Qwen3 Next 80B A3B Instruct GGUF — Hardware Requirements & GPU Compatibility
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37.6K downloads
172 likesSpecifications
- Publisher
- Unsloth
- Family
- Qwen
- Parameters
- 80B
- Architecture
- Qwen3NextForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-01-14
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Qwen3 Next 80B A3B Instruct GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 34.4 GB | 47.2 GB | 34.00 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 35.4 GB | 48.2 GB | 35.00 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 39.4 GB | 52.2 GB | 39.00 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 40.4 GB | 53.2 GB | 40.00 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 48.4 GB | 61.2 GB | 48.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 57.4 GB | 70.2 GB | 57.00 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 66.4 GB | 79.2 GB | 66.00 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 80.4 GB | 93.2 GB | 80.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 Next 80B A3B Instruct GGUF?
Q4_K_M · 48.4 GBQwen3 Next 80B A3B Instruct GGUF (Q4_K_M) requires 48.4 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 61.2 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Qwen3 Next 80B A3B Instruct GGUF?
Q4_K_M · 48.4 GB8 devices with unified memory can run Qwen3 Next 80B A3B Instruct GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3 Next 80B A3B Instruct GGUF need?
Qwen3 Next 80B A3B Instruct GGUF requires 48.4 GB of VRAM at Q4_K_M, or 80.4 GB at Q8_0. Full 262K context adds up to 12.8 GB (61.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 80B × 4.8 bits ÷ 8 = 48 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_M48.4 GBQ4_K_M + full context61.2 GB- Can NVIDIA GeForce RTX 4090 run Qwen3 Next 80B A3B Instruct GGUF?
Yes, at IQ2_XXS (22.4 GB) or lower. Higher quantizations like IQ3_XXS (31.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3 Next 80B A3B Instruct GGUF?
For Qwen3 Next 80B A3B Instruct GGUF, Q4_K_M (48.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (55.4 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 22.4 GB.
VRAM requirement by quantization
IQ2_XXS22.4 GB~53%Q3_K_M39.4 GB~83%Q4_145.4 GB~88%Q4_K_M ★48.4 GB~89%Q5_K_S55.4 GB~92%Q8_080.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 Next 80B A3B Instruct GGUF on a Mac?
Qwen3 Next 80B A3B Instruct GGUF requires at least 22.4 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 Next 80B A3B Instruct GGUF locally?
Yes — Qwen3 Next 80B A3B Instruct GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 48.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 Next 80B A3B Instruct GGUF?
At Q4_K_M, Qwen3 Next 80B A3B Instruct GGUF can reach ~60 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 ÷ 48.4 × 0.55 = ~60 tok/s
Estimated speed at Q4_K_M (48.4 GB)
AMD Instinct MI300X~60 tok/sNVIDIA H100 SXM~45 tok/sAMD Instinct MI250X~37 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 Next 80B A3B Instruct GGUF?
At Q4_K_M, the download is about 48.00 GB. The full-precision Q8_0 version is 80.00 GB. The smallest option (IQ2_XXS) is 22.00 GB.
- Which GPUs can run Qwen3 Next 80B A3B Instruct GGUF?
No single consumer GPU has enough VRAM to run Qwen3 Next 80B A3B Instruct GGUF at Q4_K_M (48.4 GB). Multi-GPU or professional hardware is required.
- Which devices can run Qwen3 Next 80B A3B Instruct GGUF?
8 devices with unified memory can run Qwen3 Next 80B A3B Instruct GGUF at Q4_K_M (48.4 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB), Mac Studio M4 Max (64 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.