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Qwen3.8-Flash-Next (180B MoE) 显存需求:需要多少 VRAM?

Qwen3.8-Flash-Next (180B MoE) 共 180B 参数(MoE,每 token 激活 6B)。以Q4_K_M (GGUF) 量化在 16K 上下文下运行约需 111 GB 显存(权重 102 GB + KV 缓存 0.4 GB + 运行时开销),最低可行硬件约为 Mac 128GB unified。以下数字为规划级估算,数据核实于 2026-09-05。

⚠ 该模型部分架构参数为估算值,结果仅供规划参考。

各量化档位显存需求(16K 上下文)

量化权重总需求最低可行硬件
FP16 / BF16335 GB362 GBMac M3 Ultra 512GB
FP8168 GB181 GBMac M3 Ultra 512GB
Q8_0 (GGUF)178 GB192 GBMac M3 Ultra 512GB
Q6_K (GGUF)137 GB149 GBAMD MI300X 192GB
Q5_K_M (GGUF)119 GB129 GBH200 141GB
MXFP488.8 GB96.3 GBMac 128GB unified
NVFP488.8 GB96.3 GBMac 128GB unified
Q4_K_M (GGUF)推荐102 GB111 GBMac 128GB unified
Q3_K_M (GGUF)78.8 GB85.5 GBMac 128GB unified
Q2_K (GGUF)58.7 GB63.7 GBA100 / H100 80GB

上下文长度对显存的影响(Q4_K_M (GGUF))

上下文KV 缓存总需求
4K0.1 GB111 GB
16K0.4 GB111 GB
64K1.5 GB112 GB
128K3.0 GB113 GB
256K6.0 GB116 GB

KV 缓存随上下文线性增长;开启 FP8 KV cache 可再减半(下方计算器可试)。

自定义估算

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LLM VRAM Calculator — Can I Run It?

Estimate how much GPU memory (VRAM) you need to run open-weight LLMs like Kimi K3, DeepSeek R1, Qwen3, or Llama locally. Pick a model, quantization, and context length — the calculator adds up weights, KV cache, and runtime overhead, then shows which GPUs or Macs can fit it. Runs entirely in your browser: no upload, no signup.

180B total params · 6B active (MoE) · max 256K context · specs partially estimated

Recommended balance · ~0.61 bytes/param

Estimated memory needed

113 GB

Model weights102 GB
KV cache (131,072 tokens)3.0 GB
Runtime overhead8.2 GB

Architecture details for this model are estimated — treat results as a ballpark.

Will it fit?

RTX 3060 12GB11× needed
RTX 4060 Ti 16GB8× needed
RTX 3090 / 4090 24GB6× needed
RTX 5090 32GB4× needed
A100 40GB4× needed
A100 / H100 80GB2× needed
H200 141GB✓ fits
AMD MI300X 192GB✓ fits
B200 192GB✓ fits
Mac 24GB unified✗ too big
Mac 36GB unified✗ too big
Mac 64GB unified✗ too big
Mac 128GB unified✓ fits
Mac M3 Ultra 512GB✓ fits

Assumes ~92% of device memory is usable. Multi-GPU counts are for tensor/pipeline parallel serving (vLLM, SGLang); Apple Silicon uses unified memory via llama.cpp or MLX.

FAQ

运行 Qwen3.8-Flash-Next (180B MoE) 需要多少显存?

以推荐的 Q4_K_M (GGUF) 量化、16K 上下文估算约 111 GB:权重 102 GB、KV 缓存 0.4 GB,另加运行时开销。上下文越长 KV 缓存越大。

Qwen3.8-Flash-Next (180B MoE) 最低需要什么硬件?

约为 Mac 128GB unified(按 92% 可用显存估算)。更低配置可尝试更激进的量化(如 Q3/Q2),但质量损失明显。

Qwen3.8-Flash-Next (180B MoE) 能在 Mac 上跑吗?

可以。在 Q4_K_M (GGUF) 量化、16K 上下文下约需 111 GB,Mac M3 Ultra 512GB 的统一内存足够(经 llama.cpp 或 MLX)。

Qwen3.8-Flash-Next (180B MoE) 用什么量化格式最合适?

Q4_K_M 是体积与质量的推荐平衡点;显存充裕可用 Q6_K 或 Q8_0 减少质量损失。

其他模型的显存需求