GLM-4.7 VRAM Requirements Explained: Run Locally, on Novita GPU Cloud, or via API

GLM-4.7 is a large Mixture-of-Experts (MoE) “thinking” model built for reasoning, coding, tool use, and long-context workloads. It’s now available on Novita AI with strong performance and competitive pricing.

When you try to run GLM-4.7 locally, the first bottleneck is usually memory, not raw compute—especially GPU VRAM, plus the system RAM required for offloading in practical MoE deployments.

GLM-4.7: What It’s Good At

Across major coding and agent benchmarks, GLM-4.7 is positioned as broadly on par with Claude Sonnet 4.5, and the scores paint a fairly clear picture of its strengths:

• Repo-level software engineering: On SWE-bench Verified, GLM-4.7 ranks #1 among open-source models at 73.8% (+5.8% vs. GLM-4.6), suggesting strong end-to-end capability for diagnosing issues, editing across files, and producing test-passing patches in real repositories.
• High-quality code generation: On LiveCodeBench v6, it reaches an open-source SOTA of 84.9, reported to exceed Claude Sonnet 4.5, indicating competitive performance on coding problems that emphasize correctness and implementation quality.
• Cross-language robustness: A 66.7% score on SWE-bench Multilingual (+12.9%) points to improved reliability when the repo context spans multiple programming languages and mixed-language artifacts.
• Agentic tool-use in practice: Terminal-Bench 2.0 at 41% (+16.5%) highlights meaningful gains in multi-step, tool-driven workflows—exactly the kind of “plan → execute → iterate” loop you want in CLI-based coding agents.

Why VRAM Is The Real Bottleneck

Even though MoE models activate only a subset of experts per token, local inference is still mostly limited by VRAM because the GPU must hold more than just “the model file.”

What actually consumes VRAM?

1. Model weights Quantization reduces weight size, but very large MoE models remain heavy.
2. KV cache (context memory) KV cache grows quickly with:

• context length (8K → 32K → 128K),
• concurrency (parallel sessions multiply cache),
• throughput settings (batching often needs more headroom).

3. Runtime overhead Framework buffers, temporary allocations, fragmentation, and kernel workspaces—often multiple GB.

Why “it fits” can still OOM

A common failure mode: weights barely fit into VRAM, then you increase context length or run a second request and KV cache + overhead pushes you over the edge → out-of-memory errors or heavy CPU/RAM offloading (which can tank speed).

A practical planning rule

Don’t aim for 100% VRAM usage by weights.

• Keep weights ≈ 70–80% of VRAM for moderate contexts
• Reserve 20–30% headroom for KV cache + overhead
• For 64K–128K context or multiple concurrent sessions, reserve even more headroom

Option 1: Run GLM-4.7 Locally

Local deployment is worth it when you must run offline/on-prem or need full control over the entire stack. In most other situations, it’s the highest-effort, highest-maintenance path.

How much memory does GLM-4.7 need? (GGUF variants)

The table below summarizes GGUF variants and the deployment memory estimates shown by Hugging Face Inference Endpoints.

Important

• Size = GGUF file size (weights only; storage footprint)
• Memory requirements = HF Endpoints deployment estimate (weights + runtime overhead)
  Actual requirements increase with context length and concurrency.

Bit-width	Representative quant	Size	Memory req.	HF Suggested GPU	Total VRAM
1-bit	TQ1_0	84.5 GB	86 GB	Nvidia L4 × 4	96 GB
2-bit	Q2_K	131 GB	133 GB	Nvidia A100 × 2	160 GB
3-bit	Q3_K_M	171 GB	173 GB	Nvidia L40S × 4	192 GB
4-bit	Q4_K_M	216 GB	218 GB	Nvidia A100 × 4	320 GB
5-bit	Q5_K_M	254 GB	256 GB	Nvidia A100 × 4	320 GB
6-bit	Q6_K	294 GB	296 GB	Nvidia A100 × 4	320 GB
8-bit	Q8_0	381 GB	383 GB	Nvidia A100 × 8	640 GB
16-bit	BF16	717 GB	719 GB	Nvidia H200 × 8	1128 GB

Minimal local knobs that matter

If you go local, focus on three levers:

• Quantization (biggest VRAM lever)
• Offloading (move some layers to CPU/RAM)
• Context length (reduce context first if you OOM)

Option 2: Novita GPU Cloud

If local feels like too much infrastructure work, Novita GPU Cloud is a clean middle path: you keep a “local-style” workflow—your runtime, your inference stack, your benchmarking scripts—without buying GPUs or managing drivers, failures, and capacity.

Modes

• GPU Instances — GPU VMs for long-running, reproducible workloads
• Serverless GPUs — per-second endpoints ideal for bursty usage
• Bare Metal — maximum isolation and the most consistent performance

Why GPU Cloud works well for GLM-4.7
Local deployments are usually constrained by VRAM headroom (weights + KV cache + overhead), especially with long context or concurrency. GPU Cloud lets you test those constraints across real hardware tiers (24GB / 48GB / 80GB+)—without owning the hardware.

Option 3: Novita Model API

Once you’ve seen how quickly VRAM, context length, and concurrency become constraints—locally or on GPU Cloud—the lowest-friction route is often the Novita Model API.

Novita AI offers GLM-4.7 API access, eliminating the need for expensive local hardware while providing production-ready inference at scale.

Step 1: Log In and Access the Model Library

Log in (or sign up) to your Novita AI account and navigate to the Model Library.

Step 2: Choose GLM-4.7

Browse the available models and select GLM-4.7 based on your workload requirements.

Step 3: Start Your Free Trial

Activate your free trial to explore GLM-4.7’s reasoning, long-context, and cost-performance characteristics.

Step 4: Get Your API Key

Open the Settings page to generate and copy your API key for authentication.

Step 5: Install and Call the API (Python Example)

Below is a simple example using the Chat Completions API with Python:

from openai import OpenAI

client = OpenAI(
    api_key="<Your API Key>",
    base_url="https://api.novita.ai/openai"
)

response = client.chat.completions.create(
    model="zai-org/glm-4.7",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello, how are you?"}
    ],
    max_tokens=131072,
    temperature=0.7
)

print(response.choices[0].message.content)

This setup lets you control reasoning depth, token usage, and generation behavior at the API level—especially useful when you want to combine turn-level “thinking” with predictable cost and latency, instead of sizing hardware around GLM-4.7’s VRAM needs.

Conclusion：Which Option Should You Choose?

Pick a deployment path based on control vs. operational effort vs. scalability:

Option	Pros	Cons
Local	Full control, no per-token cost	Hardware limits + operational complexity
GPU Cloud	Flexible hardware, near-local control	Driver/runtime management + variable costs
API	Simplest path, predictable scaling	Less low-level control

Decision tree

• Choose Local if you must run offline/on-prem or need full control over data + infrastructure.
• Choose GPU Cloud if you want reproducible benchmarking and control without owning GPUs.
• Choose API if you want the simplest path to production with minimal ops overhead.

GLM‑4.7 is extremely capable, but local deployments run into VRAM limits once you push long context and high concurrency; for most teams, the most practical path is to start with clear tier expectations, experiment on Novita GPU Cloud, and then either stay there or move to Novita’s OpenAI‑compatible API for the lowest‑ops route to production.

Frequently Asked Questions

Novita AI is the All-in-one cloud platform that empowers your AI ambitions. Integrated APIs, serverless, GPU Instance — the cost-effective tools you need. Eliminate infrastructure, start free, and make your AI vision a reality.