NVIDIA·Qwen·Qwen3MoeForCausalLM

Qwen3 Coder 480B A35B Instruct NVFP4 — Hardware Requirements & GPU Compatibility

ChatCode
1.7K downloads 8 likes262K context

Specifications

Publisher
NVIDIA
Family
Qwen
Parameters
241.0B
Architecture
Qwen3MoeForCausalLM
Context Length
262,144 tokens
Vocabulary Size
151,936
Release Date
2026-02-05
License
Apache 2.0

Get Started

How Much VRAM Does Qwen3 Coder 480B A35B Instruct NVFP4 Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_K3.40103 GB
Q3_K_S3.50106.0 GB
Q3_K_M3.90118.1 GB
Q4_04.00121.1 GB
Q4_K_M4.80145.2 GB
Q5_K_M5.70172.3 GB
Q6_K6.60199.4 GB
Q8_08.00241.6 GB

Which GPUs Can Run Qwen3 Coder 480B A35B Instruct NVFP4?

Q4_K_M · 145.2 GB

Qwen3 Coder 480B A35B Instruct NVFP4 (Q4_K_M) requires 145.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 189+ GB is recommended. Using the full 262K context window can add up to 33.0 GB, bringing total usage to 178.2 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.

Which Devices Can Run Qwen3 Coder 480B A35B Instruct NVFP4?

Q4_K_M · 145.2 GB

4 devices with unified memory can run Qwen3 Coder 480B A35B Instruct NVFP4, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Pro M2 Ultra (192 GB).

Related Models

Frequently Asked Questions

How much VRAM does Qwen3 Coder 480B A35B Instruct NVFP4 need?

Qwen3 Coder 480B A35B Instruct NVFP4 requires 145.2 GB of VRAM at Q4_K_M, or 241.6 GB at Q8_0. Full 262K context adds up to 33.0 GB (178.2 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 241.0B × 4.8 bits ÷ 8 = 144.6 GB

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

KV Cache + Overhead 33.6 GB (at full 262K context)

VRAM usage by quantization

145.2 GB
178.2 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run Qwen3 Coder 480B A35B Instruct NVFP4?

No — Qwen3 Coder 480B A35B Instruct NVFP4 requires at least 72.9 GB at IQ2_XS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.

What's the best quantization for Qwen3 Coder 480B A35B Instruct NVFP4?

For Qwen3 Coder 480B A35B Instruct NVFP4, Q4_K_M (145.2 GB) offers the best balance of quality and VRAM usage. Q5_K_S (166.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 72.9 GB.

VRAM requirement by quantization

IQ2_XS
72.9 GB
Q3_K_S
106.0 GB
Q3_K_L
124.1 GB
Q4_K_M
145.2 GB
Q5_K_S
166.3 GB
Q8_0
241.6 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Qwen3 Coder 480B A35B Instruct NVFP4 on a Mac?

Qwen3 Coder 480B A35B Instruct NVFP4 requires at least 72.9 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 Qwen3 Coder 480B A35B Instruct NVFP4 locally?

Yes — Qwen3 Coder 480B A35B Instruct NVFP4 can run locally on consumer hardware. At Q4_K_M quantization it needs 145.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Qwen3 Coder 480B A35B Instruct NVFP4?

At Q4_K_M, Qwen3 Coder 480B A35B Instruct NVFP4 can reach ~20 tok/s on AMD Instinct MI300X. 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 ÷ 145.2 × 0.55 = ~20 tok/s

Estimated speed at Q4_K_M (145.2 GB)

~20 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 Coder 480B A35B Instruct NVFP4?

At Q4_K_M, the download is about 144.63 GB. The full-precision Q8_0 version is 241.04 GB. The smallest option (IQ2_XS) is 72.31 GB.

Which GPUs can run Qwen3 Coder 480B A35B Instruct NVFP4?

No single consumer GPU has enough VRAM to run Qwen3 Coder 480B A35B Instruct NVFP4 at Q4_K_M (145.2 GB). Multi-GPU or professional hardware is required.

Which devices can run Qwen3 Coder 480B A35B Instruct NVFP4?

4 devices with unified memory can run Qwen3 Coder 480B A35B Instruct NVFP4 at Q4_K_M (145.2 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.