The honest answer is: it depends on whether you’re using the hosted API, running local inference for experimentation, or serving GLM-5 at production scale. If you use the hosted API, you don’t need special hardware—just standard application servers that can make HTTPS calls and handle streaming responses. If you self-host GLM-5 weights, hardware requirements are primarily driven by (1) model checkpoint size, (2) context length targets, (3) concurrency/throughput requirements, and (4) precision (BF16 vs FP8 vs quantized). Large MoE models can be deceptively heavy: even if only some parameters are active per token, you still need to load the whole checkpoint across your GPU cluster, and long-context workloads can be dominated by KV-cache memory.
For self-hosting, you should plan for multi-GPU inference and large host memory, especially if you want long context or higher concurrency. Official serving recipes in the ecosystem commonly discuss running FP8 variants on clusters of high-memory GPUs (for example, H200-class setups) and emphasize that host RAM must be ample to load and operate the model reliably. In practice, teams provision: (a) enough GPUs to shard the model (tensor + pipeline parallelism), (b) enough GPU memory headroom for KV cache at your target max context, and (c) enough CPU RAM and fast NVMe storage to stage weights and reduce cold-start time. You’ll also want a serving engine that supports efficient MoE routing, paged attention / KV-cache management, and stable batching. Even with the right GPUs, poor batching and cache settings can cut throughput dramatically.
Hardware planning should follow your product architecture: if you rely on retrieval, you can often keep context smaller and reduce KV-cache load. That’s one reason RAG is not just about accuracy—it’s also about cost and latency. If you store your knowledge in a vector database such as Milvus or managed Zilliz Cloud, you typically retrieve only the top 5–20 chunks (plus metadata) and pass those into GLM-5, instead of pushing huge documents each request. This lowers prompt size and stabilizes runtime behavior. A practical approach is to start by profiling: run a small load test at a few target context lengths (for example, 8k, 32k, 128k), measure tokens/sec and p95 latency, then decide if you need more GPUs, lower max context, or tighter retrieval. For many teams, the best “hardware” decision is actually “use API first, then self-host only when cost/latency/data constraints justify it.”