Will Qwen3.6, Gemma 4 or gpt-oss run on your PC?
Real measured tokens/sec from 63 home AI rigs, each linked to its source. No guesses, no marketing numbers.
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Popular 2026 models: Qwen3.8 27B · Qwen3.6 35B-A3B · Qwen3.6 27B · Muse Spark Open 816B-A42B · Gemma 4 31B · Gemma 4 26B-A4B · Gemma 4 12B · gpt-oss 120B · gpt-oss 20B · Qwen3.5 122B · Qwen3.5 9B · Mistral Medium 3.5 128B · Muse Glimmer 30B · Nemotron 3 Nano Omni 30B · GLM 4.7 Flash · LFM2.5 24B-A2B · Nemotron 3.5 Bolt 74B-A9B · Qwen3 235B-A22B · DeepSeek R1 671B
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| Build | GPU(s) | VRAM GB | Cost | Measured tok/s |
|---|---|---|---|---|
| Mac Studio M3 Ultra, 512 GB ⚠ unchecked | Apple M3 Ultra | 512 | DeepSeek R1 671B: 18.43 | |
| Mac Studio M3 Ultra 512GB (Geerling test) | Apple M3 Ultra (unified) | 512 | DeepSeek R1 671B: 19.89 gpt-oss 20B: 115.29 gpt-oss 20B: 115.52 Llama 3.1 70B: 14.08 Llama 3.1 405B: 1.96 DeepSeek R1 Distill 14B: 51.85 | |
| Mac Studio M3 Ultra 512GB: Muse Spark Open 816B (Hugging Face test) | Apple M3 Ultra (unified) | 512 | Muse Spark Open 816B-A42B: 18 Muse Spark Open 816B-A42B: 21 | |
| Mac Studio M5 Ultra 512GB: Qwen3.8 27B and Mistral Medium 3.5 (Apple MLX benchmark) | Apple M5 Ultra (unified) | 512 | Qwen3.8 27B: 19.69 Mistral Medium 3.5 128B: 7.7 | |
| Mac Studio M3 Ultra 512GB: Qwen3.8 27B and Mistral Medium 3.5 (Apple MLX benchmark) | Apple M3 Ultra (unified) | 512 | Qwen3.8 27B: 12.46 Mistral Medium 3.5 128B: 4.95 | |
| Mac Studio M2 Ultra 76-core GPU, 192 GB | Apple M2 Ultra 76-core GPU | 192 | Llama 2 7B: 125.21 gpt-oss 20B: 116.08 gpt-oss 120B: 79.68 | |
| EPYC 7F52 jank rig: 4x3090 + 4090 + 4080 (136 GB VRAM) ⚠ unchecked | 4x RTX 3090, RTX 4090, RTX 4080 | 136 | Llama 3.1 70B: 9 | |
| Mac Studio M1 Ultra (64-core GPU) 128GB | Apple M1 Ultra 64-core GPU (unified) | 128 | gpt-oss 20B: 100.43 DeepSeek R1 Distill 14B: 41.51 Llama 3.1 70B: 9.84 Llama 3.2 3B: 127.6 | |
| HP OmniBook Ultra 16 (RTX Spark N1X, 128GB) laptop: local AI benchmarks | Nvidia RTX Spark N1X, 6,144-core Blackwell GPU (unified) | 128 | $6,230 (≈€5,559) | Qwen3.6 35B-A3B: 89.5 gpt-oss 20B: 58.0 Llama 3.1 8B: 56.5 |
| 4x RTX 3090 in a 4U rack case (AM5) ⚠ unchecked | 4x Dell OEM RTX 3090 | 96 | €5,835 (≈$6,540) | |
| 4x RTX 3090 homelab on Threadripper 2920X ⚠ unchecked | 4x RTX 3090 (~$625 each, used) | 96 | $3,300 (≈€2,944) | |
| 4x RTX 3090 + 32-core Threadripper, 192 GB DDR4 ⚠ unchecked | 4x RTX 3090 | 96 | Qwen3 235B-A22B: 12-14 | |
| 4x RTX 3090 + EPYC 7443, 256 GB DDR4 8-channel ⚠ unchecked | 4x RTX 3090 | 96 | Qwen3 235B-A22B: 18 | |
| RTX Pro 6000 96GB workstation card | RTX Pro 6000 (Blackwell) | 96 | gpt-oss 120B: 196.31 gpt-oss 20B: 286.91 | |
| Strix Halo (Radeon 8060S, 96 GB): 2026 models (llamabench community) | AMD Ryzen AI Max+ (Radeon 8060S iGPU) | 96 | Qwen3.6 35B-A3B: 50.2-62.4 | |
| 4x RTX 3090 lab: Qwen3.6 on vLLM vs llama.cpp | 4x RTX 3090 | 96 | Qwen3.6 35B-A3B: 115-133 Qwen3.6 35B-A3B: 149 Qwen3.6 35B-A3B: 225-231 | |
| Mac Studio 64 GB ⚠ unchecked | Apple unified memory | 64 | $4,000 (≈€3,569) | Llama 3.3 70B: 11 |
| MacBook Pro M3 Max 40-core GPU, 64 GB | Apple M3 Max 40-core GPU | 64 | Llama 3 8B: 50.74 | |
| MacBook Pro M1 Max 32-core GPU, 64 GB | Apple M1 Max 32-core GPU | 64 | Llama 3 8B: 34.49 | |
| Apple M1 Max 64GB | Apple M1 Max (unified) | 64 | gpt-oss 20B: 75.15 | |
| Dual RTX 3090 on Ryzen 3700X (X470) ⚠ unchecked | 2x Zotac RTX 3090 | 48 | Llama 3 70B: 16.95 Llama 3 70B: 21.2 | |
| Dual used RTX 3090, ~2000 EUR total ⚠ unchecked | 2x RTX 3090 (used) | 48 | €2,000 (≈$2,242) | Llama 3.3 70B: 20 |
| RTX 3090 + RTX 4060 Ti 16GB mixed pair ⚠ unchecked | RTX 3090 + RTX 4060 Ti 16GB | 40 | Llama 3.3 70B: 11 | |
| Apple M4 Max 36GB | Apple M4 Max (unified) | 36 | gpt-oss 20B: 92.36 | |
| Intel Core Ultra 265K + Radeon AI Pro R9700 | AMD Radeon AI Pro R9700 | 32 | gpt-oss 20B: 163.26 DeepSeek R1 Distill 14B: 53.72 Llama 3.2 3B: 195.91 | |
| Intel i9-13900K + RTX 5090 | RTX 5090 | 32 | Llama 3.2 3B: 271.4 | |
| M4 Mac mini (10-core) 32GB | Apple M4 (unified) | 32 | Llama 3.2 3B: 41.31 | |
| Single RTX 5090 32GB | RTX 5090 | 32 | gpt-oss 20B: 282.51 | |
| RTX 5090: 2026 models (llamabench community) | RTX 5090 | 32 | Qwen3.6 35B-A3B: 258 Qwen3.6 27B: 75.7 Gemma 4 31B: 70.0 Gemma 4 26B-A4B: 235 Gemma 4 12B: 142 gpt-oss 20B: 313 Qwen3.5 9B: 208 | |
| Single RTX 4090 (llama.cpp CUDA scoreboard) | RTX 4090 24GB | 24 | Llama 2 7B: 188.96 | |
| RTX 4090 desktop (speed comparison vs Macs) | RTX 4090 24GB | 24 | Llama 3.1 8B: 52.99 Mistral Small 22B: 26.72 | |
| EPYC + single RTX 3090 (hybrid) ⚠ unchecked | RTX 3090 | 24 | DeepSeek R1 671B: 8.97 | |
| Intel Core Ultra 265K + RTX 4090 | RTX 4090 | 24 | gpt-oss 20B: 279.65 DeepSeek R1 Distill 14B: 92.49 Llama 3.2 3B: 334.67 | |
| Ryzen 9 7900X + RTX 4090 | RTX 4090 | 24 | Llama 3.2 3B: 237.05 Llama 3.1 70B: 3.1 | |
| Single RTX 3090 24GB | RTX 3090 | 24 | gpt-oss 20B: 161.77 | |
| RTX 4090: 2026 models (llamabench community) | RTX 4090 | 24 | Qwen3.6 27B: 47.0 Gemma 4 31B: 43.8 Gemma 4 26B-A4B: 164 Gemma 4 12B: 98.1 gpt-oss 20B: 203 Qwen3.5 9B: 137 LFM2.5 24B-A2B: 261-339 Nemotron 3.5 Bolt 74B-A9B: 108 | |
| RTX 3090: 2026 models (llamabench community) | RTX 3090 | 24 | Qwen3.6 35B-A3B: 146-153 Qwen3.6 27B: 40.2 Qwen3.8 27B: 38.2-71.6 | |
| Single RTX 3090: Qwen3.6 35B-A3B (Giles Thomas) | RTX 3090 | 24 | Qwen3.6 35B-A3B: 139.6 | |
| Single RTX 3090: Qwen3.8 27B at 78-140 tok/s with the OpenJet megakernel vs llama.cpp | RTX 3090 | 24 | Qwen3.8 27B: 139.7 Qwen3.8 27B: 72.5 Qwen3.8 27B: 78.0 Qwen3.8 27B: 50.9 Qwen3.8 27B: 40.4 Qwen3.8 27B: 42.9 | |
| Ryzen 9 9950X + Radeon RX 7900 XT | AMD Radeon RX 7900 XT | 20 | Llama 3.2 3B: 131.2 | |
| Single RTX 4060 Ti 16GB + 64 GB RAM (partial offload) ⚠ unchecked | RTX 4060 Ti 16GB | 16 | Llama 3.3 70B: 1.5 | |
| Intel Core Ultra 265K + RTX A4000 | RTX A4000 | 16 | gpt-oss 20B: 93.55 Llama 3.2 3B: 149.44 | |
| Ryzen 9 7950X + RTX 4080 | RTX 4080 | 16 | Llama 3.2 3B: 204.45 | |
| Ryzen 9 7950X + RTX 4070 Ti Super | RTX 4070 Ti Super | 16 | Llama 3.2 3B: 198.95 | |
| Single RTX 5070 Ti 16GB | RTX 5070 Ti | 16 | gpt-oss 20B: 189.45 | |
| Single RTX 5060 Ti 16GB | RTX 5060 Ti 16GB | 16 | gpt-oss 20B: 111.51 | |
| Single RTX 5080 16GB | RTX 5080 | 16 | gpt-oss 20B: 204.85 | |
| RTX 5070 Ti: 2026 models (llamabench community) | RTX 5070 Ti | 16 | Gemma 4 12B: 87.6 | |
| RTX 4060 Ti 16GB: 2026 models (llamabench community) | RTX 4060 Ti 16GB | 16 | LFM2.5 24B-A2B: 131-150 Qwen3.8 27B: 10.7-17.6 | |
| Intel Core Ultra 265K + RTX 3080 Ti | RTX 3080 Ti | 12 | DeepSeek R1 Distill 14B: 70.74 Llama 3.2 3B: 263.24 | |
| Intel Core Ultra 265K + RTX 4070 Ti | RTX 4070 Ti | 12 | gpt-oss 20B: 163.14 DeepSeek R1 Distill 14B: 49.74 Llama 3.2 3B: 196.15 | |
| Intel Core Ultra 265K + RTX 3060 | RTX 3060 | 12 | gpt-oss 20B: 83.96 DeepSeek R1 Distill 14B: 29.77 Llama 3.2 3B: 122.85 | |
| RTX 3060 12GB: 2026 models (llamabench community) | RTX 3060 | 12 | Gemma 4 12B: 38.5 LFM2.5 24B-A2B: 125 Qwen3.8 27B: 5.2-6.7 | |
| Intel Core Ultra 265K + RTX 2080 Ti | RTX 2080 Ti | 11 | DeepSeek R1 Distill 14B: 45.06 Llama 3.2 3B: 180.38 | |
| MacBook Pro M2 Max | Apple M2 Max | Llama 3.1 8B: 28.92 | ||
| Mac Studio M1 Ultra | Apple M1 Ultra | Llama 3.1 8B: 37.92 | ||
| $2K EPYC 64-core, 512 GB DDR4, CPU-only | None (CPU inference) | 0 | $2,000 (≈€1,784) | DeepSeek R1 671B: 4.31 |
| Framework Desktop (AMD Strix Halo) mainboard | AMD Ryzen AI Max+ 395 (these Ollama runs used CPU only) | DeepSeek R1 Distill 8B: 19.49 DeepSeek R1 Distill 70B: 2.43 Llama 3.1 70B: 4.97 Llama 3.2 3B: 88.14 | ||
| Dell Pro Max with GB10 (Nvidia DGX Spark) | Nvidia GB10 (unified) | gpt-oss 20B: 87.97 DeepSeek R1 Distill 14B: 23.92 Llama 3.1 70B: 4.71 Llama 3.2 3B: 97.93 | ||
| AmpereOne A192-32X 512GB (CPU only) | None (CPU only) | DeepSeek R1 671B: 4.18 Llama 3.1 70B: 3.86 Llama 3.1 405B: 0.9 Llama 3.2 3B: 23.52 | ||
| 2x Mac Studio M5 Ultra cluster (Thunderbolt 5 RDMA): Muse Spark Open 816B | 2x Apple M5 Ultra (unified, Mac Studio) | Muse Spark Open 816B-A42B: 24.99 Muse Spark Open 816B-A42B: 15.74 Mistral Medium 3.5 128B: 13.69 Qwen3.8 27B: 33.27 | ||
| 4x Mac Studio M5 Ultra cluster (Thunderbolt 5 RDMA): Muse Spark Open at 36.7 tok/s | 4x Apple M5 Ultra (unified, Mac Studio) | Muse Spark Open 816B-A42B: 36.73 Mistral Medium 3.5 128B: 23.37 Qwen3.8 27B: 50.89 | ||
| Microsoft Surface Laptop Ultra (RTX Spark N1X): llama.cpp speeds on 2026 models (Micro Center) | Nvidia RTX Spark N1X, 6,144-core Blackwell GPU (unified) | Nemotron 3 Nano Omni 30B: 63.9 GLM 4.7 Flash: 55.6 Qwen3.5 122B: 35.2 Muse Glimmer 30B: 15.9 Qwen3.8 27B: 14.5 |
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