GLM 5.3 — Hardware Requirements & GPU Compatibility
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GLM 5.3 continues Z.ai's GLM 5 line as a mixture-of-experts model with roughly 753 billion total parameters. Like other MoE designs, it activates only a fraction of those parameters for each token, which keeps inference more efficient relative to a dense model of comparable total size. The model supports a 1M token context window for very long documents or sessions, and is distributed under a custom license rather than the MIT terms used elsewhere in the family. Given its total parameter count, GLM 5.3 needs multi-GPU or server-class hardware to run; most users will access it through a hosted endpoint instead of locally.
GLM 5.3 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 512 GB (Q4_K_M · ~17 tok/s)
Score 130.1#4 of 100 open#3 of 96 that fit ≤512 GBOther
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
130.1
- 90% range 128.3–131.9
- #4 of 100 open models
- #3 of 96 that fit ≤512 GB
Run GLM 5.3
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve zai-org/GLM-5.3 --max-model-len 16384 --tensor-parallel-size 8Size the nodes from the table above
Download: GLM-5.3-Q4_K_M.gguf452.00 GBunsloth
Other ways
each opens step-by-stepRun in cloud
Rent 7× H100 SXM · Q4_K_M
- Estimated hourly price
- ~$15.48/hr
- est., whole server
- GPUs
- 7 GPUs
- 560 GB of VRAM
How Much VRAM Does GLM 5.3 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 258.5 GB | 257.5 GB | 2-bit · i-quant medium — Importance-weighted 2-bit, medium | Runs on Instinct MI350X 288 GB | |
| 296.1 GB | 295.2 GB | 3-bit · i-quant — Importance-weighted 3-bit | Runs on 5× H100 SXM 80 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 | |
| 409.1 GB | 408.2 GB | 4-bit · i-quant compact — Importance-weighted 4-bit, compact | 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 | |
| 1510.9 GB | 1509.9 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Runs on 3 nodes of 8× H100 SXM |
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 5.3?
Q4_K_M · 456.2 GBGLM 5.3 (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 5.3, including Mac Studio (M3 Ultra, 512GB).
Needs server-class hardware
GLM 5.3 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 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 256 GB: too small for this model (needs ≥ 256 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
How good is GLM 5.3?
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 5.3
- Other models
- Best Score for its VRAM
#3 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 32 with a result for this model; badges show who ran the test.
- GPQA Diamond
Epoch AI · Aug 2026Independent
Independent90.9%
#7/98 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1478.5
#3/187 open
- LiveBench Coding
LiveBench · Jun 2026Independent
Independent79.0
#4/19 open
- LiveBench Reasoning
LiveBench · Jun 2026Independent
Independent85.8
#7/19 open
- AIME 2024/2025
Epoch AI · Aug 2026Independent
Independent91.1%
#13/86 open
- FrontierMath
Epoch AI · Aug 2026Independent
Independent68.8%
#3/17 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
- #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 5.3
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.
Runner-up at this size
DeepSeek V4.1 Flash
DeepSeek
- 0.6 lower Score
- 4.7 GB more VRAM
Score 129.5 · 458.4 GB
Fits in less VRAM
GLM 5.3 Flash
Z.ai
- 5.9 lower Score
- 248.6 GB less VRAM
Score 124.2 · 205.2 GB
Other vendor
MiniMax M3
MiniMax
- 17.6 lower Score
- 195.7 GB less VRAM
Score 112.5 · 258 GB
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MiMo V2.6 Flash RLXiaomi | 131.9 | 186.5 GB | —needs > 24 GB | similar score |
| DeepSeek V4 Flash 0731DeepSeek | 127.6 | 183 GB | —needs > 24 GB | similar score |
| GLM 5.2Z.ai | 122.9 | 453.8 GB | —needs > 24 GB | similar score |
| Qwen3.8 27BAlibaba | 122.1 | 17.9 GB | ~38 | similar score |
| GLM 5.1Z.ai | 117.8 | 454.1 GB | —needs > 24 GB | similar score |
| DeepSeek V3.2DeepSeek | 110.4 | 412.7 GB | —needs > 24 GB | same size |
| NVIDIA Nemotron 3 Ultra 550B A55B BF16NVIDIA | 109.9 | 378.4 GB | —needs > 24 GB | same size |
| DeepSeek V3.2 ExpDeepSeek | 109.0 | 412.7 GB | —needs > 24 GB | same size |
| DeepSeek V3.1 TerminusDeepSeek | 107.3 | 412.2 GB | —needs > 24 GB | same size |
| DeepSeek R1 0528DeepSeek | 103.7 | 412.2 GB | —needs > 24 GB | same size |
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
- 2026-08-25
- License
- Other
Frequently Asked Questions
How much VRAM does GLM 5.3 need?
GLM 5.3 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 5.3?
No — GLM 5.3 requires at least 258.5 GB at IQ2_M, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for GLM 5.3?
For GLM 5.3, Q4_K_M (456.2 GB) offers the best balance of quality and VRAM usage. Q5_K_M (541.0 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 258.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run GLM 5.3 on a Mac?
Yes — Mac Studio (M3 Ultra, 512GB) and above can run GLM 5.3. 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).
Can I run GLM 5.3 locally?
Yes — GLM 5.3 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
What's the download size of GLM 5.3?
At Q4_K_M, the download is about 452.00 GB. The full-precision BF16 version is 1506.66 GB. The smallest option (IQ2_M) is 254.25 GB.
Which GPUs can run GLM 5.3?
No single consumer GPU has enough VRAM to run GLM 5.3 at Q4_K_M (456.2 GB). Multi-GPU or professional hardware is required.
Which devices can run GLM 5.3?
1 device with unified memory can run GLM 5.3 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.