What local AI models can the NVIDIA RTX A4500 run?
61 of our 115 models, up to Meta Muse Glimmer 30B. That is the largest current model that fits the RTX A4500's 20 GB at Q4 with room for about 37K tokens of context (76K with 8-bit KV cache), at about 30 tok/s estimate.
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| Model | Quant | File | Fits? | Speed estimate | Max context estimate |
|---|---|---|---|---|---|
| Meta Muse Glimmer 30B | Q4_K_M | 18.39 GB | Fits | ~30 tok/s | 37K · 76K (8-bit) |
| GLM-4.7-Flash | Q4_K_M | 18.31 GB | Fits | ~207 tok/s | 12K · 23K (8-bit) |
| Qwen3.6-27B | Q4_K_M | 17.98 GB | Fits | ~28 tok/s | 15K · 28K (8-bit) |
| Qwen3.5-27B | Q4_K_M | 17.98 GB | Fits | ~28 tok/s | 15K · 28K (8-bit) |
| Microsoft Fara1.5 27B | Q4_K_M | 17.53 GB | Fits | ~29 tok/s | 21K · 41K (8-bit) |
| Qwen3.8-27B | Q4_K_M | 17.44 GB | Fits | ~29 tok/s | 23K · 43K (8-bit) |
| Gemma 4 26B A4B | Q4_K_M | 17.04 GB | Fits | ~162 tok/s | 78K · 160K (8-bit) |
| Kvark Sparkle 1.1 27B | Q4_K_M | 16.81 GB | Fits | ~30 tok/s | 32K · 61K (8-bit) |
| LFM2.5-24B-A2B | Q4_K_M | 14.53 GB | Fits | ~160 tok/s | 128K (full) |
| Mistral-Small-3.2-24B-Instruct-2506 | Q4_K_M | 14.33 GB | Fits | ~38 tok/s | 27K · 52K (8-bit) |
| Devstral-Small-2505 | Q4_K_M | 14.33 GB | Fits | ~38 tok/s | 27K · 52K (8-bit) |
| gpt-oss-20b | Q4_K_M | 11.67 GB | Fits | ~190 tok/s | 128K (full) |
| phi-4 | Q4_K_M | 9.05 GB | Fits | ~58 tok/s | 16K (full) |
| JetBrains Mellum2.1 12B-A2.5B Thinking | Q4_K_M | 8.07 GB | Fits | ~215 tok/s | 128K (full) |
| Gemma 4 12B | Q4_K_M | 7.12 GB | Fits | ~72 tok/s | 256K (full) |
| NVIDIA Nemotron 3.5 Bolt E4B | Q4_K_M | 6.18 GB | Fits | ~82 tok/s | 64K (full) |
| Qwen3.5-9B | Q4_K_M | 6.17 GB | Fits | ~69 tok/s | 256K (full) |
| Ternary Bonsai 2 27B | PTQ1_0 ternary | 5.95 GB | Fits | ~71 tok/s | 194K · 256K (8-bit) |
| Microsoft Fara1.5 9B | Q4_K_M | 5.91 GB | Fits | ~71 tok/s | 256K (full) |
| Xiaomi MiMo-V2.6-Distill-Qwen-9B | Q4_K_M | 5.84 GB | Fits | ~72 tok/s | 256K (full) |
| ZoomGain-9B | Q4_K_M | 5.63 GB | Fits | ~74 tok/s | 256K (full) |
| K2-Horizon-7B | Q4_K_M | 5.59 GB | Fits | ~90 tok/s | 88K · 167K (8-bit) |
| webAI TwIL-8B Math and code reasoning | Q4_K_M | 5.35 GB | Fits | ~93 tok/s | 81K · 128K (8-bit) |
| IBM Granite 4.2 8B | Q4_K_M | 5.35 GB | Fits | ~93 tok/s | 81K · 128K (8-bit) |
| PLaMo 3 Translate 8B Translation only | Q4_K_M | 5.28 GB | Fits | ~95 tok/s | 32K (full) |
| LFM2.5-8B-A1B | Q4_K_M | 5.16 GB | Fits | ~175 tok/s | 128K (full) |
| Gemma 4 E4B | Q4_K_M | 4.98 GB | Fits | ~100 tok/s | 128K (full) |
| Spark-X2.6-4B | Q4_K_M | 3.54 GB | Fits | ~134 tok/s | 406K · 767K (8-bit) |
| Gemma 4 E2B | Q4_K_M | 3.46 GB | Fits | ~136 tok/s | 128K (full) |
| Qwen3.5-4B | Q4_K_M | 3.01 GB | Fits | ~112 tok/s | 256K (full) |
| NVIDIA Nemotron 3 Nano 4B | Q4_K_M | 2.9 GB | Fits | ~114 tok/s | 256K (full) |
| Microsoft Fara1.5 4B | Q4_K_M | 2.88 GB | Fits | ~115 tok/s | 256K (full) |
| ZoomGain-4B | Q4_K_M | 2.71 GB | Fits | ~119 tok/s | 256K (full) |
| IBM Granite 4.2 3B | Q4_K_M | 2.24 GB | Fits | ~193 tok/s | 128K (full) |
| Raon-1 nano Early support | Q4_K_M | 2.08 GB | Weights fit context unknown | ~137 tok/s | Unknown (no KV data) |
| Ternary Bonsai 2 9B | PTQ1_0 ternary | 2 GB | Fits | ~140 tok/s | 256K (full) |
| PLaMo 3 Translate 2B Translation only | Q4_K_M | 1.81 GB | Fits | ~227 tok/s | 32K (full) |
| LFM3-1.7B | Q4_K_M | 1.1 GB | Fits | ~180 tok/s | 256K (full) |
| LFM3-VL-2B | Q4_K_M | 1.1 GB | Fits | ~180 tok/s | 128K (full) |
| Ternary Bonsai 2 4B | PTQ1_0 ternary | 0.95 GB | Fits | ~189 tok/s | 256K (full) |
| MiniCPM-V 4.7 1B | Q4_K_M | 0.67 GB | Fits | ~209 tok/s | 256K (full) |
| PLaMo 3 Translate 0.8B Translation only | Q4_K_M | 0.57 GB | Fits | ~452 tok/s | 32K (full) |
| BotPsy-0.8B | Q4_K_M | 0.53 GB | Fits | ~220 tok/s | 256K (full) |
| Ternary Bonsai 2 2B | PTQ1_0 ternary | 0.43 GB | Fits | ~229 tok/s | 256K (full) |
| gemma-3-27b-it Older | Q4_K_M | 16.55 GB | Fits | ~33 tok/s | 21K · 47K (8-bit) |
| LFM2-24B-A2B Older | Q4_K_M | 14.42 GB | Fits | ~160 tok/s | 128K (full) |
| Qwen3-14B Older | Q4_K_M | 9 GB | Fits | ~58 tok/s | 40K (full) |
| Qwen2.5-Coder-14B-Instruct Older | Q4_K_M | 8.99 GB | Fits | ~58 tok/s | 32K (full) |
| DeepSeek-R1-Distill-Qwen-14B Older | Q4_K_M | 8.99 GB | Fits | ~58 tok/s | 49K · 93K (8-bit) |
| Mistral-Nemo-Instruct-2407 Older | Q4_K_M | 7.48 GB | Fits | ~69 tok/s | 68K · 128K (8-bit) |
| gemma-3-12b-it Older | Q4_K_M | 7.3 GB | Fits | ~71 tok/s | 128K (full) |
| NVIDIA-Nemotron-Nano-9B-v2 Older | Q4_K_M | 6.53 GB | Fits | ~66 tok/s | 128K (full) |
| Qwen3-8B Older | Q4_K_M | 5.03 GB | Fits | ~99 tok/s | 40K (full) |
| Llama-3.1-8B-Instruct Older | Q4_K_M | 4.92 GB | Fits | ~101 tok/s | 104K · 128K (8-bit) |
| Qwen2.5-Coder-7B-Instruct Older | Q4_K_M | 4.68 GB | Fits | ~105 tok/s | 32K (full) |
| Mistral-7B-Instruct-v0.3 Older | Q4_K_M | 4.37 GB | Fits | ~112 tok/s | 32K (full) |
| Qwen3-4B Older | Q4_K_M | 2.5 GB | Fits | ~177 tok/s | 40K (full) |
| gemma-3-4b-it Older | Q4_K_M | 2.49 GB | Fits | ~178 tok/s | 128K (full) |
| Llama-3.2-3B-Instruct Older | Q4_K_M | 2.02 GB | Fits | ~209 tok/s | 128K (full) |
| Qwen3-0.6B Older | Q4_K_M | 0.48 GB | Fits | ~487 tok/s | 40K (full) |
| IBM Granite 4.2 30B | Q4_K_M | 17.72 GB | Fits | ~31 tok/s | 4K · 8K (8-bit) Tight |
| 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