Qwen3 Coder Next FP8 — Hardware Requirements & GPU Compatibility
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Qwen3 Coder Next FP8 is Alibaba's 79.7-billion-parameter code-specialized model served in FP8 precision. As the next-generation coding model in the Qwen3 family, it is trained and tuned specifically for software engineering tasks including code generation, debugging, refactoring, and technical explanation. At nearly 80 billion parameters, this is a substantial model that benefits greatly from FP8 quantization to reduce memory requirements. Users with high-end consumer GPUs or multi-GPU setups will find it delivers strong code completion and generation quality that competes with much larger models, though it does require significant VRAM to run comfortably.
589.7K downloads
113 likesSpecifications
- 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
How Much VRAM Does Qwen3 Coder Next FP8 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 | 30.88 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 | 35.86 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 | 40.84 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 43.2 GB | 56.0 GB | 42.83 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 | 47.81 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 | 57.77 GB | 5-bit large quantization |
| Q6_K | 6.60 | 66.1 GB | 78.9 GB | 65.74 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 80.1 GB | 92.9 GB | 79.68 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 Coder Next FP8?
Q4_K_M · 48.2 GBQwen3 Coder Next FP8 (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 FP8?
Q4_K_M · 48.2 GB8 devices with unified memory can run Qwen3 Coder Next FP8, 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 Coder Next FP8 need?
Qwen3 Coder Next FP8 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_M48.2 GBQ4_K_M + full context61.0 GB- Can NVIDIA GeForce RTX 4090 run Qwen3 Coder Next FP8?
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 FP8?
For Qwen3 Coder Next FP8, 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 GB~53%IQ3_S34.3 GB~75%Q3_K_L41.2 GB~86%Q4_K_M ★48.2 GB~89%Q4_K_L49.2 GB~90%Q8_080.1 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 Coder Next FP8 on a Mac?
Qwen3 Coder Next FP8 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 FP8 locally?
Yes — Qwen3 Coder Next FP8 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 FP8?
At Q4_K_M, Qwen3 Coder Next FP8 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/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 Coder Next FP8?
At Q4_K_M, the download is about 47.81 GB. The full-precision Q8_0 version is 79.68 GB. The smallest option (IQ2_XXS) is 21.91 GB.