How much VRAM does Meituan LongCat-2.0 1.7T need at Q4_K_M? The agentic-coding MoE

How this is calculated

The 1.7T pool at Q4_K_M is 952 GB of weights. The key-value cache with 8 KV heads at head_dim 128 across 64 layers runs 275 GB at the full 1M context, and overhead lands around 123 GB. The agentic tuning means the model holds up in multi-hour tool-call sessions where cheaper frontier MoEs drift.

Verdict

The 1350 GB resident budget is 952 GB of Q4 weights, 275 GB of FP16 KV cache at 1M, and 123 GB of overhead - 17 H100 80GB or 10 H200 141GB cards resident. The 48B active count doesn't lower the resident total, because any token can route to any expert. The realistic comparison is Kimi K3 2.8T: K3's hybrid KDA+MLA gives it a much smaller long-context cache, so for long agentic sessions at 1M context K3 is cheaper to hold in memory, while LongCat-2.0 has the edge in agentic-coding evals and is roughly a trillion parameters smaller on the weight side.