<![CDATA[AI生成了一个LLM GPU显存计算器]]>deepseekv4 pro生成：

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  <title>LLM 显存计算器 · GPU内存需求估算</title>
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      background: linear-gradient(135deg, #6366f1, #a78bfa);
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    .result-card {
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      grid-column: 1 / -1; position: relative; overflow: hidden;
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    .result-card::before {
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      position: absolute; top: -60px; right: -60px;
      width: 200px; height: 200px; border-radius: 50%;
      background: radial-gradient(circle, rgba(99,102,241,0.15), transparent 70%);
      pointer-events: none;
    }

    .vram-total {
      font-size: 4rem; font-weight: 800; letter-spacing: -2px;
      font-variant-numeric: tabular-nums;
      background: linear-gradient(135deg, #818cf8, #c084fc);
      -webkit-background-clip: text; -webkit-text-fill-color: transparent;
      line-height: 1; margin: 8px 0 4px;
      transition: all 0.3s;
    }
    .vram-unit { font-size: 1.3rem; font-weight: 500; color: var(--muted); }
    .vram-label { font-size: 0.8rem; letter-spacing: 0.1em; text-transform: uppercase; color: var(--muted); }

    .breakdown { margin-top: 24px; }
    .breakdown-row { display: flex; align-items: center; gap: 10px; margin-bottom: 10px; }
    .breakdown-dot {
      width: 10px; height: 10px; border-radius: 50%; flex-shrink: 0;
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    .breakdown-name { font-size: 0.82rem; color: #94a3b8; width: 130px; flex-shrink: 0; text-align: left; }
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    .gpu-grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(140px, 1fr)); gap: 10px; margin-top: 8px; }
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      border: 1px solid var(--card-border);
      border-radius: 10px; padding: 10px 12px;
      display: flex; flex-direction: column; gap: 4px;
      transition: border-color 0.25s, background 0.25s, opacity 0.25s;
    }
    .gpu-tile.fits {
      border-color: rgba(34,211,160,0.4);
      background: rgba(34,211,160,0.06);
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    .gpu-tile.tight {
      border-color: rgba(245,158,11,0.4);
      background: rgba(245,158,11,0.06);
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    .gpu-tile.nope { opacity: 0.35; }
    .gpu-name { font-size: 0.75rem; font-weight: 600; color: #cbd5e1; }
    .gpu-mem { font-size: 0.72rem; color: var(--muted); }
    .gpu-status { font-size: 0.68rem; font-weight: 700; letter-spacing: 0.05em; margin-top: 2px; }
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    .gpu-tile.tight .gpu-status { color: var(--yellow); }
    .gpu-tile.nope .gpu-status  { color: var(--muted); }

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  </style>
</head>
<body>
<div class="page">
  <header>
    <h1>LLM 显存计算器</h1>
    <p>估算大语言模型推理所需的 GPU 显存</p>
  </header>

  <div class="grid">
    <!-- 左侧：控制区 -->
    <div class="card">
      <h2>模型参数</h2>

      <!-- 模型大小（档位已扩展：增加20B和27B） -->
      <div class="slider-group">
        <div class="slider-header">
          <span class="slider-label">模型大小</span>
          <span class="slider-value" id="val-model">7 B</span>
        </div>
        <input type="range" id="sl-model" min="0" max="15" step="1" value="4">
        <div class="tick-row">
          <span class="tick">0.5B</span><span class="tick">7B</span>
          <span class="tick">70B</span><span class="tick">671B</span>
        </div>
      </div>

      <!-- 权重量化 -->
      <div class="slider-group">
        <div class="slider-header">
          <span class="slider-label">权重量化</span>
          <span class="slider-value" id="val-quant">FP16 (16-bit)</span>
        </div>
        <input type="range" id="sl-quant" min="0" max="7" step="1" value="6">
        <div class="tick-row">
          <span class="tick">2-bit</span><span class="tick">Q4</span>
          <span class="tick">Q8</span><span class="tick">FP32</span>
        </div>
      </div>

      <!-- 上下文长度 -->
      <div class="slider-group">
        <div class="slider-header">
          <span class="slider-label">上下文长度</span>
          <span class="slider-value" id="val-ctx">4 K tokens</span>
        </div>
        <input type="range" id="sl-ctx" min="0" max="11" step="1" value="3">
        <div class="tick-row">
          <span class="tick">512</span><span class="tick">4K</span>
          <span class="tick">128K</span><span class="tick">1M</span>
        </div>
      </div>

      <!-- KV Cache 量化 -->
      <div class="slider-group">
        <div class="slider-header">
          <span class="slider-label">KV Cache 量化</span>
          <span class="slider-value" id="val-kv">FP16 (16-bit)</span>
        </div>
        <input type="range" id="sl-kv" min="0" max="2" step="1" value="2">
        <div class="tick-row">
          <span class="tick">4-bit</span><span class="tick">8-bit</span>
          <span class="tick">FP16</span>
        </div>
      </div>

      <!-- 批量大小 -->
      <div class="slider-group" style="margin-bottom:0">
        <div class="slider-header">
          <span class="slider-label">批量大小</span>
          <span class="slider-value" id="val-batch">1</span>
        </div>
        <input type="range" id="sl-batch" min="0" max="5" step="1" value="0">
        <div class="tick-row">
          <span class="tick">1</span><span class="tick">4</span>
          <span class="tick">16</span><span class="tick">32</span>
        </div>
      </div>
    </div>

    <!-- 右侧：架构信息 + 公式 -->
    <div class="card">
      <h2>估算架构</h2>
      <div class="arch-grid">
        <div class="arch-item"><div class="arch-val" id="arch-layers">32</div><div class="arch-key">层数</div></div>
        <div class="arch-item"><div class="arch-val" id="arch-hidden">4096</div><div class="arch-key">隐藏维度</div></div>
        <div class="arch-item"><div class="arch-val" id="arch-kv-heads">8</div><div class="arch-key">KV 头数</div></div>
        <div class="arch-item"><div class="arch-val" id="arch-head-dim">128</div><div class="arch-key">头维度</div></div>
      </div>

      <div style="margin-top:20px">
        <h2>显存公式</h2>
        <div class="formula-note">
          <b style="color:#c4b5fd">模型权重</b> = 参数量 × 每参数字节数<br>
          <b style="color:#34d399">KV Cache</b> = 长度 × 批量 × 2 × 层数 × kv_heads × head_dim × kv字节数<br>
          <b style="color:#f59e0b">运行时开销</b> = (权重 + KV cache) × 10%<br><br>
          <code>总显存 = 权重 + KV Cache + 开销</code>
        </div>
      </div>

      <div style="margin-top:20px">
        <h2>量化字节/参数量</h2>
        <div class="formula-note">
          2-bit <code>0.25 B</code> · 3-bit <code>0.375 B</code> · 4-bit <code>0.5 B</code> ·
          5-bit <code>0.625 B</code> · 6-bit <code>0.75 B</code> · 8-bit / Q8 <code>1 B</code> ·
          FP16 / BF16 <code>2 B</code> · FP32 <code>4 B</code>
        </div>
      </div>
    </div>

    <!-- 全宽：总显存结果 -->
    <div class="result-card">
      <div class="vram-label">所需显存</div>
      <div class="vram-total" id="vram-total">0.00</div>
      <div class="vram-unit">GB</div>

      <div class="breakdown" id="breakdown">
        <div class="breakdown-row">
          <div class="breakdown-dot" style="background:#818cf8"></div>
          <div class="breakdown-name">模型权重</div>
          <div class="breakdown-bar-wrap"><div class="breakdown-bar" id="bar-weights" style="background:#818cf8; width:0%"></div></div>
          <div class="breakdown-gb" id="gb-weights">0.00 GB</div>
        </div>
        <div class="breakdown-row">
          <div class="breakdown-dot" style="background:#34d399"></div>
          <div class="breakdown-name">KV Cache</div>
          <div class="breakdown-bar-wrap"><div class="breakdown-bar" id="bar-kv" style="background:#34d399; width:0%"></div></div>
          <div class="breakdown-gb" id="gb-kv">0.00 GB</div>
        </div>
        <div class="breakdown-row">
          <div class="breakdown-dot" style="background:#f59e0b"></div>
          <div class="breakdown-name">运行时开销</div>
          <div class="breakdown-bar-wrap"><div class="breakdown-bar" id="bar-oh" style="background:#f59e0b; width:0%"></div></div>
          <div class="breakdown-gb" id="gb-oh">0.00 GB</div>
        </div>
      </div>
    </div>

    <!-- 全宽：GPU 兼容性 -->
    <div class="card" style="grid-column: 1 / -1">
      <h2>GPU 兼容性</h2>
      <div class="gpu-grid" id="gpu-grid"></div>
    </div>
  </div>
</div>

<script>
  // ---------- 模型档位（增加了 20B 和 27B）----------
  const MODEL_STEPS = [
    { label: '0.5B',  b: 0.5  },
    { label: '1B',    b: 1    },
    { label: '1.5B',  b: 1.5  },
    { label: '3B',    b: 3    },
    { label: '7B',    b: 7    },
    { label: '8B',    b: 8    },
    { label: '13B',   b: 13   },
    { label: '20B',   b: 20   },   // 新增
    { label: '27B',   b: 27   },   // 新增
    { label: '30B',   b: 30   },
    { label: '34B',   b: 34   },
    { label: '70B',   b: 70   },
    { label: '72B',   b: 72   },
    { label: '120B',  b: 120  },
    { label: '405B',  b: 405  },
    { label: '671B',  b: 671  }
  ];

  const QUANT_STEPS = [
    { label: '2-bit',        bpw: 0.25 },
    { label: '3-bit',        bpw: 0.375 },
    { label: 'Q4 (4-bit)',   bpw: 0.5 },
    { label: 'Q5 (5-bit)',   bpw: 0.625 },
    { label: 'Q6 (6-bit)',   bpw: 0.75 },
    { label: 'Q8 (8-bit)',   bpw: 1.0 },
    { label: 'FP16 (16-bit)',bpw: 2.0 },
    { label: 'FP32 (32-bit)',bpw: 4.0 }
  ];

  const CTX_STEPS = [
    { label: '512',   v: 512    },
    { label: '1K',    v: 1024   },
    { label: '2K',    v: 2048   },
    { label: '4K',    v: 4096   },
    { label: '8K',    v: 8192   },
    { label: '16K',   v: 16384  },
    { label: '32K',   v: 32768  },
    { label: '64K',   v: 65536  },
    { label: '128K',  v: 131072 },
    { label: '256K',  v: 262144 },
    { label: '512K',  v: 524288 },
    { label: '1M',    v: 1048576}
  ];

  const KV_STEPS = [
    { label: '4-bit',        bpw: 0.5 },
    { label: 'Q8 (8-bit)',   bpw: 1.0 },
    { label: 'FP16 (16-bit)',bpw: 2.0 }
  ];

  const BATCH_STEPS = [1, 2, 4, 8, 16, 32];

  const GPUS = [
    { name: 'RTX 3060',    vram: 12  },
    { name: 'RTX 3090',    vram: 24  },
    { name: 'RTX 4070',    vram: 12  },
    { name: 'RTX 4090',    vram: 24  },
    { name: 'RTX 5090',    vram: 32  },
    { name: 'A10',         vram: 24  },
    { name: 'A100 40G',    vram: 40  },
    { name: 'A100 80G',    vram: 80  },
    { name: 'H100 80G',    vram: 80  },
    { name: 'H100 NVL',    vram: 94  },
    { name: 'H200',        vram: 141 },
    { name: 'B200',        vram: 192 },
    { name: '2× H100',     vram: 160 },
    { name: '4× H100',     vram: 320 },
    { name: '8× H100',     vram: 640 },
    { name: '8× B200',     vram: 1536}
  ];

  // 根据参数量估算架构（与第一版完全一致）
  function estimateArch(b) {
    const layers   = Math.max(8, Math.round(14 * Math.pow(b, 0.30)));
    const hidden   = Math.round(2048 * Math.pow(b, 0.285) / 64) * 64;
    const kvHeads  = b >= 200 ? 16 : 8;
    const headDim  = 128;
    return { layers, hidden, kvHeads, headDim };
  }

  // 核心计算
  function calculate() {
    const modelB   = MODEL_STEPS[+document.getElementById('sl-model').value].b;
    const quant    = QUANT_STEPS[+document.getElementById('sl-quant').value];
    const ctx      = CTX_STEPS[+document.getElementById('sl-ctx').value].v;
    const kv       = KV_STEPS[+document.getElementById('sl-kv').value];
    const batch    = BATCH_STEPS[+document.getElementById('sl-batch').value];

    const arch = estimateArch(modelB);

    const weightsGB = modelB * 1e9 * quant.bpw / (1024 ** 3);
    const kvGB = 2 * arch.layers * arch.kvHeads * arch.headDim * ctx * batch * kv.bpw / (1024 ** 3);
    const overheadGB = (weightsGB + kvGB) * 0.10;
    const totalGB = weightsGB + kvGB + overheadGB;

    return { weightsGB, kvGB, overheadGB, totalGB, arch };
  }

  function fmt(n) {
    if (n < 10)    return n.toFixed(2);
    if (n < 100)   return n.toFixed(1);
    return Math.round(n).toString();
  }

  function renderLabels() {
    const m = MODEL_STEPS[+document.getElementById('sl-model').value];
    const q = QUANT_STEPS[+document.getElementById('sl-quant').value];
    const c = CTX_STEPS[+document.getElementById('sl-ctx').value];
    const k = KV_STEPS[+document.getElementById('sl-kv').value];
    const b = BATCH_STEPS[+document.getElementById('sl-batch').value];

    document.getElementById('val-model').textContent  = m.label;
    document.getElementById('val-quant').textContent  = q.label;
    document.getElementById('val-ctx').textContent    = c.label + ' tokens';
    document.getElementById('val-kv').textContent     = k.label;
    document.getElementById('val-batch').textContent  = b;
  }

  function renderArch(arch) {
    document.getElementById('arch-layers').textContent   = arch.layers;
    document.getElementById('arch-hidden').textContent   = arch.hidden.toLocaleString();
    document.getElementById('arch-kv-heads').textContent = arch.kvHeads;
    document.getElementById('arch-head-dim').textContent = arch.headDim;
  }

  function renderResult({ weightsGB, kvGB, overheadGB, totalGB }) {
    const el = document.getElementById('vram-total');
    el.textContent = fmt(totalGB);
    el.classList.remove('pop');
    void el.offsetWidth;
    el.classList.add('pop');

    document.getElementById('gb-weights').textContent = fmt(weightsGB) + ' GB';
    document.getElementById('gb-kv').textContent      = fmt(kvGB) + ' GB';
    document.getElementById('gb-oh').textContent      = fmt(overheadGB) + ' GB';

    const maxBar = Math.max(weightsGB, kvGB, overheadGB, 0.01);
    document.getElementById('bar-weights').style.width = (weightsGB  / maxBar * 100) + '%';
    document.getElementById('bar-kv').style.width      = (kvGB       / maxBar * 100) + '%';
    document.getElementById('bar-oh').style.width      = (overheadGB / maxBar * 100) + '%';
  }

  function renderGPUs(totalGB) {
    const grid = document.getElementById('gpu-grid');
    grid.innerHTML = '';
    GPUS.forEach(gpu => {
      const ratio = totalGB / gpu.vram;
      let cls, status;
      if (ratio <= 0.85)      { cls = 'fits';  status = '✓ 够用';   }
      else if (ratio <= 1.0)  { cls = 'tight'; status = '⚠ 紧张';  }
      else                    { cls = 'nope';  status = '✗ 不足'; }

      const pct = Math.min(100, Math.round(ratio * 100));
      const tile = document.createElement('div');
      tile.className = `gpu-tile ${cls}`;
      tile.innerHTML = `
        <div class="gpu-name">${gpu.name}</div>
        <div class="gpu-mem">${gpu.vram} GB</div>
        <div class="gpu-status">${status} (${pct}%)</div>
      `;
      grid.appendChild(tile);
    });
  }

  function update() {
    renderLabels();
    const result = calculate();
    renderArch(result.arch);
    renderResult(result);
    renderGPUs(result.totalGB);
  }

  // 绑定滑块事件
  ['sl-model', 'sl-quant', 'sl-ctx', 'sl-kv', 'sl-batch'].forEach(id => {
    document.getElementById(id).addEventListener('input', update);
  });

  update();
</script>
</body>
</html>
]]>https://lcz.me/topic/452/ai生成了一个llm-gpu显存计算器RSS for NodeThu, 11 Jun 2026 16:10:33 GMTSat, 06 Jun 2026 14:22:31 GMT60<![CDATA[Reply to AI生成了一个LLM GPU显存计算器 on Mon, 08 Jun 2026 09:45:54 GMT]]>@艳阳天 Q5_K_M对7900XTX 24GB来说确实很勉强。我来算一下：

Qwen3.6-27B的Q5_K_M体积大约19-20GB，加上系统占用约1-2GB，总共21-22GB。剩下2-3GB给KV Cache和context，如果上下文超过2-4K就可能OOM。而Q4_K_M约16-17GB，能留出6-7GB给KV Cache，跑32-64K上下文很稳。

所以24GB卡跑27B模型，Q4_K_M确实是实际可用的上限。那个计算器说"适用Q5"可能没考虑系统开销和上下文显存。如果你一定要试Q5，可以：

  1. 把--ctx-size降到4096以下
  2. 关掉MTP（能省1-2GB）
  3. KV Cache用Q4甚至Q4_0量化

但体验上不如直接Q4_K_M开128K上下文来得实用。

]]>https://lcz.me/post/5712https://lcz.me/post/5712Mon, 08 Jun 2026 09:45:54 GMT<![CDATA[Reply to AI生成了一个LLM GPU显存计算器 on Mon, 08 Jun 2026 04:55:10 GMT]]>我用它測試我的主機,7900XTX 24GB,說適用QWEN3.6-27B Q5, 我現在用Q4,開機後顯存已經91%,怕Q5會OOM, 有人試過裝Q5的嗎?

]]>https://lcz.me/post/5668https://lcz.me/post/5668Mon, 08 Jun 2026 04:55:10 GMT<![CDATA[Reply to AI生成了一个LLM GPU显存计算器 on Sun, 07 Jun 2026 05:09:27 GMT]]>github上有个类似的项目: https://github.com/Andyyyy64/whichllm

]]>https://lcz.me/post/5443https://lcz.me/post/5443Sun, 07 Jun 2026 05:09:27 GMT<![CDATA[Reply to AI生成了一个LLM GPU显存计算器 on Sun, 07 Jun 2026 04:20:05 GMT]]>@AGI 可以，楼主可以优化下。你也可以发一个改进版。

]]>https://lcz.me/post/5437https://lcz.me/post/5437Sun, 07 Jun 2026 04:20:05 GMT<![CDATA[Reply to AI生成了一个LLM GPU显存计算器 on Mon, 08 Jun 2026 01:56:34 GMT]]>Key和value可以用不同的量化，这个可以优化下，我key一般8bit, value用4bit

]]>https://lcz.me/post/5419https://lcz.me/post/5419Mon, 08 Jun 2026 01:56:34 GMT<![CDATA[Reply to AI生成了一个LLM GPU显存计算器 on Sun, 07 Jun 2026 01:40:19 GMT]]>想法不错，大家可以下载测试下。

]]>https://lcz.me/post/5418https://lcz.me/post/5418Sun, 07 Jun 2026 01:40:19 GMT<![CDATA[Reply to AI生成了一个LLM GPU显存计算器 on Sat, 06 Jun 2026 14:25:15 GMT]]>PixPin_2026-06-06_22-24-44.png

]]>https://lcz.me/post/5377https://lcz.me/post/5377Sat, 06 Jun 2026 14:25:15 GMT