GLM-5.2 753B vs DeepSeek V4 Pro 1.6T
Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.
Quick verdict
GLM-5.2 753B is more hardware-efficient — it needs 486.5 GB at Q4_K_M vs 1010.0 GB for DeepSeek V4 Pro 1.6T, fitting on 3 GPUs natively.
VRAM at each quantization (8k context)
FP32
GLM-5.2 753B
3385.2 GB
DeepSeek V4 Pro 1.6T
7169.1 GB
BF16
GLM-5.2 753B
1698.4 GB
DeepSeek V4 Pro 1.6T
3585.1 GB
FP16
GLM-5.2 753B
1698.4 GB
DeepSeek V4 Pro 1.6T
3585.1 GB
Q8_0
GLM-5.2 753B
855.1 GB
DeepSeek V4 Pro 1.6T
1793.1 GB
Q6_K
GLM-5.2 753B
703.3 GB
DeepSeek V4 Pro 1.6T
1470.6 GB
Q5_K_M
GLM-5.2 753B
554.8 GB
DeepSeek V4 Pro 1.6T
1155.2 GB
Q4_K_M
GLM-5.2 753B
486.5 GB
DeepSeek V4 Pro 1.6T
1010.0 GB
Q3_K_M
GLM-5.2 753B
374.4 GB
DeepSeek V4 Pro 1.6T
771.7 GB
Q2_K
GLM-5.2 753B
289.2 GB
DeepSeek V4 Pro 1.6T
590.7 GB
NVFP4
GLM-5.2 753B
433.4 GB
DeepSeek V4 Pro 1.6T
897.1 GB
| Quant | GLM-5.2 753B | DeepSeek V4 Pro 1.6T | Diff |
|---|---|---|---|
| FP32 | 3385.2 GB | 7169.1 GB | -53% |
| BF16 | 1698.4 GB | 3585.1 GB | -53% |
| FP16 | 1698.4 GB | 3585.1 GB | -53% |
| Q8_0 | 855.1 GB | 1793.1 GB | -52% |
| Q6_K | 703.3 GB | 1470.6 GB | -52% |
| Q5_K_M | 554.8 GB | 1155.2 GB | -52% |
| Q4_K_M | 486.5 GB | 1010.0 GB | -52% |
| Q3_K_M | 374.4 GB | 771.7 GB | -51% |
| Q2_K | 289.2 GB | 590.7 GB | -51% |
| NVFP4 | 433.4 GB | 897.1 GB | -52% |
Diff is GLM-5.2 753B relative to DeepSeek V4 Pro 1.6T. Green = lower VRAM (fits more GPUs).
Model specifications
| Spec | GLM-5.2 753B | DeepSeek V4 Pro 1.6T |
|---|---|---|
| Org | Z.ai | DeepSeek |
| Parameters | 753B | 1600B |
| Architecture | MoE (40B active) | MoE (49B active) |
| Context | 977k tokens | 1024k tokens |
| Modalities | text | text, vision, video |
| License | MIT | MIT |
| Commercial | Yes | Yes |
| Released | 2026-06-13 | 2026-04-24 |
| GPUs (native) | 3 / 107 | 0 / 107 |
Benchmark scores
| Benchmark | GLM-5.2 753B | DeepSeek V4 Pro 1.6T |
|---|---|---|
| MMLU-Pro | 80.6 | 87.5 |
| GPQA Diamond | 91.2 | 90.1 |
Green = higher score (better). — = not yet available.
GPUs that run only GLM-5.2 753B(3)
- Apple M4 Ultra (384GB)384 GB
- Apple M3 Ultra (512GB)512 GB
- Apple M2 Ultra (384GB)384 GB
GPUs that run only DeepSeek V4 Pro 1.6T(0)
Every GPU that runs DeepSeek V4 Pro 1.6T also runs GLM-5.2 753B.
Which should you use?
Choose GLM-5.2 753B if:
- • You have limited VRAM — it's a smaller model needing 486.5 GB vs 1010.0 GB
- • You're running coding tasks
- • You need chain-of-thought reasoning
Choose DeepSeek V4 Pro 1.6T if:
- • You want maximum capability and have a 1011 GB+ GPU
- • Long context matters — it supports 1024k tokens vs 977k
- • Benchmark quality matters — scores 87.5 vs 80.6 on MMLU-Pro
- • You need vision/image understanding
Frequently asked questions
- Which is better, GLM-5.2 753B or DeepSeek V4 Pro 1.6T?
- GLM-5.2 753B has 753B parameters vs 1600B for DeepSeek V4 Pro 1.6T, so DeepSeek V4 Pro 1.6T is the larger model. GLM-5.2 753B is more hardware-efficient, needing 486.5 GB at Q4_K_M vs 1010.0 GB. GLM-5.2 753B runs on more GPUs natively (3 vs 0). On MMLU-Pro, DeepSeek V4 Pro 1.6T scores higher (87.5 vs 80.6).
- How much VRAM does GLM-5.2 753B need vs DeepSeek V4 Pro 1.6T?
- At Q4_K_M quantization with 8k context, GLM-5.2 753B needs approximately 486.5 GB of VRAM, while DeepSeek V4 Pro 1.6T needs 1010.0 GB. At FP16, GLM-5.2 753B requires 1698.4 GB vs 3585.1 GB for DeepSeek V4 Pro 1.6T.
- Can you run GLM-5.2 753B on the same GPUs as DeepSeek V4 Pro 1.6T?
- These models have very different VRAM requirements, so they do not share the same compatible GPU set.
- What is the difference between GLM-5.2 753B and DeepSeek V4 Pro 1.6T?
- GLM-5.2 753B has 753B parameters (40B active, MoE) with a 977k context window. DeepSeek V4 Pro 1.6T has 1600B parameters (49B active, MoE) with a 1024k context window.
- Which model fits in 24 GB of VRAM, GLM-5.2 753B or DeepSeek V4 Pro 1.6T?
- Neither fits in 24 GB at Q4_K_M — GLM-5.2 753B needs 486.5 GB and DeepSeek V4 Pro 1.6T needs 1010.0 GB. Both require at least a 48 GB GPU.