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Home / Compute Matrix / AI Large Models, VRAM & Deep Learning Compute / InternLM2.5 20B 1M Context (FP8 Scaled Native Hopper) on NVIDIA B200 192GB Blackwell VRAM & Throughput Calculator
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← AI Large Models, VRAM & Deep Learning Compute 💻 矩阵工具 #234
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InternLM2.5 20B 1M Context (FP8 Scaled Native Hopper) on NVIDIA B200 192GB Blackwell VRAM & Throughput Calculator

Exact VRAM memory allocation, dynamic KV-cache requirements, and tensor parallelism slicing for InternLM2.5 20B 1M Context quantized in FP8 Scaled Native Hopper deployed on NVIDIA B200 192GB Blackwell.

🎛️ Architecture & System Parameters
Reactive Compute Engine

Engineering Execution Protocol

  1. Set model parameter size (20B) and verify FP8 Scaled Native Hopper quantization precision.
  2. Define production context length in tokens and peak concurrent query concurrency.
  3. Evaluate required memory capacity and calculate multi-GPU tensor parallelism scaling across NVIDIA B200 192GB Blackwell nodes.

Technical Authority & System Specifications

Q: How much VRAM does InternLM2.5 20B 1M Context require in FP8 Scaled Native Hopper?

Uncompressed weights alone consume 20.0 GB. In addition, the KV cache scales with context tokens and concurrency batch size, plus ~1.8 GB CUDA driver overhead.

Q: Can a single NVIDIA B200 192GB Blackwell run this model without Out-Of-Memory (OOM)?

If total weights + KV cache exceeds the 192 GB boundary, Tensor Parallelism (TP) or vLLM PagedAttention multi-GPU sharding across NVLink is required.

Q: How does 4-bit quantization affect inference quality and speed?

Modern AWQ and GPTQ retain >98% perplexity compared to FP16 while halving memory footprint and doubling memory-bandwidth-bound token generation speed.

Specialized In-Category Architectures

💻 DeepSeek-V3 671B MoE (FP16 Uncompressed Native) on NVIDIA H100 80GB SXM5 VRAM & Throughput Calculator
Exact VRAM memory allocation, dynamic KV-cache requirements, and tensor parallelism slicing for DeepSeek-V3 671B MoE quantized in FP16 Uncompressed Native deployed on NVIDIA H100 80GB SXM5.
💻 DeepSeek-V3 671B MoE (BF16 Bfloat16 Mixed Precision) on NVIDIA H200 141GB HBM3e VRAM & Throughput Calculator
Exact VRAM memory allocation, dynamic KV-cache requirements, and tensor parallelism slicing for DeepSeek-V3 671B MoE quantized in BF16 Bfloat16 Mixed Precision deployed on NVIDIA H200 141GB HBM3e.
💻 DeepSeek-V3 671B MoE (FP8 Scaled Native Hopper) on NVIDIA B200 192GB Blackwell VRAM & Throughput Calculator
Exact VRAM memory allocation, dynamic KV-cache requirements, and tensor parallelism slicing for DeepSeek-V3 671B MoE quantized in FP8 Scaled Native Hopper deployed on NVIDIA B200 192GB Blackwell.
💻 DeepSeek-V3 671B MoE (INT8 SmoothQuant Precision) on NVIDIA A100 80GB PCIe VRAM & Throughput Calculator
Exact VRAM memory allocation, dynamic KV-cache requirements, and tensor parallelism slicing for DeepSeek-V3 671B MoE quantized in INT8 SmoothQuant Precision deployed on NVIDIA A100 80GB PCIe.
💻 DeepSeek-V3 671B MoE (AWQ 4-Bit Activation-Aware) on NVIDIA RTX 4090 24GB GDDR6X VRAM & Throughput Calculator
Exact VRAM memory allocation, dynamic KV-cache requirements, and tensor parallelism slicing for DeepSeek-V3 671B MoE quantized in AWQ 4-Bit Activation-Aware deployed on NVIDIA RTX 4090 24GB GDDR6X.
💻 DeepSeek-V3 671B MoE (GPTQ 4-Bit Second-Order) on NVIDIA L40S 48GB Ada Lovelace VRAM & Throughput Calculator
Exact VRAM memory allocation, dynamic KV-cache requirements, and tensor parallelism slicing for DeepSeek-V3 671B MoE quantized in GPTQ 4-Bit Second-Order deployed on NVIDIA L40S 48GB Ada Lovelace.

Cross-Disciplinary Compute Workflows

⚡ NVIDIA GeForce RTX 4090 + Intel Core i9-14900K (1080p Full HD) Bottleneck & PSU Sizing Calculator
Determine CPU-GPU performance bottleneck ratio at 1080p Full HD, calculate 1-10ms microsecond transient power spikes, and determine safe ATX 3.0 power supply capacity.
⚡ NVIDIA GeForce RTX 4090 + Intel Core i9-14900K (1440p 2K QHD) Bottleneck & PSU Sizing Calculator
Determine CPU-GPU performance bottleneck ratio at 1440p 2K QHD, calculate 1-10ms microsecond transient power spikes, and determine safe ATX 3.0 power supply capacity.
⚡ NVIDIA GeForce RTX 4090 + Intel Core i9-14900K (4K 2160p Ultra HD) Bottleneck & PSU Sizing Calculator
Determine CPU-GPU performance bottleneck ratio at 4K 2160p Ultra HD, calculate 1-10ms microsecond transient power spikes, and determine safe ATX 3.0 power supply capacity.
⚡ NVIDIA GeForce RTX 4090 + AMD Ryzen 7 7800X3D (1080p Full HD) Bottleneck & PSU Sizing Calculator
Determine CPU-GPU performance bottleneck ratio at 1080p Full HD, calculate 1-10ms microsecond transient power spikes, and determine safe ATX 3.0 power supply capacity.
Primary System Output
Calculating...
HARDWARE ARCHITECTURE ACTIVE
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Tool #234 of 101,000 Compute Matrix
Calculated Output
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