{
  "model_id": "qwen3.6_uncensored_ollama_q4km",
  "model": "fredrezones55/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive:latest",
  "task_id": "rs_04",
  "category": "reasoning",
  "slug": "bandwidth_tps_calc",
  "timestamp": "2026-04-19T18:48:56",
  "ok": true,
  "wall_s": 36.603,
  "prompt_tokens": 116,
  "completion_tokens": 1692,
  "tok_s_decode": 46.23,
  "finish_reason": "stop",
  "turns": [
    {
      "id": "chatcmpl-86",
      "object": "chat.completion",
      "created": 1776592136,
      "model": "fredrezones55/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive:latest",
      "system_fingerprint": "fp_ollama",
      "choices": [
        {
          "index": 0,
          "message": {
            "role": "assistant",
            "content": "Here's the step-by-step calculation:\n\n### (1) Theoretical Maximum Tokens/Second\n- **Active parameters per token**: 3B  \n- **Precision**: 2 bytes/parameter (standard FP16/BF16 for inference)  \n- **Weight data per token**: `3B × 2 bytes = 6 GB`  \n- **Available bandwidth**: 900 GB/s  \n\n\\[\n\\text{Theoretical max tokens/sec} = \\frac{900 \\text{ GB/s}}{6 \\text{ GB/token}} = \\mathbf{150 \\text{ tokens/sec}}\n\\]\n\n### (2) Realistic Range (30–50% of Peak)\nvLLM and similar inference engines typically achieve 30–50% of theoretical memory bandwidth due to:\n- Memory controller overhead & fragmentation\n- KV cache management & attention compute\n- Weight decompression, routing, and expert dispatch latency\n- System-level scheduling and PCIe/NVLink protocol overhead\n\n\\[\n\\text{Lower bound} = 150 \\times 0.30 = 45 \\text{ tokens/sec}\n\\]\n\\[\n\\text{Upper bound} = 150 \\times 0.50 = 75 \\text{ tokens/sec}\n\\]\n\n**Realistic range: `45–75 tokens/sec`**\n\n---\n💡 **Notes**:  \n- This assumes the 900 GB/s NVLink-C2C link is fully dedicated to streaming active MoE weights per token.  \n- Actual throughput may vary slightly based on sequence length, batch size, expert routing patterns, and whether weights are cached in GPU HBM vs. streamed fresh each step.  \n- For longer sequences, attention compute (FLOPs) may eventually dominate, shifting the bottleneck from memory bandwidth to compute.",
            "reasoning": "Here's a thinking process:\n\n1.  **Understand User Input:**\n   - Chip: GB10 (likely refers to NVIDIA Grace Blackwell GB10 or similar, but the key spec given is NVLink-C2C bandwidth = 900 GB/s)\n   - Model: 35B MoE (Mixture of Experts)\n   - Active parameters: 3B\n   - Weight data per token: active_params × 2 bytes (assuming FP16/BF16 precision, which is standard for inference)\n   - Questions:\n     (1) Theoretical maximum tokens per second?\n     (2) Realistic range given vLLM achieves 30-50% of theoretical peak?\n\n2.  **Identify Key Variables & Formulas:**\n   - Bandwidth (B) = 900 GB/s = 900 × 10^9 bytes/s\n   - Active parameters (P) = 3B = 3 × 10^9 parameters\n   - Bytes per parameter (BPP) = 2 bytes (FP16/BF16)\n   - Data per token (D) = P × BPP = 3 × 10^9 × 2 = 6 × 10^9 bytes = 6 GB\n   - Theoretical max tokens/sec (T_max) = Bandwidth / Data per token = B / D\n   - Realistic range = T_max × [0.30, 0.50]\n\n3.  **Calculate Theoretical Maximum:**\n   - D = 6 GB/token\n   - B = 900 GB/s\n   - T_max = 900 / 6 = 150 tokens/second\n\n4.  **Calculate Realistic Range:**\n   - Lower bound: 150 × 0.30 = 45 tokens/second\n   - Upper bound: 150 × 0.50 = 75 tokens/second\n   - Range: 45–75 tokens/second\n\n5.  **Check Assumptions & Context:**\n   - NVLink-C2C bandwidth: 900 GB/s is given. This is the inter-chip bandwidth. For a single GB10 chip, this might be the total available bandwidth for weight loading. I'll assume it's the effective bandwidth for weight streaming.\n   - MoE active parameters: 3B active out of 35B total. Only active weights are loaded per token. Correct.\n   - Precision: 2 bytes implies FP16/BF16. Standard for inference. Correct.\n   - vLLM efficiency: 30-50% is a reasonable assumption for memory-bound inference workloads on this architecture.\n   - Calculation: 900 / 6 = 150. Straightforward.\n   - Realistic: 45-75. Straightforward.\n\n6.  **Formulate Response:**\n   - State assumptions clearly (FP16/BF16 precision, bandwidth fully utilized for weight loading, single-chip scenario).\n   - Show step-by-step calculation.\n   - Provide theoretical max.\n   - Provide realistic range.\n   - Add brief context/notes (e.g., actual performance may vary due to KV cache, attention compute, memory overhead, etc.).\n\n   Structure:\n   - Step 1: Calculate bytes per token\n   - Step 2: Theoretical max tokens/sec\n   - Step 3: Realistic range (30-50%)\n   - Final answer clearly stated\n   - Caveats/notes\n\n   Draft:\n   (1) Theoretical maximum:\n   - Active parameters = 3B\n   - Precision = 2 bytes/param (FP16/BF16)\n   - Data per token = 3B × 2 = 6 GB\n   - Bandwidth = 900 GB/s\n   - Max tokens/sec = 900 / 6 = 150 tokens/sec\n\n   (2) Realistic range (30–50% efficiency):\n   - Lower: 150 × 0.30 = 45 tokens/sec\n   - Upper: 150 × 0.50 = 75 tokens/sec\n   - Range: 45–75 tokens/sec\n\n   Add notes: Assumes bandwidth is fully dedicated to weight streaming, single GB10 chip, FP16/BF16 precision, and memory-bound regime. Actual vLLM performance may vary due to KV cache management, attention compute, memory fragmentation, and system overhead.\n\n7.  **Self-Correction/Verification:**\n   - Check units: GB/s / GB/token = tokens/s. Correct.\n   - 3B × 2 = 6 GB. Correct.\n   - 900 / 6 = 150. Correct.\n   - 30-50% of 150 = 45-75. Correct.\n   - Is NVLink-C2C bandwidth typically the limiting factor for MoE weight loading? Yes, for large MoE models, weight streaming over interconnects is often memory-bound.\n   - Could there be a nuance about \"GB10\"? NVIDIA's Grace Blackwell GB10 has 2 Grace CPUs + 2 Blackwell GPUs, with NVLink-C2C between CPUs and GPUs, and NVLink-S2S between GPUs. The 900 GB/s likely refers to the CPU-GPU interconnect bandwidth. For inference, weights are typically loaded from GPU HBM, but for MoE, expert weights might be stored on CPU or streamed via NVLink-C2C. The problem states \"NVLink-C2C bandwidth of 900 GB/s\" and asks to use it for weight loading, so we'll stick with that.\n   - All good.\n\n   Final output matches draft. I'll present it clearly.✅"
          },
          "finish_reason": "stop"
        }
      ],
      "usage": {
        "prompt_tokens": 116,
        "completion_tokens": 1692,
        "total_tokens": 1808
      }
    }
  ],
  "final_text": "Here's the step-by-step calculation:\n\n### (1) Theoretical Maximum Tokens/Second\n- **Active parameters per token**: 3B  \n- **Precision**: 2 bytes/parameter (standard FP16/BF16 for inference)  \n- **Weight data per token**: `3B × 2 bytes = 6 GB`  \n- **Available bandwidth**: 900 GB/s  \n\n\\[\n\\text{Theoretical max tokens/sec} = \\frac{900 \\text{ GB/s}}{6 \\text{ GB/token}} = \\mathbf{150 \\text{ tokens/sec}}\n\\]\n\n### (2) Realistic Range (30–50% of Peak)\nvLLM and similar inference engines typically achieve 30–50% of theoretical memory bandwidth due to:\n- Memory controller overhead & fragmentation\n- KV cache management & attention compute\n- Weight decompression, routing, and expert dispatch latency\n- System-level scheduling and PCIe/NVLink protocol overhead\n\n\\[\n\\text{Lower bound} = 150 \\times 0.30 = 45 \\text{ tokens/sec}\n\\]\n\\[\n\\text{Upper bound} = 150 \\times 0.50 = 75 \\text{ tokens/sec}\n\\]\n\n**Realistic range: `45–75 tokens/sec`**\n\n---\n💡 **Notes**:  \n- This assumes the 900 GB/s NVLink-C2C link is fully dedicated to streaming active MoE weights per token.  \n- Actual throughput may vary slightly based on sequence length, batch size, expert routing patterns, and whether weights are cached in GPU HBM vs. streamed fresh each step.  \n- For longer sequences, attention compute (FLOPs) may eventually dominate, shifting the bottleneck from memory bandwidth to compute.",
  "final_reasoning": "Here's a thinking process:\n\n1.  **Understand User Input:**\n   - Chip: GB10 (likely refers to NVIDIA Grace Blackwell GB10 or similar, but the key spec given is NVLink-C2C bandwidth = 900 GB/s)\n   - Model: 35B MoE (Mixture of Experts)\n   - Active parameters: 3B\n   - Weight data per token: active_params × 2 bytes (assuming FP16/BF16 precision, which is standard for inference)\n   - Questions:\n     (1) Theoretical maximum tokens per second?\n     (2) Realistic range given vLLM achieves 30-50% of theoretical peak?\n\n2.  **Identify Key Variables & Formulas:**\n   - Bandwidth (B) = 900 GB/s = 900 × 10^9 bytes/s\n   - Active parameters (P) = 3B = 3 × 10^9 parameters\n   - Bytes per parameter (BPP) = 2 bytes (FP16/BF16)\n   - Data per token (D) = P × BPP = 3 × 10^9 × 2 = 6 × 10^9 bytes = 6 GB\n   - Theoretical max tokens/sec (T_max) = Bandwidth / Data per token = B / D\n   - Realistic range = T_max × [0.30, 0.50]\n\n3.  **Calculate Theoretical Maximum:**\n   - D = 6 GB/token\n   - B = 900 GB/s\n   - T_max = 900 / 6 = 150 tokens/second\n\n4.  **Calculate Realistic Range:**\n   - Lower bound: 150 × 0.30 = 45 tokens/second\n   - Upper bound: 150 × 0.50 = 75 tokens/second\n   - Range: 45–75 tokens/second\n\n5.  **Check Assumptions & Context:**\n   - NVLink-C2C bandwidth: 900 GB/s is given. This is the inter-chip bandwidth. For a single GB10 chip, this might be the total available bandwidth for weight loading. I'll assume it's the effective bandwidth for weight streaming.\n   - MoE active parameters: 3B active out of 35B total. Only active weights are loaded per token. Correct.\n   - Precision: 2 bytes implies FP16/BF16. Standard for inference. Correct.\n   - vLLM efficiency: 30-50% is a reasonable assumption for memory-bound inference workloads on this architecture.\n   - Calculation: 900 / 6 = 150. Straightforward.\n   - Realistic: 45-75. Straightforward.\n\n6.  **Formulate Response:**\n   - State assumptions clearly (FP16/BF16 precision, bandwidth fully utilized for weight loading, single-chip scenario).\n   - Show step-by-step calculation.\n   - Provide theoretical max.\n   - Provide realistic range.\n   - Add brief context/notes (e.g., actual performance may vary due to KV cache, attention compute, memory overhead, etc.).\n\n   Structure:\n   - Step 1: Calculate bytes per token\n   - Step 2: Theoretical max tokens/sec\n   - Step 3: Realistic range (30-50%)\n   - Final answer clearly stated\n   - Caveats/notes\n\n   Draft:\n   (1) Theoretical maximum:\n   - Active parameters = 3B\n   - Precision = 2 bytes/param (FP16/BF16)\n   - Data per token = 3B × 2 = 6 GB\n   - Bandwidth = 900 GB/s\n   - Max tokens/sec = 900 / 6 = 150 tokens/sec\n\n   (2) Realistic range (30–50% efficiency):\n   - Lower: 150 × 0.30 = 45 tokens/sec\n   - Upper: 150 × 0.50 = 75 tokens/sec\n   - Range: 45–75 tokens/sec\n\n   Add notes: Assumes bandwidth is fully dedicated to weight streaming, single GB10 chip, FP16/BF16 precision, and memory-bound regime. Actual vLLM performance may vary due to KV cache management, attention compute, memory fragmentation, and system overhead.\n\n7.  **Self-Correction/Verification:**\n   - Check units: GB/s / GB/token = tokens/s. Correct.\n   - 3B × 2 = 6 GB. Correct.\n   - 900 / 6 = 150. Correct.\n   - 30-50% of 150 = 45-75. Correct.\n   - Is NVLink-C2C bandwidth typically the limiting factor for MoE weight loading? Yes, for large MoE models, weight streaming over interconnects is often memory-bound.\n   - Could there be a nuance about \"GB10\"? NVIDIA's Grace Blackwell GB10 has 2 Grace CPUs + 2 Blackwell GPUs, with NVLink-C2C between CPUs and GPUs, and NVLink-S2S between GPUs. The 900 GB/s likely refers to the CPU-GPU interconnect bandwidth. For inference, weights are typically loaded from GPU HBM, but for MoE, expert weights might be stored on CPU or streamed via NVLink-C2C. The problem states \"NVLink-C2C bandwidth of 900 GB/s\" and asks to use it for weight loading, so we'll stick with that.\n   - All good.\n\n   Final output matches draft. I'll present it clearly.✅",
  "final_tool_calls": [],
  "auto_pass": true,
  "auto_notes": "",
  "auto_reasoning_hit": "11/11"
}