Kwaipilot·Qwen3ForCausalLM

KAT Dev — Hardware Requirements & GPU Compatibility

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KAT Dev is a 32.8B-parameter open language model from Kwaipilot. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 20.29 GB of VRAM — see which GPUs and Macs can run it below.

1.0K downloads 215 likes131K context

Specifications

Publisher
Kwaipilot
Parameters
32.8B
Architecture
Qwen3ForCausalLM
Context Length
131,072 tokens
Vocabulary Size
151,936
Release Date
2025-09-15
License
Apache 2.0

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How Much VRAM Does KAT Dev Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.4014.6 GB
Q3_K_Mest.3.9016.6 GB
Q4_K_Mest.4.8020.3 GB
Q5_K_Mest.5.7024.0 GB
Q6_Kest.6.6027.7 GB
Q8_0est.8.0033.4 GB
BF16est.16.0066.2 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 KAT Dev?

Q4_K_M · 20.3 GB

KAT Dev (Q4_K_M) requires 20.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 27+ GB is recommended. Using the full 131K context window can add up to 21.1 GB, bringing total usage to 41.4 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Which Devices Can Run KAT Dev?

Q4_K_M · 20.3 GB

41 devices with unified memory can run KAT Dev, 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 KAT Dev need?

KAT Dev requires 20.3 GB of VRAM at Q4_K_M, or 66.2 GB at BF16. Full 131K context adds up to 21.1 GB (41.4 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 32.8B × 4.8 bits ÷ 8 = 19.7 GB

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

KV Cache + Overhead 21.7 GB (at full 131K context)

VRAM usage by quantization

20.3 GB
41.4 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 4090 run KAT Dev?

Yes, at Q5_K_M (24.0 GB) or lower. Higher quantizations like Q6_K (27.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for KAT Dev?

For KAT Dev, Q4_K_M (20.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (24.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 14.6 GB.

VRAM requirement by quantization

Q2_K
14.6 GB
Q4_K_M
20.3 GB
Q5_K_M
24.0 GB
Q6_K
27.7 GB
Q8_0
33.4 GB
BF16
66.2 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run KAT Dev on a Mac?

KAT Dev requires at least 14.6 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 KAT Dev locally?

Yes — KAT Dev can run locally on consumer hardware. At Q4_K_M quantization it needs 20.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is KAT Dev?

At Q4_K_M, KAT Dev can reach ~217 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~32 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 ÷ 20.3 × 0.65 = ~256 tok/s

Estimated speed at Q4_K_M (20.3 GB)

~256 tok/s
~32 tok/s
~256 tok/s
~217 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 KAT Dev?

At Q4_K_M, the download is about 19.66 GB. The full-precision BF16 version is 65.52 GB. The smallest option (Q2_K) is 13.92 GB.

Which GPUs can run KAT Dev?

7 consumer GPUs can run KAT Dev at Q4_K_M (20.3 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU have plenty of headroom for comfortable inference.

Which devices can run KAT Dev?

41 devices with unified memory can run KAT Dev at Q4_K_M (20.3 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.