Where to Run GLM-5 Inference in the Cloud: GPU Requirements, Deployment Options, and Scaling Considerations

GLM-5 and its successor GLM-5.1 represent a genuine shift in the open-weight frontier. Released under the MIT license by Z.ai (formerly Zhipu AI), GLM-5 is a 744 billion parameter Mixture-of-Experts model that reached the top of LMArena in both Text Arena and Code Arena at launch. GLM-5.1, released April 7, 2026, extended that position to an Elo score of 1530 on Code Arena, making it the highest-ranked open-source coding model as of May 2026.

Running it at production scale requires the same class of hardware as DeepSeek-V3: multi-GPU H100 or H200 clusters with NVLink interconnects, FP8 precision for practical VRAM fit, and serving frameworks that handle MoE expert routing efficiently.

• GLM-5.1 is a 754B total / 40B active parameter MoE model. The full weight matrix must reside in VRAM regardless of the active parameter count per token. At FP8, that is approximately 800 GB. The minimum practical production configuration is 8x H100 SXM (640 GB) at INT4 with quality tradeoffs, or 8x H200 SXM (1,128 GB) at FP8 for full-precision serving.
• The official Z.ai API is the fastest path to GLM-5 inference but carries the same data sovereignty constraints as DeepSeek. All traffic routes through infrastructure subject to Chinese data law. For workloads involving personal data under GDPR, healthcare data under HIPAA, or any enterprise data governance requirement, the official API is not a viable production option.
• 10 third-party providers now host GLM-5.1 as a managed API, including Fireworks, DeepInfra, Together AI, Nebius, and CoreWeave. DeepInfra leads on price at $0.74 blended per million tokens. Fireworks and Wafer lead on throughput at 151 to 162 tokens per second.
• GMI Cloud provides the dedicated H100 and H200 cluster infrastructure for teams that need to self-host GLM-5 or GLM-5.1 with full data governance control. H200 SXM at $2.60/hr bare metal with NVLink interconnects, pre-installed vLLM and SGLang, and RDMA-ready networking covers the hardware requirements for both the 8x H200 FP8 configuration and the 10x H100 alternative.
• SGLang's expert parallelism flag (--enable-moe-ep) is essential for GLM-5 throughput. Without it, the runtime uses tensor parallelism only, copying all expert weights to every GPU and reducing effective memory efficiency. With it, expert routing traffic across the interconnect is managed efficiently, and SGLang's RadixAttention delivers additional throughput gains for the long-context agentic workloads GLM-5 is designed for.
• GLM-5 was trained entirely on Huawei Ascend 910B chips with no NVIDIA hardware involvement. This is the first frontier-class open-weight model to achieve that distinction and has implications for GPU export restrictions, supply chain planning, and the long-term hardware landscape for large-model training.

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Understanding the GLM-5 Family

Z.ai has released several model variants under the GLM-5 umbrella, each serving different use cases.

GLM-5 (released February 11, 2026): The original flagship. 744 billion total parameters, 40 billion active per token, 203K token context window. MIT license. Trained on 28.5 trillion tokens with emphasis on code, technical documentation, and reasoning-dense data. Scored 50.4 percent on Humanity's Last Exam at launch, ranking above Claude Opus 4.5 on that benchmark. Number one open-source model on LMArena Text Arena and Code Arena at launch.

GLM-5.1 (released April 7, 2026): The direct successor. 754 billion total parameters, 40 billion active per token, 202,752 token context window, up to 131K output tokens in a single response. Elo 1530 on Code Arena as of May 2026, the highest-ranked open-source model. Designed specifically for long-horizon agentic tasks: sustained iterative workflows, hundreds of conversation rounds, thousands of tool calls. Weights available on Hugging Face under the MIT license.

GLM-5 Turbo (released March 15, 2026): A proprietary closed-source companion model optimized for fast inference and supervised agent runs. Not available for self-hosting. Official API pricing: $1.20 per million input tokens, $4.00 per million output tokens. Designed for latency-sensitive applications where GLM-5's long-horizon strength is not required.

GLM-4.5 Air: A lighter variant available free on OpenRouter for teams evaluating the GLM model family without committing to the 754B flagship infrastructure.

The relevant production decision is usually between GLM-5.1 on self-hosted multi-GPU infrastructure, GLM-5.1 through a managed US-hosted API (Fireworks, DeepInfra, Together AI), or the official Z.ai API for workloads without data sovereignty constraints.

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Hardware Requirements: What GLM-5 and GLM-5.1 Actually Need

The most important hardware planning fact for GLM-5/5.1 is identical to the Qwen3-235B situation: the MoE architecture activates only 40 billion parameters per token, but the full 754 billion parameter weight matrix must reside in VRAM before inference can begin. Planning hardware around the active parameter figure leads to severe under-provisioning.

FP8 (recommended for production): At FP8 precision, GLM-5.1 weights require approximately 800 GB of VRAM for storage alone, with additional memory needed for KV cache, activations, and framework overhead. The recommended production configuration is 8x H200 SXM (1,128 GB total), which provides the weight footprint plus meaningful KV cache headroom for the 203K context window and concurrent requests.

An alternative is 10x H100 SXM (800 GB total), which is tight at FP8 with limited KV cache room. The 8x H200 configuration is preferred for production workloads because of the additional headroom.

INT4 / AWQ (budget alternative): INT4 quantization reduces the weight footprint to approximately 200 GB, fitting within 5x A100 80GB (400 GB total) at the cost of 1 to 3 percent quality regression on coding benchmarks. Z.ai has not published an official AWQ variant as of April 2026; self-quantization from the base weights using AutoAWQ is required. This configuration is viable for research and development but not the recommended path for production serving where coding accuracy is the primary requirement.

The Serving Stack: vLLM and SGLang for GLM-5

Both vLLM and SGLang officially support GLM-5 and GLM-5.1. The Z.ai team recommends running benchmarks with 8x H200 as the reference configuration. vLLM exposes an OpenAI-compatible API at standard ports, making GLM-5.1 a drop-in replacement for any OpenAI SDK client with a base URL change.

Critical configuration flags for both frameworks:

python -m vllm.entrypoints.openai.api_server   --model zai-org/GLM-5.1-FP8   --tensor-parallel-size 8   --max-model-len 200000   --enable-expert-parallel   --trust-remote-code   --port 8000

python -m sglang.launch_server   --model-path ./glm-5.1   --tp 8   --enable-moe-ep   --port 8000

The --enable-expert-parallel flag in vLLM and --enable-moe-ep in SGLang are not optional for production throughput. Without expert parallelism, the runtime falls back to tensor parallelism only, which copies all expert weights to every GPU. This wastes VRAM and removes the memory efficiency benefit of the MoE design. With expert parallelism enabled, routing traffic distributes expert computations across GPUs via the NVLink interconnect, which is why NVLink bandwidth matters substantially for this model.

SGLang versus vLLM for GLM-5 workloads:

SGLang delivers 29 percent higher throughput than vLLM on standard H100 benchmarks (16,200 versus 12,500 tokens per second on comparable workloads) and up to 6.4x higher throughput on prefix-heavy workloads through RadixAttention KV cache reuse. For GLM-5's primary use case, long-horizon agentic tasks with shared system prompts and multi-turn context, the RadixAttention advantage is especially significant. Each tool call cycle in a multi-step agent run typically shares a large prefix with previous turns; SGLang reuses that cached KV computation rather than recomputing it.

GLM-5.1 also supports Multi-Token Prediction, which SGLang integrates natively and which delivers decode speedup at batch size 1. The actual throughput gain in production is higher than benchmark numbers suggest because MTP acceptance rates in real workloads exceed those in standardized benchmarks.

For teams new to GLM-5 deployment, vLLM is the safer starting choice due to its larger community, more detailed error messages, and broader documentation. For teams optimizing for throughput on agentic workloads, SGLang with MoE expert parallelism and RadixAttention is the higher-performance option.

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Where to Run GLM-5 Inference: Deployment Options

Option 1: Official Z.ai API

The official Z.ai API for GLM-5 starts at $0.60 per million input tokens and $1.92 per million output tokens. GLM-5.1 is priced at $0.98 per million input tokens and $3.08 per million output tokens. Cache discounts reduce repeated input to $0.26 per million tokens for GLM-5.

At these rates, the official API is cost-competitive for low-to-medium volume workloads. The operational simplicity is real: no infrastructure to manage, instant access to the most capable variant, and a 203K context window immediately available.

The production constraint is identical to the situation with the official DeepSeek API: all traffic routes through infrastructure subject to Chinese data law. Mandatory government access provisions under China's Cybersecurity Law and Data Security Law apply. For workloads involving personal data under GDPR, healthcare data under HIPAA, financial data, or government and defense applications, the official API is not viable. The pattern for compliance-conscious teams is the same as with DeepSeek: use the open weights on US or EU-hosted infrastructure, which preserves all capability advantages without the jurisdictional exposure.

Option 2: Third-Party US-Hosted Managed APIs

Ten providers now host GLM-5.1 as a managed API on US and European infrastructure. This is the clearest path for teams that want managed API simplicity without the official API's data residency risk.

DeepInfra offers the lowest blended pricing at $0.74 per million tokens for GLM-5.1, with time-to-first-token of 0.82 seconds and FP8 precision. DeepInfra also hosts GLM-5 at $0.15 per million input tokens on their standard tier.

Fireworks AI delivers the highest verified throughput among US-hosted providers at 151.3 tokens per second on GLM-5.1, with time-to-first-token of 26.18 seconds (reflecting the model's large KV prefill on long contexts). SOC 2 Type II and HIPAA certification makes Fireworks the compliance-ready option for regulated industry GLM-5 workloads.

Wafer leads on combined throughput-to-latency balance at 161.8 tokens per second and $0.86 blended per million tokens, making it competitive with DeepInfra on price while delivering the fastest raw throughput in the benchmark set.

Together AI and CoreWeave round out the enterprise-focused options, with Together AI offering fine-tuning support on GLM variants and CoreWeave providing dedicated capacity with SLA guarantees for teams that need guaranteed throughput on GLM-5.1 at production scale.

Nebius hosts GLM-5.1 on European infrastructure, providing GDPR-compliant inference for EU workloads where both data residency and model capability are requirements.

Option 3: Self-Hosted on Dedicated GPU Infrastructure with GMI Cloud

Self-hosting GLM-5.1 on dedicated GPU infrastructure is the right path for three categories of teams: those with data sovereignty requirements that no managed API can satisfy, those running volume that exceeds the per-token crossover threshold, and those that need fine-grained serving stack control for agentic workload optimization.

GMI Cloud provides the hardware layer for self-hosted GLM-5.1 deployment on NVIDIA H100 and H200 infrastructure with the interconnect and software stack the model requires.

H200 SXM bare metal at $2.60/hr with NVLink 4.0 (900 GB/s bidirectional per GPU) and 3.2 Tbps InfiniBand for inter-node communication. This is the reference hardware configuration for 8x H200 FP8 GLM-5.1 deployment. GMI Cloud nodes ship pre-configured with vLLM, SGLang, TensorRT-LLM, and Triton Inference Server on CUDA 12.x, eliminating the environment setup phase. Root access and custom software stacks are supported.

An 8x H200 cluster at $20.80/hr total running GLM-5.1 FP8 at production throughput with SGLang and expert parallelism delivers cost-per-token economics that beat managed API rates above approximately 40 to 60 million output tokens per day. Below that volume, the managed API providers are more economical because you are not paying for idle GPU time.

For teams in the 10 to 40 million token per day range where the economics are marginal, GMI Cloud's per-minute billing and on-demand provisioning mean you can run dedicated clusters for peak periods and scale down during off-peak hours without minimum commitment penalties.

Multi-region deployment across GMI Cloud's US, Taiwan, Singapore, Thailand, Malaysia, and Japan infrastructure also directly addresses data residency requirements for the APAC enterprise workloads where GLM-5's Chinese language training depth is particularly valuable.

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Scaling Considerations for Long-Horizon Agentic Workloads

GLM-5 and GLM-5.1 are explicitly designed for tasks that differ from standard chat inference in important ways. Long-horizon agentic workloads have distinct scaling characteristics that affect infrastructure design.

Output token volume is much higher. Standard chatbot completion averages 100 to 500 output tokens per request. A GLM-5.1 agentic coding task completing a multi-file software project might generate 10,000 to 100,000 output tokens across many sequential calls. The 131K output token limit per response is a capability that only becomes infrastructure-relevant when you size GPU-hours and KV cache allocation for it.

Sequential call patterns change throughput requirements. Agentic workflows make many tool calls in sequence, with each call depending on the previous result. Unlike batched inference where many requests process in parallel, sequential agent workflows bottleneck on single-request latency rather than aggregate throughput. p99 time-to-first-token matters more than tokens-per-second for the agent orchestration layer.

Shared prefix length grows with context. Each tool call response is appended to the conversation context before the next call. After 50 tool calls in a long agentic task, the shared prefix prefix can be 50,000 to 100,000 tokens long. SGLang's RadixAttention eliminates KV recomputation for that shared prefix on every subsequent call, which at long context lengths is the single largest throughput optimization available for GLM-5 workloads.

Chunked prefill prevents request starvation. Long prompts from accumulated agent context can block the GPU during prefill, starving shorter requests waiting in queue. Setting --enable-chunked-prefill in vLLM interleaves prefill and decode operations, preventing this starvation pattern in multi-user GLM-5 deployments.

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The Data Sovereignty Question for GLM-5

GLM-5 and GLM-5.1 are developed by Z.ai, a Chinese company incorporated and operating in China. The official API routes traffic through Chinese infrastructure. The same CLOUD Act and Chinese data law analysis that applies to DeepSeek's official API applies here.

The open weights under the MIT license resolve this directly. Deploying GLM-5.1 open weights on US or EU infrastructure removes the Chinese jurisdiction exposure entirely. Legal analysis of the regulatory landscape is consistent on this point: hosting open-weight Chinese models on non-Chinese infrastructure operated by non-Chinese companies eliminates the data sovereignty concern for enterprise workloads.

For EU organizations, Nebius provides GLM-5.1 inference on European infrastructure within GDPR jurisdiction. For APAC organizations, GMI Cloud's in-country infrastructure across Singapore, Thailand, Malaysia, and Japan serves the regional data residency requirements directly. For US organizations, any of the US-hosted managed providers (DeepInfra, Fireworks, Wafer, Together AI, CoreWeave) or self-hosted deployment on GMI Cloud's US infrastructure addresses the requirement.

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Conclusion

GLM-5.1 is the highest-ranked open-source coding model available in 2026 and the clearest open-weight option for production agentic engineering workflows. The infrastructure requirements are substantial but well-understood: 8x H200 SXM at FP8 with expert parallelism enabled in vLLM or SGLang.

For teams that need managed API access without data sovereignty risk, Fireworks AI and DeepInfra are the benchmark-verified US-hosted options. For teams that need infrastructure control, data residency compliance, or the economics of self-hosting at scale, GMI Cloud's bare metal H200 clusters with NVLink interconnects and pre-installed inference stack provide the deployment path.

The combination of GLM-5.1's long-horizon agentic capability and GMI Cloud's inference-first infrastructure across US and APAC regions makes it particularly well-suited for enterprise engineering teams building production agentic systems where open weights, data governance, and frontier coding performance all matter simultaneously.

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FAQs

How much VRAM does GLM-5.1 require for production inference? Despite activating only 40 billion parameters per token, GLM-5.1 requires the full 754 billion parameter weight matrix loaded into GPU memory before inference begins. At FP8 precision, the recommended production configuration, this requires approximately 800 GB of VRAM for weights alone. The recommended production setup is 8x H200 SXM (1,128 GB total), which provides weight storage plus KV cache headroom for the 203K context window and concurrent requests. A 10x H100 SXM configuration (800 GB total) is viable but tight at FP8. Budget-constrained teams can use INT4 quantization on 5x A100 80GB (400 GB), accepting 1 to 3 percent quality regression on coding benchmarks. Planning infrastructure around the 40B active parameter figure leads to severe under-provisioning.

Why is the official Z.ai API not suitable for enterprise production workloads? The official GLM-5 and GLM-5.1 APIs route all inference traffic through Z.ai's infrastructure in China, subject to Chinese data law including the Cybersecurity Law and Data Security Law, which mandate government access to data upon request. This creates the same compliance conflict as the official DeepSeek API for workloads involving EU personal data under GDPR, US healthcare data under HIPAA, financial data under SOX, or any government and defense applications. The open weights under the MIT license resolve this: deploying GLM-5.1 on US or EU-hosted infrastructure via third-party providers like Fireworks AI, DeepInfra, or Nebius, or through self-hosted deployment on GMI Cloud, eliminates the jurisdictional exposure while preserving the model's full capability.

What is the difference between GLM-5, GLM-5.1, and GLM-5 Turbo? GLM-5 (February 2026) is the original flagship: 744B total parameters, 40B active, 203K context, MIT license. GLM-5.1 (April 2026) is the direct successor with 754B parameters, improved long-horizon agentic performance, Elo 1530 on Code Arena, and support for up to 131K output tokens per response. Both carry the MIT license with open weights for self-hosting. GLM-5 Turbo is a separate proprietary closed-source model optimized for fast inference and supervised agent runs, not available for self-hosting, priced at $1.20 per million input tokens and $4.00 per million output tokens through the official Z.ai API. For production agentic coding workloads where open weights and data governance matter, GLM-5.1 is the relevant model.

Why does expert parallelism matter so much for GLM-5 serving performance? GLM-5.1 uses a Mixture-of-Experts architecture with 256 total experts, routing each token to 8 experts per forward pass. Without expert parallelism enabled, vLLM and SGLang default to tensor parallelism, which copies the full expert weight matrix to every GPU in the cluster. This wastes VRAM and eliminates the memory efficiency benefit that MoE architectures are designed to provide. With expert parallelism enabled via --enable-expert-parallel in vLLM or --enable-moe-ep in SGLang, expert computations distribute across GPUs via the NVLink interconnect, improving both throughput and VRAM efficiency. For agentic workloads with long context, combining expert parallelism with SGLang's RadixAttention KV cache reuse eliminates the two largest sources of throughput loss in production GLM-5 deployments.

At what token volume does self-hosting GLM-5.1 on GMI Cloud beat managed API pricing? For managed US-hosted APIs, DeepInfra's blended rate of $0.74 per million tokens is the current low-water mark. An 8x H200 SXM cluster on GMI Cloud at $20.80/hr total running GLM-5.1 FP8 with SGLang at high utilization achieves effective costs of roughly $0.40 to $0.70 per million output tokens, depending on throughput and batch efficiency. The crossover point where self-hosting beats the cheapest managed API rate sits at approximately 40 to 60 million output tokens per day of sustained throughput, which is roughly 460 to 700 tokens per second. Below that volume, managed API providers are more economical. Above it, dedicated infrastructure with per-minute billing and no idle minimums on GMI Cloud delivers better cost-per-token with full serving stack control and no data sovereignty constraints.