Kimi K2.6 Tests 8x A100 Limits

Kimi K2.6 is a strong open-source release, but this thread is a reminder that on big local rigs, "best model" and "fits in VRAM" are separate questions. For this exact workload, I'd lean GLM-5.1 first, DeepSeek-V3 second, and Kimi K2.6 only if you're willing to trade context and cache headroom for model quality.

• –Kimi K2.6 is genuinely frontier-leaning, with 262K official context and strong long-horizon behavior, but the MoE + long-context memory bill is still steep in local serving.
• –GLM-5.1 looks like the cleaner fit for structured teacher-data generation: strong reasoning, 200K context, and official emphasis on long-horizon consistency and structured output.
• –DeepSeek-V3 remains attractive when KV cache is the bottleneck because its MLA architecture is built to make long context cheaper than standard attention stacks.
• –For single-user inference, consistency matters more than throughput, so a slightly smaller model with stable BF16/FP16 cache handling can beat a larger model squeezed in with aggressive quantization.
• –The thread also reinforces that llama.cpp is not the obvious serving stack here; if quality is the priority, the community is pointing toward vLLM or sglang-style serving.