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LLM 显存计算器(全部模型)

DeepSeek V4 Pro (1.6T MoE) 显存需求:需要多少 VRAM?

DeepSeek V4 Pro (1.6T MoE) 共 1.6T 参数(MoE,每 token 激活 50B)。以Q4_K_M (GGUF) 量化在 16K 上下文下运行约需 983 GB 显存(权重 909 GB + KV 缓存 1.1 GB + 运行时开销),最低可行硬件约为 6× B200 192GB。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 / BF162.91 TB3.14 TB19× B200 192GB
FP81.46 TB1.57 TB10× B200 192GB
Q8_0 (GGUF)1.54 TB1.67 TB10× B200 192GB
Q6_K (GGUF)1.19 TB1.29 TB8× B200 192GB
Q5_K_M (GGUF)1.03 TB1.12 TB7× B200 192GB
MXFP4790 GB854 GB5× B200 192GB
NVFP4790 GB854 GB5× B200 192GB
Q4_K_M (GGUF)推荐909 GB983 GB6× B200 192GB
Q3_K_M (GGUF)700 GB757 GB5× B200 192GB
Q2_K (GGUF)522 GB564 GB8× 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
4K0.3 GB982 GB
16K1.1 GB983 GB
64K4.3 GB986 GB
128K8.6 GB990 GB
256K17.2 GB999 GB
1M65.4 GB1.02 TB

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.

1.6T total params · 50B active (MoE) · max 1M context · specs partially estimated

Recommended balance · ~0.61 bytes/param

Estimated memory needed

990 GB

Model weights909 GB
KV cache (131,072 tokens)8.6 GB
Runtime overhead72.7 GB

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

Will it fit?

RTX 3060 12GB90× needed
RTX 4060 Ti 16GB68× needed
RTX 3090 / 4090 24GB45× needed
RTX 5090 32GB34× needed
A100 40GB27× needed
A100 / H100 80GB14× needed
H200 141GB8× needed
AMD MI300X 192GB6× needed
B200 192GB6× needed
Mac 24GB unified✗ too big
Mac 36GB unified✗ too big
Mac 64GB unified✗ too big
Mac 128GB unified✗ too big
Mac M3 Ultra 512GB✗ too big

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 DeepSeek V4 Pro (1.6T MoE) 需要多少显存?

以推荐的 Q4_K_M (GGUF) 量化、16K 上下文估算约 983 GB:权重 909 GB、KV 缓存 1.1 GB,另加运行时开销。上下文越长 KV 缓存越大。

DeepSeek V4 Pro (1.6T MoE) 最低需要什么硬件?

约为 6× B200 192GB(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。

DeepSeek V4 Pro (1.6T MoE) 能在 Mac 上跑吗?

很难。即使 Q4_K_M (GGUF) 量化也需要约 983 GB,超过了目前最大的 Mac M3 Ultra 512GB。建议使用多卡服务器或直接调 API。

DeepSeek V4 Pro (1.6T 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