GLM-4.7

GLM-4.7, developed by Z.ai, is a flagship 358B parameter Mixture-of-Experts (MoE) model designed for high-end local orchestration and complex agentic workflows. While its total parameter count is massive, the MoE architecture activates only 32B parameters during any single forward pass, positioning it as a direct competitor to other large-scale open-weight models like DeepSeek-V3 and Llama-3-405B. It is released under the permissive MIT license, making it a viable candidate for commercial local deployments where data privacy and custom fine-tuning are priorities.

What distinguishes GLM-4.7 from its predecessors and contemporaries is its focus on "Interleaved and Preserved Thinking." This approach stabilizes multi-step task execution, allowing the model to "think before acting" when integrated into agentic frameworks. For practitioners, this translates to higher reliability in long-running tasks such as multi-file software engineering, complex mathematical reasoning, and autonomous tool use.

GLM-4.7 utilizes a sparse Mixture-of-Experts (MoE) architecture. Out of its 358B total parameters, only 32B are active per token during inference. This provides a specific advantage for local deployments: you get the knowledge density and reasoning depth of a 300B+ model with the inference latency (tokens per second) more typical of a 30B-70B dense model.

Key technical specifications include:

• Parameters: 358B (Total) / 32B (Active)
• Architecture: MoE (Mixture of Experts)
• Context Length: 131,072 tokens
• Modality: Text-only
• License: MIT
• Inference Efficiency: Approximately 55 tokens per second (on optimized enterprise-grade hardware)

The 128k context window is particularly robust, supporting a maximum output capacity of 128,000 tokens. This makes GLM-4.7 suitable for "Long-Context Reasoning" (LCR) tasks, such as analyzing entire codebases or dense technical documentation, without the performance degradation often seen in smaller models when the KV cache fills up.

GLM-4.7 is optimized for high-logic, high-precision workloads. It is not merely a chat model; it is designed to function as the "brain" of an AI agent.

The model shows significant gains in what the developers call "Vibe Coding"—the ability to generate cleaner, modern UI/UX code with accurate layouts and sizing. On the SWE-bench Verified metric, GLM-4.7 scores 73.8%, indicating a high proficiency in generating real-world software patches. It is specifically optimized for use within agent frameworks like Claude Code, Cline, and Roo Code.

With a 95.7% accuracy on AIME 2025 and an 85.7% on GPQA-Diamond, GLM-4.7 competes at the absolute frontier of mathematical and graduate-level scientific reasoning. It is an ideal choice for local RAG (Retrieval-Augmented Generation) systems in STEM fields where logical consistency is non-negotiable.

The model features stabilized function-calling capabilities. It excels at "Terminal Bench" tasks, where it must navigate a command-line interface to solve multi-step problems. Its ability to maintain state across long sequences of tool calls makes it a primary candidate for local autonomous agents that need to interact with local file systems or APIs.

Running a 358B parameter model locally is a significant hardware challenge. While the active parameters are low, you must still fit the total parameter weight into VRAM to avoid the massive performance hit of system RAM offloading.

The total VRAM required depends heavily on the quantization level. For a 358B parameter model:

• FP16 (Original): Requires ~720GB VRAM. This is strictly for multi-node H100/A100 clusters.
• Q4_K_M (Recommended): Requires ~210GB - 230GB VRAM. This can be achieved on a single workstation with 10x RTX 3090/4090s (24GB each) or a Mac Studio with 256GB Unified Memory.
• Q2_K (Minimum): Requires ~120GB - 140GB VRAM. This is the bare minimum for "functional" inference on a multi-GPU setup (e.g., 6x RTX 3090s).

• Consumer GPU Setup: 8x to 10x RTX 4090s using NVLink (where supported) or high-bandwidth PCIe lanes. Expect roughly 5–15 tokens per second depending on the backend (llama.cpp or vLLM).
• Mac Silicon: An M2 Ultra or M4 Max Mac Studio with at least 192GB of Unified Memory is the most power-efficient way to run GLM-4.7.
• Enterprise: A single NVIDIA H100 (80GB) is insufficient for the full model at usable bit-rates; you will need an H100 NVL (dual-GPU) or an A100 80GB 4-GPU node to run 4-bit quants comfortably.

The fastest way to test GLM-4.7 on your local machine is via Ollama. If you have the requisite VRAM, you can pull the model directly:

ollama run glm-4.7

For users with limited hardware, look for GGUF quants specifically optimized for low-bit (IQ2_XS or Q3_K_S) to fit within smaller VRAM envelopes, though expect a noticeable drop in reasoning accuracy.

GLM-4.7 occupies the "Ultra-Large" category of open-weight models.

• GLM-4.7 vs. DeepSeek-V3: Both use MoE architectures and have similar total parameter counts. GLM-4.7 generally shows stronger performance in "agentic" tasks and terminal-based reasoning, while DeepSeek-V3 may lead in raw coding speed and Chinese-language nuances.
• GLM-4.7 vs. Llama-3-405B: Llama-3-405B is a dense model, meaning every parameter is active for every token. This makes Llama-3-405B significantly slower and more computationally expensive to run than GLM-4.7. GLM-4.7 offers a "snappier" local experience due to its 32B active parameters.
• GLM-4.7 vs. Qwen-2.5-72B: While Qwen-2.5-72B is much easier to run on a dual-RTX 4090 setup, GLM-4.7's 358B parameter pool gives it a wider knowledge base and superior zero-shot performance on graduate-level reasoning benchmarks.

Choose GLM-4.7 if you are building complex, multi-step agents and have the hardware overhead to support a 200GB+ VRAM footprint. If you are limited to a single or dual GPU setup, consider a smaller dense model or a more aggressive quantization of GLM-4.7.