ai-local-lab

広告

GPU搭載PC

RTX A6000 48GB ワークステーション

memory_gb はVRAM容量。自宅GPU上限帯の比較基準として掲載

スペック

RTX A6000 48GB ワークステーションの主要スペック
ベンダーNVIDIA
カテゴリGPU搭載PC
メモリ48 GB
メモリ帯域768 GB/s
AI性能
最大消費電力300 W

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価格・在庫は変動します。最新はリンク先でご確認ください。広告

この機材で計測したモデル

全計測データ

RTX A6000 48GB ワークステーションの全ベンチマーク計測
モデル量子化ランタイムtok/sTTFT(ms)温度(℃)計測日
GLM-OCRQ8_0ollama 0.31.2473.2104592026-07-15
MiniCPM-V 4.6Q4_K_Mollama 0.31.2298.7270592026-07-15
Nemotron 3 Nano 4BQ4_K_Mollama 0.31.2166.5229642026-07-15
Qwen2.5 Coder 7B InstructQ4_K_Mollama 0.31.2115.9165602026-07-15
Qwen3-VL 8BQ4_K_Mollama 0.31.2104.1200562026-07-15
Gemma 3 4BQ4_K_Mollama 0.31.2146.4409612026-07-14
Qwen2.5 0.5BQ4_K_Mollama 0.31.2423.4159502026-07-14
Qwen2.5 14BQ4_K_Mollama 0.31.264.3178672026-07-14
Qwen2.5 Coder 32B InstructQ4_K_Mollama 0.31.229.8205702026-07-14
Qwen3 30B (A3B)Q4_K_Mollama 0.31.2160.6171552026-07-14
Qwen3 30B (A3B)Q8_0ollama 0.31.2124.3165592026-07-14
Qwen3.5 2BQ8_0ollama 0.31.2188.9297582026-07-14
Qwen3-VL 2BQ4_K_Mollama 0.31.2268.2180562026-07-14
Llama-3-ELYZA-JP-8BQ4_K_Mollama 0.31.1112.2189642026-07-07
gpt-oss 20B (MoE)MXFP4ollama 0.31.1132.8331602026-07-07
LLM-jp-4 8B (thinking)Q4_K_Mollama 0.31.1106.0148682026-07-07
Sarashina 2.2 3B InstructQ4_K_Mollama 0.31.1180.999612026-07-07
Llama 3.1 Swallow 8B Instruct v0.5Q4_K_Mollama 0.31.1111.9191682026-07-07
LFM2.5 1.2B JPQ4_K_Mollama 0.30.8448.981652026-07-02
LFM2.5 230MQ8_0ollama 0.30.8735.882602026-07-02
Ministral 3 14BQ4_K_Mollama 0.30.862.3239642026-07-02
Ministral 3 3BQ4_K_Mollama 0.30.8177.5217532026-07-02
Ministral 3 8BQ4_K_Mollama 0.30.894.7224592026-07-02
DeepSeek-R1 Distill Qwen 32BQ4_K_Mollama 0.30.830.4210792026-06-22
Falcon3 7B InstructQ4_K_Mollama 0.30.8117.4139692026-06-22
Gemma 3 27BQ4_K_Mollama 0.30.833.0474762026-06-22
Mistral Nemo (12B)Q4_0ollama 0.30.881.4202662026-06-22
Mistral Small 3 (24B)Q4_K_Mollama 0.30.842.9217622026-06-22
Mixtral 8x7B Instruct (MoE)Q4_0ollama 0.30.878.849702026-06-22
Qwen3.6 27BQ4_K_Mollama 0.30.833.5374782026-06-22
Teuken 7B Instruct (EU)F16ollama 0.30.842.3205802026-06-22
Granite 3.3 8B InstructQ4_K_Mollama 0.30.8102.283592026-06-21
Mistral 7B Instruct v0.3F16ollama 0.30.828.876772026-06-21
Mistral 7B Instruct v0.3Q4_K_Mollama 0.30.8114.847792026-06-21
Mistral 7B Instruct v0.3Q8_0ollama 0.30.873.753792026-06-21
Phi-4Q4_K_Mollama 0.30.865.6131652026-06-21
Qwen2.5 32B InstructQ4_K_Mollama 0.30.830.6195682026-06-21
Llama 3.1 8B InstructF16ollama 0.30.843.1214712026-06-18
Llama 3.1 8B InstructQ4_K_Mollama 0.30.8111.2199572026-06-18
Llama 3.1 8B InstructQ8_0ollama 0.30.873.6200632026-06-18
Qwen2.5 7B InstructF16ollama 0.30.845.1172692026-06-18
Qwen2.5 7B InstructQ4_K_Mollama 0.30.8117.4151572026-06-18
Qwen2.5 7B InstructQ8_0ollama 0.30.877.2161622026-06-18
Gemma 3 1BQ4_K_Mollama 0.30.7245.4386552026-06-16
Gemma 4 E2B (有効2B / 実5.1B)Q4_K_Mollama 0.30.7166.5389632026-06-16
Gemma 4 E4B (有効4B / 実8.0B)Q4_K_Mollama 0.30.7110.5398682026-06-16
Llama 3.2 1B InstructQ8_0ollama 0.30.7315.8182532026-06-16
Llama 3.2 3B InstructQ4_K_Mollama 0.30.7201.8193552026-06-16
Phi-4-mini InstructQ4_K_Mollama 0.30.7166.8205602026-06-16
Qwen2.5 1.5B InstructQ4_K_Mollama 0.30.7282.1153532026-06-16
Qwen2.5 3B InstructQ4_K_Mollama 0.30.7191.3154612026-06-16
Qwen3 1.7BQ4_K_Mollama 0.30.7294.2153572026-06-16
Qwen3.5 0.8BQ8_0ollama 0.30.7266.9283532026-06-16
SmolLM2 1.7B InstructQ8_0ollama 0.30.7242.468632026-06-16
Gemma 4 26B (A4B)Q4_K_Mollama 0.30.799.6407642026-06-12
LFM2.5 8B A1BQ4_K_Mollama 0.30.7283.9205612026-06-12
Qwen3.5 4BQ4_K_Mollama 0.30.7123.5313602026-06-12
Qwen3.6 35B (A3B)Q4_K_Mollama 0.30.7122.1314672026-06-12

※ 数値はすべて自前機材での実測です(ollama API・2回平均・num_predict=256)。

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