GLM 4.6 Derestricted — Hardware Requirements & GPU Compatibility
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GLM 4.6 Derestricted is a 356.8B-parameter open language model from ArliAI in the GLM 4 family. It supports a context window of up to 202,752 tokens. At Q4_K_M it needs about 214.69 GB of VRAM — see which GPUs and Macs can run it below.
GLM 4.6 Derestricted runs on a 256 GB+ Mac or a datacenter GPU
Best local pick → Mac Studio (M3 Ultra, 256GB) · Q4_K_M · 216.9 GB · ~19 tok/s
Macs from 128 GB (IQ2_M · ~26 tok/s)
MIT
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run GLM 4.6 Derestricted
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/bartowski/ArliAI_GLM-4.6-Derestricted-GGUF:Q4_K_MQ4_K_M runs on one Instinct MI325X 256 GB · 16K context
Download: ArliAI_GLM-4.6-Derestricted-Q4_K_M/ArliAI_GLM-4.6-Derestricted-Q4_K_M-00001-of-00006.gguf217.65 GB + 5 partsbartowski
Other ways
each opens step-by-stepRun in cloud
Fits on B300 (288 GB) (71 GB headroom) · Q4_K_M
- Generation speed
- ~111 tok/s
- generation speed
- Cost per 1M output tokens
- $17.35
- per 1M output tokens
How Much VRAM Does GLM 4.6 Derestricted Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 152.3 GB | 157.1 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Runs on B200 192 GB | |
| 156.7 GB | 161.5 GB | 3-bit small — 3-bit small quantization | Runs on B200 192 GB | |
| 174.6 GB | 179.4 GB | 3-bit medium — 3-bit medium quantization | Runs on B200 192 GB | |
| 179.0 GB | 183.8 GB | 4-bit · legacy — 4-bit legacy quantization | Runs on B200 192 GB | |
| 214.7 GB | 219.5 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Runs on Instinct MI325X 256 GB | |
| 254.8 GB | 259.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Runs on Instinct MI350X 288 GB | |
| 295.0 GB | 299.8 GB | 6-bit, very good — 6-bit quantization, very good quality | Runs on 5× H100 SXM 80 GB | |
| 357.4 GB | 362.2 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Runs on 5× H100 SXM 80 GB |
Selected Q4_K_M
4-bit medium quantization216.9GBat 16K context
- Weights
- 214.1 GB
- KV cache
- 2.6 GB
- Overhead
- 0.3 GB
Server-class: ~216.9 GB at 16K context · one Instinct MI325X 256 GB
Which Hardware Can Run GLM 4.6 Derestricted?
Q4_K_M · 214.7 GBGLM 4.6 Derestricted (Q4_K_M) requires 214.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 280+ GB is recommended. Using the full 203K context window can add up to 31.5 GB, bringing total usage to 246.2 GB. No consumer GPU has enough memory. 2 devices with unified memory can run GLM 4.6 Derestricted, including Mac Studio (M3 Ultra, 512GB), Mac Studio (M3 Ultra, 256GB).
128–144 GB
Q4_K_M · best fit IQ2_M (123.3 GB)
4 GPUs · 2 Mac classes
GH200 Grace Hopper Superchip~115Simulate output on GH200 Grace Hopper SuperchipH200 NVL~114Simulate output on H200 NVLH200 SXM~114Simulate output on H200 SXMM2 Ultra*128 GB~26Simulate output on Mac Pro M2 Ultra (128 GB)M5 Max128 GB~22Simulate output on Mac Studio M5 Max (128 GB)192+ GB
Q4_K_M · best fit Q3_K_L (185.7 GB)
6 GPUs · 1 Mac class · 2 device families
B200~119Simulate output on B200B300~119Simulate output on B300Instinct MI350X~45Simulate output on Instinct MI350XM3 Ultra*256–512 GB~20Simulate output on Mac Studio (M3 Ultra, 256GB)8x H100 SXM5~165Simulate output on NVIDIA DGX H1008x A100 SXM4~149Simulate output on NVIDIA DGX A100 640GB
By VRAM tier · typical GPUs and Macs at Q4_K_M
Consumer GPUs (up to 32 GB) and Macs up to 96 GB: too small for this model (needs ≥ 101 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
Alternatives to GLM 4.6 Derestricted
GLM 4 family · 18 models →Similar models
1| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| K2 Horizon 375B A23BIFM | — | 762.7 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- ArliAI
- Family
- GLM 4
- Parameters
- 356.8B
- Active parameters
- 34B per token
- Architecture
- Glm4MoeForCausalLM
- Context Length
- 202,752 tokens
- Vocabulary Size
- 151,552
- Release Date
- 2025-12-02
- License
- MIT
Frequently Asked Questions
How much VRAM does GLM 4.6 Derestricted need?
GLM 4.6 Derestricted requires 214.7 GB of VRAM at Q4_K_M, or 714.2 GB at BF16. Full 203K context adds up to 31.5 GB (246.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 356.8B × 4.8 bits ÷ 8 = 214.1 GB
KV Cache + Overhead ≈ 0.6 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 ≈ 32.1 GB (at full 203K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run GLM 4.6 Derestricted?
No — GLM 4.6 Derestricted requires at least 98.7 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for GLM 4.6 Derestricted?
For GLM 4.6 Derestricted, Q4_K_M (214.7 GB) offers the best balance of quality and VRAM usage. Q5_K_S (245.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 98.7 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run GLM 4.6 Derestricted on a Mac?
Yes — Mac Pro M2 Ultra (128 GB) and 14 other Macs can run GLM 4.6 Derestricted, the smallest at IQ2_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 214.7 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio (M3 Ultra, 256GB).
Can I run GLM 4.6 Derestricted locally?
Yes — GLM 4.6 Derestricted can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is GLM 4.6 Derestricted?
At Q4_K_M, GLM 4.6 Derestricted can reach ~44 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B300 → 20.5 GB active ÷ (8000 × 0.65) = 3.94 ms, plus 92 layers × 0.055 ms = 5.06 ms, so 1000 ÷ 9.00 ms = ~111 tok/s
Estimated speed at Q4_K_M (214.7 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of GLM 4.6 Derestricted?
At Q4_K_M, the download is about 214.07 GB. The full-precision BF16 version is 713.57 GB. The smallest option (IQ2_XXS) is 98.12 GB.
Which GPUs can run GLM 4.6 Derestricted?
No single consumer GPU has enough VRAM to run GLM 4.6 Derestricted at Q4_K_M (214.7 GB). Multi-GPU or professional hardware is required.
Which devices can run GLM 4.6 Derestricted?
2 devices with unified memory can run GLM 4.6 Derestricted at Q4_K_M (214.7 GB), including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.