Qwen3 Coder Next — Hardware Requirements & GPU Compatibility
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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.
912.3K downloads
1.4K likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 79.7B
- Architecture
- Qwen3NextForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-01-30
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3 Coder Next Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| 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 |
| 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 |
| Q4_K_M | 4.80 | 48.2 GB | 61.0 GB | 47.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 57.2 GB | 70.0 GB | 56.77 GB | 5-bit medium quantization — good quality/size tradeoff |
| 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 GBQwen3 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 GB22 devices with unified memory can run Qwen3 Coder Next, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 96GB).
Runs great
— Plenty of headroom
Where to Download Qwen3 Coder Next
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
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 159.8 GB at BF16. 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_M48.2 GBQ4_K_M + full context61.0 GB- 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_XXS22.3 GBIQ3_S34.3 GBQ3_K_L41.2 GBQ4_K_M ★48.2 GBQ4_K_L49.2 GBBF16159.8 GB★ Recommended — best balance of quality and VRAM usage.
- 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 ~91 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 48.2 × 0.65 = ~108 tok/s
Estimated speed at Q4_K_M (48.2 GB)
~108 tok/s~108 tok/s~91 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 Coder Next?
At Q4_K_M, the download is about 47.80 GB. The full-precision BF16 version is 159.35 GB. The smallest option (IQ2_XXS) is 21.91 GB.
- Which GPUs can run Qwen3 Coder Next?
No single consumer GPU has enough VRAM to run Qwen3 Coder Next at Q4_K_M (48.2 GB). Multi-GPU or professional hardware is required.
- Which devices can run Qwen3 Coder Next?
23 devices with unified memory can run Qwen3 Coder Next at Q4_K_M (48.2 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.