GLM 5.1 — Hardware Requirements & GPU Compatibility
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GLM 5.1 is a 753.9B-parameter open language model from Z.ai in the GLM 5 family. It supports a context window of up to 202,752 tokens. At Q4_K_M it needs about 456.54 GB of VRAM — see which GPUs and Macs can run it below.
GLM 5.1 runs on a 512 GB+ Mac or a multi-GPU server
Best local pick → Mac Studio (M3 Ultra, 512GB) · Q4_K_M · 454.1 GB · ~17 tok/s
Macs from 256 GB (IQ2_S · ~26 tok/s)
Score 117.8#15 of 100 open#13 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 →
117.8
- 90% range 114.8–120.8
- #15 of 100 open models
- #13 of 96 that fit ≤512 GB
Run GLM 5.1
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve zai-org/GLM-5.1 --max-model-len 16384 --tensor-parallel-size 8Size the nodes from the table above
Download: UD-Q4_K_M/GLM-5.1-UD-Q4_K_M-00001-of-00011.gguf464.50 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 5.1 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 324.6 GB | 323.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Runs on 5× H100 SXM 80 GB | |
| 334.0 GB | 333.1 GB | 3-bit small — 3-bit small quantization | Runs on 5× H100 SXM 80 GB | |
| 371.7 GB | 370.8 GB | 3-bit medium — 3-bit medium quantization | Runs on 6× H100 SXM 80 GB | |
| 381.2 GB | 380.2 GB | 4-bit · legacy — 4-bit legacy quantization | Runs on 6× H100 SXM 80 GB | |
| 456.5 GB | 455.6 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Runs on 7× H100 SXM 80 GB | |
| 541.4 GB | 540.4 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Runs on 8× H100 SXM 80 GB | |
| 626.2 GB | 625.2 GB | 6-bit, very good — 6-bit quantization, very good quality | Runs on 5× H200 SXM 141 GB | |
| 758.1 GB | 757.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Runs on 6× H200 SXM 141 GB |
Selected Q4_K_M
4-bit medium quantization454.1GBat 16K context
- Weights
- 452.3 GB
- KV cache
- 1.5 GB
- Overhead
- 0.3 GB
Server-class: ~454.1 GB at 16K context · about 7× H100 SXM 80 GB
Which Hardware Can Run GLM 5.1?
Q4_K_M · 456.5 GBGLM 5.1 (Q4_K_M) requires 456.5 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 203K context window can add up to 14.3 GB, bringing total usage to 470.8 GB. No consumer GPU has enough memory. 1 device with unified memory can run GLM 5.1, including Mac Studio (M3 Ultra, 512GB).
Needs server-class hardware
GLM 5.1 runs on a 512 GB+ Mac or a multi-GPU server · Q4_K_M needs ~454.1 GB
| Hardware | Quant | Memory | Speed | Context | Hardware page | |
|---|---|---|---|---|---|---|
| Mac 256 GB · Mac Studio (M3 Ultra, 256GB) | IQ2_S | 237.4 GB | ~26 tok/s | 128K ctx | Mac Studio → | IQ2_S · 237.4 GB · ~26 tok/s · 128K ctx |
| Mac 512 GB · Mac Studio (M3 Ultra, 512GB) | Q4_K_M | 454.1 GB | ~17 tok/s | 198K ctx | Mac Studio → | Q4_K_M · 454.1 GB · ~17 tok/s · 198K ctx |
| 7× H100 SXM 80 GB · 560 GB | Q4_K_M | 454.1 GB | — | 198K ctx | H100 SXM → | Q4_K_M · 454.1 GB · 198K ctx |
| 4× H200 SXM 141 GB · 564 GB | Q4_K_M | 454.1 GB | — | 198K ctx | H200 SXM → | Q4_K_M · 454.1 GB · 198K ctx |
| 3× B200 192 GB · 576 GB | Q4_K_M | 454.1 GB | — | 198K ctx | B200 → | Q4_K_M · 454.1 GB · 198K 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 ≥ 209.1 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
How good is GLM 5.1?
The llmrun Score merges 14 public benchmark boards into a single figure, where 100 represents the average for open models.
Score vs VRAM in the ≤512 GB tier
- GLM 5.1
- Other models
- Best Score for its VRAM
#13 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 16 with a result for this model; badges show who ran the test.
- GPQA Diamond
Epoch AI · Apr 2026Independent
Independent89.9%
#10/98 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1465.2
#8/187 open
- AIME 2024/2025
Epoch AI · Apr 2026Independent
Independent93.3%
#10/86 open
- FrontierMath
Epoch AI · Apr 2026Independent
Independent36.8%
#13/17 open
- LMArena WebDev
LMArena · Oct 2026Arena
Arena1508.5
#15/52 open
- SciCode
Epoch AI · Apr 2026Lab-reported
Lab-reported44.8%
#25/66 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 5.1
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
- 12.3 higher Score
- 0.3 GB less VRAM
Score 130.1 · 453.8 GB
Fits in less VRAM
GLM 5.3 Flash
Z.ai
- 6.4 higher Score
- 248.9 GB less VRAM
Score 124.2 · 205.2 GB
Other vendor
DeepSeek V4.1 Flash
DeepSeek
- 11.7 higher Score
- 4.3 GB more VRAM
Score 129.5 · 458.4 GB
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| 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 |
| Inkling SmallThinking Machines | 119.6 | 162.7 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 |
| Qwen3.6 27BAlibaba | 111.2 | 17.9 GB | ~38 | 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
- 753.9B
- Active parameters
- 42.4B per token
- Architecture
- GlmMoeDsaForCausalLM
- Context Length
- 202,752 tokens
- Vocabulary Size
- 154,880
- Release Date
- 2026-04-03
- License
- MIT
Frequently Asked Questions
How much VRAM does GLM 5.1 need?
GLM 5.1 requires 456.5 GB of VRAM at Q4_K_M, or 1512.0 GB at BF16. Full 203K context adds up to 14.3 GB (470.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 753.9B × 4.8 bits ÷ 8 = 452.3 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 ≈ 18.5 GB (at full 203K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run GLM 5.1?
No — GLM 5.1 requires at least 211.5 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for GLM 5.1?
For GLM 5.1, Q4_K_M (456.5 GB) offers the best balance of quality and VRAM usage. Q4_K_L (466.0 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 211.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run GLM 5.1 on a Mac?
Yes — Mac Studio (M3 Ultra, 256GB) and 1 other Mac can run GLM 5.1, the smallest at IQ2_S. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 456.5 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio (M3 Ultra, 512GB).
Can I run GLM 5.1 locally?
Yes — GLM 5.1 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
What's the download size of GLM 5.1?
At Q4_K_M, the download is about 452.32 GB. The full-precision BF16 version is 1507.73 GB. The smallest option (IQ2_XXS) is 207.31 GB.
Which GPUs can run GLM 5.1?
No single consumer GPU has enough VRAM to run GLM 5.1 at Q4_K_M (456.5 GB). Multi-GPU or professional hardware is required.
Which devices can run GLM 5.1?
1 device with unified memory can run GLM 5.1 at Q4_K_M (456.5 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.