p-e-w·Qwen·Qwen3ForCausalLM

Qwen3 4B Instruct 2507 Heretic — Hardware Requirements & GPU Compatibility

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Qwen3 4B Instruct 2507 Heretic is a 4.0B-parameter open language model from p-e-w in the Qwen family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 2.90 GB of VRAM — see which GPUs and Macs can run it below.

3.4K downloads 46 likes262K context

Specifications

Publisher
p-e-w
Family
Qwen
Parameters
4.0B
Architecture
Qwen3ForCausalLM
Context Length
262,144 tokens
Vocabulary Size
151,936
Release Date
2025-11-15
License
Apache 2.0

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How Much VRAM Does Qwen3 4B Instruct 2507 Heretic Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_K3.402.2 GB
Q3_K_S3.502.3 GB
Q3_K_M3.902.5 GB
Q4_04.002.5 GB
Q4_K_M4.802.9 GB
Q5_K_M5.703.4 GB
Q6_K6.603.8 GB
Q8_08.004.5 GB

Which GPUs Can Run Qwen3 4B Instruct 2507 Heretic?

Q4_K_M · 2.9 GB

Qwen3 4B Instruct 2507 Heretic (Q4_K_M) requires 2.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 262K context window can add up to 24.0 GB, bringing total usage to 26.9 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Which Devices Can Run Qwen3 4B Instruct 2507 Heretic?

Q4_K_M · 2.9 GB

33 devices with unified memory can run Qwen3 4B Instruct 2507 Heretic, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.

Related Models

Frequently Asked Questions

How much VRAM does Qwen3 4B Instruct 2507 Heretic need?

Qwen3 4B Instruct 2507 Heretic requires 2.9 GB of VRAM at Q4_K_M, or 4.5 GB at Q8_0. Full 262K context adds up to 24.0 GB (26.9 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 4.0B × 4.8 bits ÷ 8 = 2.4 GB

KV Cache + Overhead 0.5 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 24.5 GB (at full 262K context)

VRAM usage by quantization

2.9 GB
26.9 GB

Learn more about VRAM estimation →

What's the best quantization for Qwen3 4B Instruct 2507 Heretic?

For Qwen3 4B Instruct 2507 Heretic, Q4_K_M (2.9 GB) offers the best balance of quality and VRAM usage. Q4_K_L (3.0 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 1.9 GB.

VRAM requirement by quantization

IQ2_M
1.9 GB
IQ3_M
2.3 GB
Q4_1
2.8 GB
Q4_K_M
2.9 GB
Q4_K_L
3.0 GB
Q8_0
4.5 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Qwen3 4B Instruct 2507 Heretic on a Mac?

Qwen3 4B Instruct 2507 Heretic requires at least 1.9 GB at IQ2_M, 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 Qwen3 4B Instruct 2507 Heretic locally?

Yes — Qwen3 4B Instruct 2507 Heretic can run locally on consumer hardware. At Q4_K_M quantization it needs 2.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Qwen3 4B Instruct 2507 Heretic?

At Q4_K_M, Qwen3 4B Instruct 2507 Heretic can reach ~1005 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~226 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 ÷ 2.9 × 0.55 = ~1005 tok/s

Estimated speed at Q4_K_M (2.9 GB)

~1005 tok/s
~226 tok/s
~751 tok/s
~622 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 Qwen3 4B Instruct 2507 Heretic?

At Q4_K_M, the download is about 2.41 GB. The full-precision Q8_0 version is 4.02 GB. The smallest option (IQ2_M) is 1.36 GB.

Which GPUs can run Qwen3 4B Instruct 2507 Heretic?

35 consumer GPUs can run Qwen3 4B Instruct 2507 Heretic at Q4_K_M (2.9 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 Qwen3 4B Instruct 2507 Heretic?

33 devices with unified memory can run Qwen3 4B Instruct 2507 Heretic at Q4_K_M (2.9 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.