GLM 5.2 — Hardware Requirements & GPU Compatibility
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GLM 5.2, released by Z.ai in June 2026, is a mixture-of-experts language model in the GLM 5 family with roughly 753 billion total parameters. As an MoE design, it activates only a subset of those parameters for any given token, which helps keep inference more efficient than a dense model of the same total size, even though it remains a very large checkpoint overall. The model offers a 1M token context window, suited to long-document and long-context workloads, and is released under the MIT license, one of the more permissive terms available for local and commercial use. Given its total parameter count, running GLM 5.2 locally needs multi-GPU or server-class hardware; most users will access it through a hosted endpoint rather than on a single machine.
GLM 5.2 runs on a 512 GB+ Mac or a multi-GPU server
Best local pick → Mac Studio (M3 Ultra, 512GB) · Q4_K_M · 453.8 GB · ~17 tok/s
Macs from 256 GB (IQ2_XS · ~26 tok/s)
Score 122.9#10 of 100 open#8 of 96 that fit ≤512 GBMIT
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
122.9
- 90% range 121.1–124.7
- #10 of 100 open models
- #8 of 96 that fit ≤512 GB
Run GLM 5.2
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve zai-org/GLM-5.2 --max-model-len 16384 --tensor-parallel-size 8Size the nodes from the table above
Download: UD-Q4_K_M/GLM-5.2-UD-Q4_K_M-00001-of-00011.gguf465.83 GB + 10 partsunsloth
Other ways
each opens step-by-stepRun in cloud
Rent 7× H100 SXM · Q4_K_M
- Estimated hourly price
- ~$18.83/hr
- est., whole server
- GPUs
- 7 GPUs
- 560 GB of VRAM
How Much VRAM Does GLM 5.2 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 211.4 GB | 210.4 GB | 2-bit · i-quant extreme compression — Importance-weighted 2-bit, extreme compression — significant quality loss | Runs on Instinct MI325X 256 GB | |
| 230.2 GB | 229.2 GB | 2-bit · i-quant extra small — Importance-weighted 2-bit, extra small | Runs on Instinct MI325X 256 GB | |
| 324.4 GB | 323.4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Runs on 5× H100 SXM 80 GB | |
| 371.5 GB | 370.5 GB | 3-bit medium — 3-bit medium quantization | Runs on 6× H100 SXM 80 GB | |
| 456.2 GB | 455.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Runs on 7× H100 SXM 80 GB | |
| 541.0 GB | 540 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Runs on 8× H100 SXM 80 GB | |
| 625.7 GB | 624.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Runs on 5× H200 SXM 141 GB | |
| 757.6 GB | 756.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Runs on 6× H200 SXM 141 GB |
Selected Q4_K_M
4-bit medium quantization453.8GBat 16K context
- Weights
- 452.0 GB
- KV cache
- 1.5 GB
- Overhead
- 0.3 GB
Server-class: ~453.8 GB at 16K context · about 7× H100 SXM 80 GB
Which Hardware Can Run GLM 5.2?
Q4_K_M · 456.2 GBGLM 5.2 (Q4_K_M) requires 456.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 594+ GB is recommended. Using the full 1049K context window can add up to 90.3 GB, bringing total usage to 546.5 GB. No consumer GPU has enough memory. 1 device with unified memory can run GLM 5.2, including Mac Studio (M3 Ultra, 512GB).
Needs server-class hardware
GLM 5.2 runs on a 512 GB+ Mac or a multi-GPU server · Q4_K_M needs ~453.8 GB
| Hardware | Quant | Memory | Speed | Context | Hardware page | |
|---|---|---|---|---|---|---|
| Mac 256 GB · Mac Studio (M3 Ultra, 256GB) | IQ2_XS | 227.8 GB | ~26 tok/s | 256K ctx | Mac Studio → | IQ2_XS · 227.8 GB · ~26 tok/s · 256K ctx |
| Mac 512 GB · Mac Studio (M3 Ultra, 512GB) | Q4_K_M | 453.8 GB | ~17 tok/s | 512K ctx | Mac Studio → | Q4_K_M · 453.8 GB · ~17 tok/s · 512K ctx |
| 7× H100 SXM 80 GB · 560 GB | Q4_K_M | 453.8 GB | — | 512K ctx | H100 SXM → | Q4_K_M · 453.8 GB · 512K ctx |
| 4× H200 SXM 141 GB · 564 GB | Q4_K_M | 453.8 GB | — | 512K ctx | H200 SXM → | Q4_K_M · 453.8 GB · 512K ctx |
| 3× B200 192 GB · 576 GB | Q4_K_M | 453.8 GB | — | 512K ctx | B200 → | Q4_K_M · 453.8 GB · 512K ctx |
Also: Mac Studio (M3 Ultra, 512GB) · NVIDIA DGX A100 640GB · NVIDIA DGX H100
By VRAM tier · typical GPUs and Macs at Q4_K_M
Consumer GPUs (up to 32 GB) and Macs up to 192 GB: too small for this model (needs ≥ 208.9 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
How good is GLM 5.2?
The llmrun Score merges 26 public benchmark boards into a single figure, where 100 represents the average for open models.
Score vs VRAM in the ≤512 GB tier
- GLM 5.2
- Other models
- Best Score for its VRAM
#8 of 96 that fit ≤512 GB
Each dot is a model at the best quant that fits ≤512 GB. The line joins the models with the best Score for their VRAM; a dot below it is beaten by a model that needs no more memory.
Full ≤512 GB ranking →Benchmarks
The 6 boards that weigh most in the llmrun Score, of 28 with a result for this model; badges show who ran the test.
- GPQA Diamond
Epoch AI · Jun 2026Independent
Independent91.9%
#2/98 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1475.9
#4/187 open
- LiveBench Coding
LiveBench · Jun 2026Independent
Independent79.7
#3/19 open
- LiveBench Reasoning
LiveBench · Jun 2026Independent
Independent78.6
#13/19 open
- AIME 2024/2025
Epoch AI · Jun 2026Independent
Independent86.4%
#21/86 open
- FrontierMath
Epoch AI · Jun 2026Independent
Independent59.2%
#6/17 open
In the ≤512 GB class
96 models fit ≤512 GBSame Score, only models that fit ≤512 GB at their best quant: this model's peers by memory need.
- #1
MiMo V2.6 Pro RLXiaomi
133.4
499.3 GB
- #2
MiMo V2.6 Flash RLXiaomi
131.9
186.5 GB
- #3
GLM 5.3Z.ai
130.1
453.8 GB
- #4
DeepSeek V4.1 FlashDeepSeek
129.5
458.4 GB
- #5
DeepSeek V4 Flash 0731DeepSeek
127.6
183.0 GB
- #6
125.3
461.5 GB
Alternatives to GLM 5.2
GLM 5 family · 6 models →Official models with an llmrun Score, each answering one question; sizes are each model's default quant (usually Q4_K_M) with a 16K-token context.
Best alternative at this size
GLM 5.3
Z.ai
- 7.2 higher Score
Score 130.1 · 453.8 GB
Fits in less VRAM
GLM 5.3 Flash
Z.ai
- 1.3 higher Score
- 248.6 GB less VRAM
Score 124.2 · 205.2 GB
Other vendor
DeepSeek V4.1 Flash
DeepSeek
- 6.6 higher Score
- 4.7 GB more VRAM
Score 129.5 · 458.4 GB
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| DeepSeek V4 Flash 0731DeepSeek | 127.6 | 183 GB | —needs > 24 GB | similar score |
| Qwen3.8 27BAlibaba | 122.1 | 17.9 GB | ~38 | similar score |
| Inkling SmallThinking Machines | 119.6 | 162.7 GB | —needs > 24 GB | similar score |
| GLM 5.1Z.ai | 117.8 | 454.1 GB | —needs > 24 GB | similar score |
| Qwen3.5 397B A17BAlibaba | 113.0 | 242.6 GB | —needs > 24 GB | similar score |
| MiniMax M3MiniMax | 112.5 | 258 GB | —needs > 24 GB | similar score |
| GLM 5Z.ai | 111.6 | 454.1 GB | —needs > 24 GB | similar score |
| DeepSeek V3.2DeepSeek | 110.4 | 412.7 GB | —needs > 24 GB | similar score |
| NVIDIA Nemotron 3 Ultra 550B A55B BF16NVIDIA | 109.9 | 378.4 GB | —needs > 24 GB | similar score |
| DeepSeek V3.2 ExpDeepSeek | 109.0 | 412.7 GB | —needs > 24 GB | same size |
Fine-tunes & variants(2)
Specifications
- Publisher
- Z.ai
- Family
- GLM 5
- Parameters
- 753.3B
- Active parameters
- 41.8B per token
- Architecture
- GlmMoeDsaForCausalLM
- Context Length
- 1,048,576 tokens
- Vocabulary Size
- 154,880
- Release Date
- 2026-06-16
- License
- MIT
Frequently Asked Questions
How much VRAM does GLM 5.2 need?
GLM 5.2 requires 456.2 GB of VRAM at Q4_K_M, or 1510.9 GB at BF16. Full 1049K context adds up to 90.3 GB (546.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 753.3B × 4.8 bits ÷ 8 = 452 GB
KV Cache + Overhead ≈ 4.2 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 94.5 GB (at full 1049K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run GLM 5.2?
No — GLM 5.2 requires at least 211.4 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for GLM 5.2?
For GLM 5.2, Q4_K_M (456.2 GB) offers the best balance of quality and VRAM usage. Q5_K_S (522.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 211.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run GLM 5.2 on a Mac?
Yes — Mac Studio (M3 Ultra, 256GB) and 1 other Mac can run GLM 5.2, the smallest at IQ2_XS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 456.2 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio (M3 Ultra, 512GB).
Can I run GLM 5.2 locally?
Yes — GLM 5.2 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
What's the download size of GLM 5.2?
At Q4_K_M, the download is about 452.00 GB. The full-precision BF16 version is 1506.66 GB. The smallest option (IQ2_XXS) is 207.17 GB.
Which GPUs can run GLM 5.2?
No single consumer GPU has enough VRAM to run GLM 5.2 at Q4_K_M (456.2 GB). Multi-GPU or professional hardware is required.
Which devices can run GLM 5.2?
1 device with unified memory can run GLM 5.2 at Q4_K_M (456.2 GB), including Mac Studio (M3 Ultra, 512GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.