WizardLM 2 8x22B — Hardware Requirements & GPU Compatibility
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WizardLM 2 8x22B is a 140.6B-parameter open language model from alpindale. It supports a context window of up to 65,536 tokens. At Q4_K_M it needs about 85.14 GB of VRAM — see which GPUs and Macs can run it below.
9.9K downloads
414 likesSpecifications
- Publisher
- alpindale
- Parameters
- 140.6B
- Architecture
- MixtralForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 32,000
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does WizardLM 2 8x22B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 60.5 GB | 75.1 GB | 59.76 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 62.3 GB | 76.8 GB | 61.52 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 69.3 GB | 83.9 GB | 68.55 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 85.1 GB | 99.7 GB | 84.37 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 101.0 GB | 115.5 GB | 100.19 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 116.8 GB | 131.3 GB | 116.01 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 141.4 GB | 155.9 GB | 140.62 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run WizardLM 2 8x22B?
Q4_K_M · 85.1 GBWizardLM 2 8x22B (Q4_K_M) requires 85.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 111+ GB is recommended. Using the full 66K context window can add up to 14.6 GB, bringing total usage to 99.7 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run WizardLM 2 8x22B?
Q4_K_M · 85.1 GB5 devices with unified memory can run WizardLM 2 8x22B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Benchmarks
View all 2 →Related Models
Frequently Asked Questions
- How much VRAM does WizardLM 2 8x22B need?
WizardLM 2 8x22B requires 85.1 GB of VRAM at Q4_K_M, or 141.4 GB at Q8_0. Full 66K context adds up to 14.6 GB (99.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 140.6B × 4.8 bits ÷ 8 = 84.4 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 15.3 GB (at full 66K context)
VRAM usage by quantization
Q4_K_M85.1 GBQ4_K_M + full context99.7 GB- Can NVIDIA GeForce RTX 5090 run WizardLM 2 8x22B?
No — WizardLM 2 8x22B requires at least 39.4 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for WizardLM 2 8x22B?
For WizardLM 2 8x22B, Q4_K_M (85.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (97.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 39.4 GB.
VRAM requirement by quantization
IQ2_XXS39.4 GBQ2_K_S57.0 GBIQ3_M64.0 GBQ4_K_S79.9 GBQ4_K_M ★85.1 GBQ8_0141.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run WizardLM 2 8x22B on a Mac?
WizardLM 2 8x22B requires at least 39.4 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run WizardLM 2 8x22B locally?
Yes — WizardLM 2 8x22B can run locally on consumer hardware. At Q4_K_M quantization it needs 85.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is WizardLM 2 8x22B?
At Q4_K_M, WizardLM 2 8x22B can reach ~34 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 85.1 × 0.55 = ~34 tok/s
Estimated speed at Q4_K_M (85.1 GB)
~34 tok/s~21 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of WizardLM 2 8x22B?
At Q4_K_M, the download is about 84.37 GB. The full-precision Q8_0 version is 140.62 GB. The smallest option (IQ2_XXS) is 38.67 GB.
- Which GPUs can run WizardLM 2 8x22B?
No single consumer GPU has enough VRAM to run WizardLM 2 8x22B at Q4_K_M (85.1 GB). Multi-GPU or professional hardware is required.
- Which devices can run WizardLM 2 8x22B?
5 devices with unified memory can run WizardLM 2 8x22B at Q4_K_M (85.1 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB), NVIDIA DGX A100 640GB. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.