 4K tokens of context with an 8-bit KV cache. For a model with no KV cache data, only the file and headroom are checked, so a fit says nothing about context. Multi-GPU: VRAM adds, speed stays one card's (a layer split runs the cards in turn); max context uses the total. Speed is for short context. Max context = what is left after the file and a 1 GB runtime reserve ÷ KV cache bytes per token, default FP16 and 8-bit (q8_0) cache. How speed works · how context works