Can NVIDIA GeForce RTX 4090 run Qwen3 Coder Next?

Yes, at IQ2_XXS (22.3 GB) or lower. Higher quantizations like IQ2_XS (24.3 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Qwen3 Coder Next?

For Qwen3 Coder Next, Q4_K_M (48.2 GB) offers the best balance of quality and VRAM usage. Q4_K_L (49.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 22.3 GB.

Can I run Qwen3 Coder Next on a Mac?

Qwen3 Coder Next requires at least 22.3 GB at IQ2_XXS, 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 Next locally?

Yes — Qwen3 Coder Next can run locally on consumer hardware. At Q4_K_M quantization it needs 48.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Qwen3 Coder Next?

At Q4_K_M, Qwen3 Coder Next can reach ~61 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

What's the download size of Qwen3 Coder Next?

At Q4_K_M, the download is about 47.80 GB. The full-precision Q8_0 version is 79.67 GB. The smallest option (IQ2_XXS) is 21.91 GB.

Alibaba·Qwen·Qwen3NextForCausalLM

Qwen3 Coder Next — Hardware Requirements & GPU Compatibility

ChatCode

Qwen3 Coder Next is a 79.7-billion parameter code-specialized instruction-tuned model from Alibaba Cloud, the next generation of the Qwen Coder series. It is trained extensively on source code and programming-related data, delivering strong performance across code generation, completion, debugging, refactoring, and software engineering dialogue. The model represents a significant step up in coding capability within the Qwen family. Due to its large parameter count, running Qwen3 Coder Next locally requires substantial VRAM, typically 48GB or more at reduced precision, placing it in the territory of professional GPUs or multi-GPU consumer setups. It is a top-tier choice for developers who need the most capable local coding assistant available. Released under the Apache 2.0 license.

1.1M downloads 1.1K likesFeb 2026262K context

Specifications

Publisher: Alibaba
Family: Qwen
Parameters: 79.7B
Architecture: Qwen3NextForCausalLM
Context Length: 262,144 tokens
Vocabulary Size: 151,936
Release Date: 2026-02-03
License: Apache 2.0

Get Started

HuggingFace

Qwen/Qwen3-Coder-Next

How Much VRAM Does Qwen3 Coder Next Need?

Select a quantization to see compatible GPUs below.

Quantization	Bits	VRAM	+ Context	File Size	Quality
IQ2_XXS	2.20	22.3 GB	35.1 GB	21.91 GB	Importance-weighted 2-bit, extreme compression — significant quality loss
IQ2_XS	2.40	24.3 GB	37.1 GB	23.90 GB	Importance-weighted 2-bit, extra small
IQ2_S	2.50	25.3 GB	38.1 GB	24.90 GB	Importance-weighted 2-bit, small
IQ2_M	2.70	27.3 GB	40.1 GB	26.89 GB	Importance-weighted 2-bit, medium
IQ3_XXS	3.10	31.3 GB	44.1 GB	30.87 GB	Importance-weighted 3-bit
IQ3_XS	3.30	33.3 GB	46.0 GB	32.87 GB	Importance-weighted 3-bit, extra small
IQ3_S	3.40	34.3 GB	47.0 GB	33.86 GB	Importance-weighted 3-bit, small
Q2_K	3.40	34.3 GB	47.0 GB	33.86 GB	2-bit quantization with K-quant improvements
Q3_K_S	3.50	35.3 GB	48.0 GB	34.86 GB	3-bit small quantization
IQ3_M	3.60	36.3 GB	49.0 GB	35.85 GB	Importance-weighted 3-bit, medium
Q3_K_M	3.90	39.2 GB	52.0 GB	38.84 GB	3-bit medium quantization
Q4_0	4.00	40.2 GB	53.0 GB	39.84 GB	4-bit legacy quantization
Q3_K_L	4.10	41.2 GB	54.0 GB	40.83 GB	3-bit large quantization
IQ4_XS	4.30	43.2 GB	56.0 GB	42.82 GB	Importance-weighted 4-bit, compact
Q4_1	4.50	45.2 GB	58 GB	44.82 GB	4-bit legacy quantization with offset
Q4_K_S	4.50	45.2 GB	58 GB	44.82 GB	4-bit small quantization
IQ4_NL	4.50	45.2 GB	58 GB	44.82 GB	Importance-weighted 4-bit, non-linear
Q4_K_M	4.80	48.2 GB	61.0 GB	47.80 GB	4-bit medium quantization — most popular sweet spot
Q4_K_L	4.90	49.2 GB	62.0 GB	48.80 GB	4-bit large quantization
Q5_K_S	5.50	55.2 GB	68.0 GB	54.78 GB	5-bit small quantization
Q5_K_M	5.70	57.2 GB	70.0 GB	56.77 GB	5-bit medium quantization — good quality/size tradeoff
Q5_K_L	5.80	58.2 GB	71.0 GB	57.76 GB	5-bit large quantization
Q6_K	6.60	66.1 GB	78.9 GB	65.73 GB	6-bit quantization, very good quality
Q8_0	8.00	80.1 GB	92.9 GB	79.67 GB	8-bit quantization, near-lossless

Which GPUs Can Run Qwen3 Coder Next?

Q4_K_M · 48.2 GB

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Qwen3 Coder Next (Q4_K_M) requires 48.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 63+ GB is recommended. Using the full 262K context window can add up to 12.8 GB, bringing total usage to 61.0 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.

Which Devices Can Run Qwen3 Coder Next?

Q4_K_M · 48.2 GB

8 devices with unified memory can run Qwen3 Coder Next, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).

Runs great

— Plenty of headroom

NVIDIA DGX H100~361 tok/s NVIDIA DGX A100 640GB~220 tok/s Mac Pro M2 Ultra (192 GB)~11 tok/s Mac Studio M2 Ultra (192 GB)~11 tok/s Mac Studio M4 Max (128 GB)~7 tok/s

Decent

— Enough memory, may be tight

Mac Studio M4 Max (64 GB)~7 tok/s MacBook Pro 16" M4 Max (64 GB)~7 tok/s NVIDIA Jetson AGX Orin 64GB~3 tok/s

Related Models

Derivatives (2)

Qwen3 Coder Next FP8 Dynamic Qwen3 Coder Next Int4 AutoRound

Frequently Asked Questions

How much VRAM does Qwen3 Coder Next need?: Qwen3 Coder Next requires 48.2 GB of VRAM at Q4_K_M, or 80.1 GB at Q8_0. Full 262K context adds up to 12.8 GB (61.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 79.7B × 4.8 bits ÷ 8 = 47.8 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
Q4_K_M
48.2 GB
Q4_K_M + full context
61.0 GB
Learn more about VRAM estimation →
Can NVIDIA GeForce RTX 4090 run Qwen3 Coder Next?: Yes, at IQ2_XXS (22.3 GB) or lower. Higher quantizations like IQ2_XS (24.3 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Qwen3 Coder Next?: For Qwen3 Coder Next, Q4_K_M (48.2 GB) offers the best balance of quality and VRAM usage. Q4_K_L (49.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 22.3 GB.
VRAM requirement by quantization
IQ2_XXS
22.3 GB~53%
IQ3_S
34.3 GB~75%
Q3_K_L
41.2 GB~86%
Q4_K_M ★
48.2 GB~89%
Q4_K_L
49.2 GB~90%
Q8_0
80.1 GB~99%
★ Recommended — best balance of quality and VRAM usage.
Learn more about quantization →
Can I run Qwen3 Coder Next on a Mac?: Qwen3 Coder Next requires at least 22.3 GB at IQ2_XXS, 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 Next locally?: Yes — Qwen3 Coder Next can run locally on consumer hardware. At Q4_K_M quantization it needs 48.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3 Coder Next?: At Q4_K_M, Qwen3 Coder Next can reach ~61 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 MI300X → 5300 ÷ 48.2 × 0.55 = ~61 tok/s
Estimated speed at Q4_K_M (48.2 GB)
AMD Instinct MI300X
~61 tok/s
NVIDIA H100 SXM
~45 tok/s
AMD Instinct MI250X
~37 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 Next?: At Q4_K_M, the download is about 47.80 GB. The full-precision Q8_0 version is 79.67 GB. The smallest option (IQ2_XXS) is 21.91 GB.