How much VRAM does Meituan LongCat-2.0 1.7T need at Q4_K_M? The agentic-coding MoE
LongCat-2.0 at Q4 needs ~1350 GB resident or ~332 GB active-only for 1M context - comparable to DeepSeek V4 Pro's footprint but tuned for agentic coding. Self-host when you need an open agentic loop with no rate limits; otherwise Meituan's API pricing does the job.
Meituan LongCat-2.0 1.7T at Q4_K_M with native 1M context needs about 1350 GB of VRAM with all experts resident, or 332 GB with active-expert offload. Meituan's July 2026 open release activates 48B parameters per token and is tuned specifically for agentic coding loops, making it the most direct open challenger to Kimi K3 in that niche.
By TechCompare · Updated
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Estimated VRAM required
1350 GB
1700B params at Q4_K_M, 1,048,576 token context, batch 1, inference.
Estimate accuracy: Weights within ~2%. KV cache within ~5% for standard GQA models, ~10% for MLA (DeepSeek). Real VRAM may vary with framework (vLLM vs llama.cpp vs Transformers), Flash Attention, and driver overhead.
Hardware that fits
No single GPU in our catalog has enough memory. Multi-GPU or CPU offload required.
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. Active-only loading drops resident weights to 26.9 GB while the cache stays, totaling ~332 GB, which fits four H200 141GB cards. 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. Active-only cuts weights to 26.9 GB and the total to ~332 GB on five H200s. 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.
More LongCat scenarios
Related guides
Frequently asked questions
What is LongCat-2.0 tuned for?
LongCat-2.0 or Kimi K3 for self-hosted agents?
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