Hugging Face·MistralForCausalLM

Zephyr 7B Beta — Hardware Requirements & GPU Compatibility

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Zephyr 7B Beta is a 7.2B-parameter open language model from Hugging Face. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 4.91 GB of VRAM — see which GPUs and Macs can run it below.

144.5K downloads 1.8K likes 3.2K quant downloads33K context

Specifications

Publisher
Hugging Face
Parameters
7.2B
Architecture
MistralForCausalLM
Context Length
32,768 tokens
Vocabulary Size
32,000
Release Date
2023-10-26
License
MIT

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How Much VRAM Does Zephyr 7B Beta Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_K3.403.6 GB
Q3_K_S3.503.7 GB
Q3_K_M3.904.1 GB
Q4_04.004.2 GB
Q4_K_M4.804.9 GB
Q5_K_M5.705.7 GB
Q6_K6.606.5 GB
Q8_08.007.8 GB

est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.

Which GPUs Can Run Zephyr 7B Beta?

Q4_K_M · 4.9 GB

Zephyr 7B Beta (Q4_K_M) requires 4.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 33K context window can add up to 4.0 GB, bringing total usage to 8.9 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Which Devices Can Run Zephyr 7B Beta?

Q4_K_M · 4.9 GB

33 devices with unified memory can run Zephyr 7B Beta, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.

Where to Download Zephyr 7B Beta

Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.

Related Models

Frequently Asked Questions

How much VRAM does Zephyr 7B Beta need?

Zephyr 7B Beta requires 4.9 GB of VRAM at Q4_K_M, or 15.1 GB at BF16. Full 33K context adds up to 4.0 GB (8.9 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 7.2B × 4.8 bits ÷ 8 = 4.3 GB

KV Cache + Overhead 0.6 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 4.6 GB (at full 33K context)

VRAM usage by quantization

4.9 GB
8.9 GB

Learn more about VRAM estimation →

What's the best quantization for Zephyr 7B Beta?

For Zephyr 7B Beta, Q4_K_M (4.9 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.1 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 3.6 GB.

VRAM requirement by quantization

IQ3_XS
3.6 GB
IQ3_M
3.8 GB
IQ4_XS
4.5 GB
Q4_K_M
4.9 GB
Q5_K_S
5.5 GB
BF16
15.1 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Zephyr 7B Beta on a Mac?

Zephyr 7B Beta requires at least 3.6 GB at IQ3_XS, 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 Zephyr 7B Beta locally?

Yes — Zephyr 7B Beta can run locally on consumer hardware. At Q4_K_M quantization it needs 4.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Zephyr 7B Beta?

At Q4_K_M, Zephyr 7B Beta can reach ~594 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~133 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: AMD Instinct MI300X5300 ÷ 4.9 × 0.55 = ~594 tok/s

Estimated speed at Q4_K_M (4.9 GB)

~594 tok/s
~133 tok/s
~444 tok/s
~367 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Zephyr 7B Beta?

At Q4_K_M, the download is about 4.35 GB. The full-precision BF16 version is 14.48 GB. The smallest option (IQ3_XS) is 2.99 GB.

Which GPUs can run Zephyr 7B Beta?

35 consumer GPUs can run Zephyr 7B Beta at Q4_K_M (4.9 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.

Which devices can run Zephyr 7B Beta?

33 devices with unified memory can run Zephyr 7B Beta at Q4_K_M (4.9 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.