GLM-5.3 753B vs GLM-5.2 753B
Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.
Quick verdict
Both models need similar VRAM (515.1 GB) at their recommended builds. The choice comes down to benchmarks and architecture.
Analysis
GLM-5.3 and GLM-5.2 are, underneath, the exact same weights shape. GLM-5.3 is Z.ai's own post-train of GLM-5.2's base model, confirmed by an identical config.json (78 layers, 256 routed experts, the same Multi-head Latent Attention design with a 512-dimension compressed KV latent), so there is no VRAM tradeoff to analyze here at all. The only real questions are what five more months of post-training bought, and whether GLM-5.3's new license and unusual release story are worth accepting for it.
At any quant or context length, the two models' weights and KV-cache totals are identical: 286.9 GB at Q2_K, 322.8 GB total at 8k context, 508.9 GB at the full shared 1,048,576-token window. Capability is where they diverge, and unevenly. General reasoning barely moved: GPQA Diamond went from 91.2 (GLM-5.2's own reported score) to 91.7 (Artificial Analysis's independent measurement of GLM-5.3, since Z.ai doesn't publish GPQA for the new release), a gap easily inside measurement noise between two different scoring methodologies. Coding and, especially, offensive-security ability moved a great deal more, on Z.ai's own benchmark table: Terminal-Bench 2.1 rose from 81.0 to 88.2, the newer and harder Terminal-Bench 3.0 from 4.6 to 28.3, CyberGym from 77.2 to 84.5, and ExploitBench more than doubled, from 24.4 to 54.4. Z.ai attributes the security-benchmark jump to an unplanned, emergent gain in the model's ability to chain multi-stage exploitation plans, discovered during GLM-5.3's post-training, which is also why the open-weight release lagged the API by about two weeks while Z.ai ran a safety review. Licensing is the other real change: GLM-5.2 ships under a plain MIT license, while GLM-5.3 uses a new, custom GLM-5.3 License that is MIT-like for nearly everyone but requires a Z.ai security review for a Model-as-a-Service business whose group revenue tops $10 billion in any trailing 12 months, a threshold that affects essentially no self-hoster.
Bottom line: If you already have the multi-GPU, 80GB-class hardware GLM-5.2 needs, upgrading to GLM-5.3 costs nothing extra in VRAM and buys real, Z.ai-reported gains in coding and agentic security work, at the cost of trading a plain MIT license for a custom one. There's no scenario where GLM-5.2 is the better technical pick over GLM-5.3 today; the only reason to stay on GLM-5.2 is a hard organizational requirement for an unambiguous MIT license, or a policy against running a model with demonstrated offensive-security capability. Given that capability, treat GLM-5.3 like any other dual-use security tool: fine for authorized research and testing, not for casual or unsupervised deployment.
ExploitBench and Terminal-Bench 3.0: what post-training alone bought
Neither suite has a field in this site's benchmark schema. Both come from Z.ai's own GLM-5.3 model card, comparing the new release directly against the GLM-5.2 base it was post-trained from.
Z.ai, GLM-5.3 model card benchmark table (huggingface.co/zai-org/GLM-5.3, 25 August 2026). Both scores are Z.ai's own vendor-reported figures; independent reproduction was not yet available at time of writing.
VRAM at each quantization (8k context)
| Quant | GLM-5.3 753B | GLM-5.2 753B | Diff |
|---|---|---|---|
| FP32 | 3374.9 GB | 3374.9 GB | +0% |
| BF16 | 1688.2 GB | 1688.2 GB | +0% |
| FP16 | 1688.2 GB | 1688.2 GB | +0% |
| Q8_0 | 898.0 GB | 898.0 GB | +0% |
| Q6_K | 693.9 GB | 693.9 GB | +0% |
| Q5_K_M | 601.9 GB | 601.9 GB | +0% |
| Q4_K_M | 515.1 GB | 515.1 GB | +0% |
| Q3_K_M | 407.1 GB | 407.1 GB | +0% |
| Q2_K | 322.8 GB | 322.8 GB | +0% |
| NVFP4 | 423.1 GB | 423.1 GB | +0% |
Diff is GLM-5.3 753B relative to GLM-5.2 753B. Green = lower VRAM (fits more GPUs).
Model specifications
| Spec | GLM-5.3 753B | GLM-5.2 753B |
|---|---|---|
| Org | Z.ai | Z.ai |
| Parameters | 753B | 753B |
| Architecture | MoE (40B active) | MoE (40B active) |
| Context | 1024k tokens | 1024k tokens |
| Modalities | text | text |
| License | GLM-5.3 | MIT |
| Commercial | Yes | Yes |
| Released | 2026-08-25 | 2026-06-13 |
| GPUs (native) | 2 / 119 | 2 / 119 |
Benchmark scores
| Benchmark | GLM-5.3 753B | GLM-5.2 753B |
|---|---|---|
| GPQA Diamond | 91.7 | 91.2 |
| Terminal-Bench 2.1 | 88.2 | 81.0 |
Green = higher score (better). N/A = not yet available. ~ = inherited from a base model, not independently reported for that release itself.
GPUs that run only GLM-5.3 753B(0)
Every GPU that runs GLM-5.3 753B also runs GLM-5.2 753B.
GPUs that run only GLM-5.2 753B(0)
Every GPU that runs GLM-5.2 753B also runs GLM-5.3 753B.
GPUs that run both natively(2)
- Apple M5 Ultra (512GB)512 GB
- Apple M3 Ultra (512GB)512 GB
Which should you use?
- • It's the newer release (2026-08-25 vs 2026-06-13); check the benchmark table above for what actually improved
- No clear spec advantage over GLM-5.3 753B, see the benchmark and VRAM tables above.
Frequently asked questions
- Which is better, GLM-5.3 753B or GLM-5.2 753B?
- Both are capable models. The best choice depends on your hardware and use case.
- How much VRAM does GLM-5.3 753B need vs GLM-5.2 753B?
- At 8k context, GLM-5.3 753B needs approximately 515.1 GB of VRAM at its Q4_K_M build, while GLM-5.2 753B needs 515.1 GB at its Q4_K_M build. At the largest build each ships, GLM-5.3 753B requires 1688.2 GB (FP16) vs 1688.2 GB (FP16) for GLM-5.2 753B.
- Can you run GLM-5.3 753B on the same GPUs as GLM-5.2 753B?
- Yes, 2 GPUs can run both natively in VRAM, including Apple M5 Ultra (512GB), Apple M3 Ultra (512GB). However, no GPU can run GLM-5.3 753B without also fitting GLM-5.2 753B, and no GPU can run GLM-5.2 753B without also fitting GLM-5.3 753B.
- What is the difference between GLM-5.3 753B and GLM-5.2 753B?
- GLM-5.3 753B has 753B parameters (40B active, MoE) with a 1024k context window. GLM-5.2 753B has 753B parameters (40B active, MoE) with a 1024k context window. Licensing differs: GLM-5.3 753B is GLM-5.3 while GLM-5.2 753B is MIT.
- Which model fits in 24 GB of VRAM, GLM-5.3 753B or GLM-5.2 753B?
- Neither fits in 24 GB: GLM-5.3 753B needs 515.1 GB at Q4_K_M and GLM-5.2 753B needs 515.1 GB at Q4_K_M. Both require a multi-GPU server with 516 GB+ of combined VRAM.