If you searched for GLM-5, the first thing worth knowing is that the family has moved on since the original release. GLM-5 arrived in February 2026, GLM-5.1 followed in April, and GLM-5.2 landed in mid-June 2026 as the current open-weights flagship. Unless you have a specific reason to pin an older version, GLM-5.2 is the one to deploy.
This guide covers what the GLM-5 family actually is, which version to run, the hardware a 744B-parameter Mixture-of-Experts model genuinely needs, and how to get it running on Runpod.
What is GLM-5?
GLM-5 is Zhipu AI's frontier open-weights model line, built for agentic coding and long-horizon software engineering rather than general conversation. The defining trait across the family is that the weights ship under an MIT license, so commercial use and modification are permitted.
The current lineup:
- GLM-5 (February 2026): the original release, roughly 744B total parameters with about 40B active per token
- GLM-5.1 (April 2026): an incremental improvement on the same architecture
- GLM-5.2 (June 2026): the current flagship, 744B total and around 40B active, with a usable 1M-token context window
GLM-5.2 is where the family got genuinely competitive. It scored 62.1 on SWE-bench Pro, ahead of GPT-5.5 at 58.6, and it holds the highest position of any open-weights model on the Artificial Analysis Intelligence Index at the time of writing. For teams that want frontier-adjacent coding performance on weights they control, that combination is difficult to find elsewhere.
Why run GLM-5 on Runpod?
A 744B-parameter model is not a single-GPU workload, and that is the practical constraint shaping every decision here.
You need multi-GPU capacity you almost certainly do not own. Even quantized, GLM-5.2 spans several high-VRAM cards. Renting that configuration for an evaluation is a fundamentally different proposition from buying it.
Long context is memory-hungry. A 1M-token window means KV cache dominates your memory budget at long prompts. Having headroom to experiment matters more than peak throughput.
You can size before you commit. Start on a smaller configuration with a shorter context, confirm the model does what you need, then scale up. On Runpod that is a redeploy, billed by the second.
GPU requirements for GLM-5.2
Sparse activation helps with speed, not with storage. All 744B parameters must be resident even though only about 40B fire per token.
- 4-bit quantized: roughly 400 GB of VRAM. Practical on 4x B300 (288 GB each) or a comparable multi-GPU configuration.
- 8-bit quantized: roughly 800 GB. Multi-node territory.
- bf16 full precision: beyond 1.4 TB. Cluster-scale only.
On Runpod, the realistic paths are Clusters for multi-node deployment, or a multi-GPU pod using B300 at $6.94/hr or B200 at $5.98/hr per GPU on Community Cloud. Add a network volume sized for the weights, because re-downloading several hundred gigabytes on each deploy wastes both time and money.
If that footprint is larger than your project justifies, GLM-4.7-Flash gives you the same lineage on a single 24 GB card. It is a materially smaller model, but for many coding tasks the gap is smaller than the hardware difference suggests.
Step 1: Create a Runpod account
Go to runpod.io and sign up with email, GitHub, or Google. Add credits or a payment method, and be aware that a multi-GPU deployment accrues faster than a single card.
Step 2: Provision the hardware
- Create a network volume first, sized for your chosen quantization plus headroom. Mount it at
/workspace. - Go to the Pods section and select a multi-GPU B200 or B300 configuration, or open Clusters if you need multi-node.
- Attach the network volume you just created.
- Choose a vLLM or PyTorch template and deploy.
The pod itself comes up in under 30 seconds. Downloading weights at this scale takes considerably longer, so budget accordingly on the first run.
Step 3: Serve the model
vLLM handles tensor parallelism across the GPUs in your pod. Set tensor parallel size to match your GPU count, and set max model length to the context you actually need rather than the full 1M. KV cache at maximum context will consume memory you would rather spend on batch size.
Once the server is up, send a test request against the OpenAI-compatible endpoint vLLM exposes, then increase context and concurrency gradually so you can see where memory pressure begins.
Pro tips
Start at 32k context, not 1M. Confirm the deployment works before you pay for KV cache you are not using. Raising context later is a config change.
Evaluate on your own repository. Benchmark scores are a starting signal, not a decision. GLM-5.2's strength is long-horizon agentic coding, which is exactly the kind of work that varies most by codebase.
Keep weights on a network volume. At this size, re-downloading is the single biggest avoidable cost in your workflow.
Stop your pod when you are done. Runpod bills by the second, and a multi-GPU configuration left running overnight is an expensive mistake.
Wrapping up
GLM-5.2 is one of the strongest open-weights models available for agentic coding, and the MIT license means you can build on it without negotiating terms. The tradeoff is hardware: this is a multi-GPU deployment, and there is no way around that. Runpod is where you can find out whether it earns the footprint before committing to it.
Ready to try it? Deploy a multi-GPU pod, or start with GLM-4.7-Flash if you want the same family on a single card.
FAQ
Which GLM-5 version should I use?
GLM-5.2, released in June 2026, is the current open-weights flagship and the strongest of the family. GLM-5 and GLM-5.1 remain available, but there is rarely a reason to pin them for new work.
What GPU do I need to run GLM-5.2?
It is a multi-GPU model. Quantized to 4-bit it needs roughly 400 GB of VRAM, which means several B200 or B300 cards. Full precision is cluster-scale. If that is more than your project needs, GLM-4.7-Flash runs on a single 24 GB card.
Is GLM-5 free for commercial use?
Yes. The GLM-5 family ships under an MIT license, which permits commercial use, modification, and redistribution.
How does GLM-5.2 compare to closed models?
On published benchmarks it scores 62.1 on SWE-bench Pro against GPT-5.5's 58.6, and it leads open-weights models on the Artificial Analysis Intelligence Index. As always, treat benchmarks as a starting point and evaluate on your own workload before switching anything in production.
Can I run GLM-5 as an API instead of a UI?
Yes. vLLM exposes an OpenAI-compatible REST endpoint, and you can containerize that deployment for Runpod Serverless if you want per-request billing rather than a continuously running pod.
