Qwen3.8 2.4T A95B — Hardware Requirements & GPU Compatibility
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Qwen3.8 2.4T A95B is a 2446.2B-parameter open language model from Alibaba in the Qwen 3.8 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 1468.40 GB of VRAM — see which GPUs and Macs can run it below.
50.7K downloads
1.2K likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 3.8
- Parameters
- 2446.2B
- Architecture
- Qwen3_5MoeForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-08-08
- License
- Other
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HuggingFace
How Much VRAM Does Qwen3.8 2.4T A95B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 1040.3 GB | 1089.3 GB | 1039.63 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 1193.2 GB | 1242.2 GB | 1192.51 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 1468.4 GB | 1517.4 GB | 1467.71 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 1743.6 GB | 1792.6 GB | 1742.91 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 2018.8 GB | 2067.8 GB | 2018.10 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 2446.9 GB | 2495.9 GB | 2446.18 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Qwen3.8 2.4T A95B?
Q4_K_M · 1468.4 GBQwen3.8 2.4T A95B (Q4_K_M) requires 1468.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1909+ GB is recommended. Using the full 262K context window can add up to 49.0 GB, bringing total usage to 1517.4 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Where to Download Qwen3.8 2.4T A95B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does Qwen3.8 2.4T A95B need?
Qwen3.8 2.4T A95B requires 1468.4 GB of VRAM at Q4_K_M, or 4893.1 GB at BF16. Full 262K context adds up to 49.0 GB (1517.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 2446.2B × 4.8 bits ÷ 8 = 1467.7 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 49.7 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M1468.4 GBQ4_K_M + full context1517.4 GB- Can NVIDIA GeForce RTX 5090 run Qwen3.8 2.4T A95B?
No — Qwen3.8 2.4T A95B requires at least 673.4 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Qwen3.8 2.4T A95B?
For Qwen3.8 2.4T A95B, Q4_K_M (1468.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1743.6 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 673.4 GB.
VRAM requirement by quantization
IQ2_XXS673.4 GBQ2_K1040.3 GBIQ4_XS1315.5 GBQ4_K_M ★1468.4 GBQ6_K2018.8 GBBF164893.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.8 2.4T A95B on a Mac?
Qwen3.8 2.4T A95B requires at least 673.4 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 Qwen3.8 2.4T A95B locally?
Yes — Qwen3.8 2.4T A95B can run locally on consumer hardware. At Q4_K_M quantization it needs 1468.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- What's the download size of Qwen3.8 2.4T A95B?
At Q4_K_M, the download is about 1467.71 GB. The full-precision BF16 version is 4892.37 GB. The smallest option (IQ2_XXS) is 672.70 GB.
- Which GPUs can run Qwen3.8 2.4T A95B?
No single consumer GPU has enough VRAM to run Qwen3.8 2.4T A95B at Q4_K_M (1468.4 GB). Multi-GPU or professional hardware is required.
- Which devices can run Qwen3.8 2.4T A95B?
Qwen3.8 2.4T A95B requires at least 1468.4 GB at Q4_K_M, which exceeds the unified memory of most consumer devices. A high-memory Mac Studio, Mac Pro, or multi-GPU desktop setup is recommended.