perplexity-ai·Qwen3MoeForCausalLM

Browsesafe — Hardware Requirements & GPU Compatibility

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Browsesafe is a 30.5B-parameter open language model from perplexity-ai. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 18.72 GB of VRAM — see which GPUs and Macs can run it below.

277 downloads 44 likes262K context

Specifications

Publisher
perplexity-ai
Parameters
30.5B
Architecture
Qwen3MoeForCausalLM
Context Length
262,144 tokens
Vocabulary Size
151,936
Release Date
2025-11-14
License
MIT

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How Much VRAM Does Browsesafe Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.4013.4 GB
Q3_K_Mest.3.9015.3 GB
Q4_K_Mest.4.8018.7 GB
Q5_K_Mest.5.7022.1 GB
Q6_Kest.6.6025.6 GB
Q8_0est.8.0030.9 GB
BF16est.16.0061.5 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 Browsesafe?

Q4_K_M · 18.7 GB

Browsesafe (Q4_K_M) requires 18.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 25+ GB is recommended. Using the full 262K context window can add up to 12.8 GB, bringing total usage to 31.5 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Which Devices Can Run Browsesafe?

Q4_K_M · 18.7 GB

41 devices with unified memory can run Browsesafe, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).

Runs great

Plenty of headroom

Related Models

Frequently Asked Questions

How much VRAM does Browsesafe need?

Browsesafe requires 18.7 GB of VRAM at Q4_K_M, or 61.5 GB at BF16. Full 262K context adds up to 12.8 GB (31.5 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 30.5B × 4.8 bits ÷ 8 = 18.3 GB

KV Cache + Overhead 0.4 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 13.2 GB (at full 262K context)

VRAM usage by quantization

18.7 GB
31.5 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 4090 run Browsesafe?

Yes, at Q5_K_M (22.1 GB) or lower. Higher quantizations like Q6_K (25.6 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Browsesafe?

For Browsesafe, Q4_K_M (18.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (22.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 13.4 GB.

VRAM requirement by quantization

Q2_K
13.4 GB
Q4_K_M
18.7 GB
Q5_K_M
22.1 GB
Q6_K
25.6 GB
Q8_0
30.9 GB
BF16
61.5 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Browsesafe on a Mac?

Browsesafe requires at least 13.4 GB at Q2_K, 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 Browsesafe locally?

Yes — Browsesafe can run locally on consumer hardware. At Q4_K_M quantization it needs 18.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Browsesafe?

At Q4_K_M, Browsesafe can reach ~235 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~35 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

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

Example: NVIDIA B2008000 ÷ 18.7 × 0.65 = ~278 tok/s

Estimated speed at Q4_K_M (18.7 GB)

~278 tok/s
~35 tok/s
~278 tok/s
~235 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 Browsesafe?

At Q4_K_M, the download is about 18.32 GB. The full-precision BF16 version is 61.06 GB. The smallest option (Q2_K) is 12.98 GB.

Which GPUs can run Browsesafe?

8 consumer GPUs can run Browsesafe at Q4_K_M (18.7 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.

Which devices can run Browsesafe?

41 devices with unified memory can run Browsesafe at Q4_K_M (18.7 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.