What hardware is recommended to self-host GLM-5?

To self-host GLM-5, the recommended hardware depends on which checkpoint you run (BF16 vs FP8), your target throughput, and the maximum context length you need. If you want a realistic baseline: GLM-5 is a large model, and production hosting generally means multiple high-memory GPUs with tensor parallelism. The vLLM recipe documentation for GLM-5 explicitly provides a reference setup for serving the FP8 model on 8×H200 (or H20) GPUs (141GB × 8), which is a strong signal of the “comfortable” production class for full-capability serving with high concurrency and long context. See the primary serving guidance here: vLLM GLM-5 recipe. The official model page also notes that vLLM, SGLang, and xLLM support local deployment: GLM-5 on Hugging Face.

A practical hardware planning framework is to decide what you’re optimizing for:

1) Quick local experiments (developer workstation / small server)

• You may be able to run a small test (short context, low concurrency) if you have one or more modern GPUs with enough VRAM, but expect compromises: reduced max_model_len, smaller batch sizes, slower throughput.

• Use an inference engine like vLLM and start with conservative limits (--max-model-len low, --max-num-batched-tokens low).

• Expect that BF16 will require more VRAM than FP8; FP8 can reduce memory if your GPU and kernels support it.

2) Production serving (multi-user, sustained traffic)

• Plan for multi-GPU tensor parallelism and high VRAM headroom.

• FP8 is a common path for throughput/memory efficiency; vLLM documents FP8 hardware support and the performance/memory tradeoffs: vLLM FP8 quantization.

• Favor server-grade GPUs with stable cooling/power and fast interconnect (NVLink-class) for high tensor-parallel efficiency.

3) Long-context applications (big prompts, lots of retrieved context)

• KV cache grows with context length and concurrent sequences. Even if weights fit, long context can still OOM at runtime.

• Size hardware for worst-case concurrency and context, not just “single request fits.”

Also account for the rest of the stack: CPU cores for tokenization and orchestration, RAM for caching and request queues, and fast NVMe for model loading.

In a Milvus-style RAG application, you can often lower the model-side hardware pressure by keeping prompts smaller. Instead of stuffing huge documents into GLM-5, store and retrieve relevant context from Milvus or Zilliz Cloud, then feed only top-k chunks. That reduces average context length and helps you serve more concurrent users on the same GPU budget. A simple “capacity form” you can use internally is:

• Target QPS: ___

• Max context tokens per request: ___

• Max output tokens: ___

• Concurrency target: ___

• SLO (p95 latency): ___

Then test with vLLM load parameters (batch tokens, max seqs, GPU memory utilization) and measure p95 latency and OOM frequency. Hardware selection becomes much less guessy once you measure with your real prompt sizes and retrieval strategy.