deepcogito·Qwen·Qwen2ForCausalLM

Cogito V1 Preview Qwen 32B — Hardware Requirements & GPU Compatibility

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Specifications

Publisher
deepcogito
Family
Qwen
Parameters
32B
Architecture
Qwen2ForCausalLM
Context Length
131,072 tokens
Vocabulary Size
151,665
Release Date
2025-04-08
License
Apache 2.0

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How Much VRAM Does Cogito V1 Preview Qwen 32B Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_K3.4014.4 GB
Q3_K_S3.5014.8 GB
Q3_K_M3.9016.4 GB
Q4_04.0016.8 GB
Q4_K_M4.8020.0 GB
Q5_K_M5.7023.6 GB
Q6_K6.6027.2 GB
Q8_08.0032.8 GB

Which GPUs Can Run Cogito V1 Preview Qwen 32B?

Q4_K_M · 20.0 GB

Cogito V1 Preview Qwen 32B (Q4_K_M) requires 20.0 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 33.8 GB, bringing total usage to 53.9 GB. 5 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Which Devices Can Run Cogito V1 Preview Qwen 32B?

Q4_K_M · 20.0 GB

21 devices with unified memory can run Cogito V1 Preview Qwen 32B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).

Related Models

Frequently Asked Questions

How much VRAM does Cogito V1 Preview Qwen 32B need?

Cogito V1 Preview Qwen 32B requires 20.0 GB of VRAM at Q4_K_M, or 32.8 GB at Q8_0. Full 131K context adds up to 33.8 GB (53.9 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 32B × 4.8 bits ÷ 8 = 19.2 GB

KV Cache + Overhead 0.8 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 34.7 GB (at full 131K context)

VRAM usage by quantization

20.0 GB
53.9 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 4090 run Cogito V1 Preview Qwen 32B?

Yes, at Q5_K_M (23.6 GB) or lower. Higher quantizations like Q5_K_L (24.0 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Cogito V1 Preview Qwen 32B?

For Cogito V1 Preview Qwen 32B, Q4_K_M (20.0 GB) offers the best balance of quality and VRAM usage. Q4_K_L (20.4 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 10.4 GB.

VRAM requirement by quantization

IQ2_XS
10.4 GB
Q2_K
14.4 GB
IQ4_XS
18.0 GB
Q4_K_M
20.0 GB
Q4_K_L
20.4 GB
Q8_0
32.8 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Cogito V1 Preview Qwen 32B on a Mac?

Cogito V1 Preview Qwen 32B requires at least 10.4 GB at IQ2_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 Cogito V1 Preview Qwen 32B locally?

Yes — Cogito V1 Preview Qwen 32B can run locally on consumer hardware. At Q4_K_M quantization it needs 20.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Cogito V1 Preview Qwen 32B?

At Q4_K_M, Cogito V1 Preview Qwen 32B can reach ~146 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~33 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 ÷ 20.0 × 0.55 = ~146 tok/s

Estimated speed at Q4_K_M (20.0 GB)

~146 tok/s
~33 tok/s
~109 tok/s
~90 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 Cogito V1 Preview Qwen 32B?

At Q4_K_M, the download is about 19.20 GB. The full-precision Q8_0 version is 32.00 GB. The smallest option (IQ2_XS) is 9.60 GB.

Which GPUs can run Cogito V1 Preview Qwen 32B?

5 consumer GPUs can run Cogito V1 Preview Qwen 32B at Q4_K_M (20.0 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 Cogito V1 Preview Qwen 32B?

21 devices with unified memory can run Cogito V1 Preview Qwen 32B at Q4_K_M (20.0 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.