Apply a dark theme and highly detailed, ultra hyperrealistic illustrations with Unreal Engine 5 quality rendering, Lumen-style dynamic global illumination, Nanite-level geometric detail, and MetaHuman photorealistic characters - near-film fidelity facial features, pore-level skin texture, anatomically correct proportions. Right, let's get straight to what you actually asked. You have ~640GB VRAM and want to run Kimi-K2-Instruct locally. That's a solid setup - 8x A100 80GB cards, I'm guessing. **Best quantization for your hardware** The community has largely coalesced around GGUFs, specifically Unsloth's Dynamic quants. On your 640GB Ampere rig, **UD-Q2_K_XL** at ~381GB hits a practical sweet spot. It leaves you substantial headroom for KV cache and system overhead - enough breathing room to avoid memory pressure during long generations. Unsloth recommends this quant for A100 setups, with community reports showing roughly 9-11 tokens/sec on similar hardware with proper offloading. Quality loss versus full precision appears minimal on coding tasks, though exact degradation percentages vary by benchmark. The more aggressive **UD-TQ1_0** at 281GB works too, but you'll feel the quality drop on complex reasoning. Reserve that for scenarios where you absolutely must maximize headroom. **Why minimal AWQ discussion?** Simple - the open-source tooling focus went to GGUF first. llama.cpp and vLLM integrated GGUF support earlier and more comprehensively for MoE models. You're not necessarily missing quality, just community momentum. **Architecture limitations you should know** Kimi K2's MoE design activates 384 experts with 32B parameters per token. That's massive, and creates real constraints. First, **expert routing overhead** can punish slower interconnects. Ampere's NVLink works, but many users offload MoE layers to CPU with `-ot ".ffn_.*_exps.=CPU"` - this frees GPU memory for dense attention layers where your cards still perform well. Second, **attention head reduction to 64** (versus DeepSeek V3's 128) saves compute but can slightly degrade fine-grained nuance on tasks requiring extreme contextual sensitivity. Most users won't notice, but it's there. Third, **text-only modality**. Kimi K2 doesn't handle images or audio - it's designed for language and tool use. Fourth, **Heavy-Mode evaluation context**. Those impressive benchmark scores often use eight parallel trajectories aggregated together. Single-pass performance will be lower - how much depends on the specific task, so manage expectations accordingly. Fifth, **tool use verbosity**. The official documentation notes the model may generate excessive tokens when tool definitions are unclear, occasionally leading to truncated outputs. Keep tool schemas tight and explicit. **Hardware compatibility reality** Your 640GB Ampere setup absolutely runs Kimi K2. The "Hopper-only" perception stems from NVIDIA's NIM container focus, but community implementations work fine on Ampere. You'll see slower inference than H100s - typical estimates suggest 30-40% speed differences in some benchmarks, though this varies wildly based on quantization and offloading strategy. **The conclusion** Grab UD-Q2_K_XL, use llama.cpp with MoE offloading, and you'll have a world-class model running on hardware you already own. The setup isn't trivial, but it's absolutely doable. Don't overthink the quantization choice - the 2-bit dynamic quant gives you the best balance of quality and speed for your hardware profile.