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How much VRAM does Kimi K3 2.8T (MoE) need at Q4_K_M? Moonshot 1M frontier

By TechCompare · Updated · How we check

Kimi K3 2.8T at Q4_K_M is a 1781 GB resident deployment for 1M context, and the 1568 GB of weights are most of it.

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Estimated VRAM required

1781 GB

2800B params at Q4_K_M, 1,048,576 token context, batch 1, inference.

Weights
1568 GB
KV cache
51.5 GB
Overhead
162 GB

Estimate accuracy: Weights within ~2%. KV cache within ~5% for standard GQA models, ~10% for MLA or hybrid state estimates. Real VRAM may vary with framework (vLLM vs llama.cpp vs Transformers), Flash Attention, and driver overhead.

Hardware that fits

Apple entries show installed unified memory separately from a model budget based on an 8 GB reserve. That budget is a planning heuristic, not a fixed hardware limit.

No single GPU in our catalog has enough memory. Multi-GPU or CPU offload required.

Same 2800B @ Q4_K_M, 1,048,576 context scenario, pre-loaded. Change the quant, shrink the context, or try a different preset.
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Kimi K3 2.8T at Q4_K_M with native 1M context needs about 1781 GB of VRAM for an all-resident deployment. Moonshot's third-generation trillion-parameter MoE activates 104B parameters per token from a 2.8 trillion total pool. That doesn't shrink what has to stay loaded, because different tokens pick different experts. The hybrid KDA and Gated-MLA attention keeps the key-value cache to 52 GB at full context.

How this is calculated

The 2.8T parameter pool requires 1568 GB of weights at Q4_K_M regardless of expert routing. Kimi K3's hybrid design uses 69 Kimi Delta Attention layers that store no key-value cache at all, plus 24 Gated-MLA layers that compress per-token KV to a 512-dim latent.

Only the 24 MLA layers contribute to the cache, which lands at 52 GB at the full 1,048,576 token window. Activation and software overhead adds about 162 GB. The cache depends only on context length, not on which experts a token picks.

Verdict

An all-resident Kimi K3 deployment is multi-node datacenter territory: 23 H100 cards of 80 GB or 13 H200 cards of 141 GB, with NVLink. The KDA plus MLA hybrid keeps the cache to 52 GB at 1M context, so the 1568 GB of weights are most of the bill.

More Kimi scenarios

Kimi K3.1 Pro at Q4_K_M
A 2.78T expert pool with 24 latent attention layers and fixed state in the other 69.
View details
Mistral Large 4 at Q4_K_M
A 1T expert pool with 63 latent cache owners and 44 layers that also store an index key.
View details
Muse Spark Open at Q4_K_M
Only 16 of its 62 layers grow with context, and the other 46 hold a 2,048 token window.
View details

Related guides

Kimi K3.1 Pro VRAM
Size the post-trained K3.1 Pro the same way.
View guide
kimi k3 pricing
Per-token cost for the kimi k3 family this VRAM entry serves.
View guide
BPE tokenization explained
How tokenizers chunk text into the units VRAM serves.
View guide

Frequently asked questions

Why is Kimi K3's key-value cache so small for a 2.8T model?
Kimi K3 uses 69 Kimi Delta Attention layers that store no key-value cache at all, plus 24 Gated-MLA layers that compress per-token KV to a 512-dim latent. Only the 24 MLA layers contribute to the cache. At 1M context the cache is about 52 GB, far smaller than a comparable all-attention model of the same layer count.
Can I run Kimi K3 on a single GPU?
No. Even a single H200 141GB is far short of the roughly 1781 GB resident total. The 104B active count doesn't lower that, because different tokens pick different experts.
Can I run Kimi K3 2.8T on a single GPU?
No. The Q4 weights alone are roughly 1568 GB. Moving expert weights to system RAM would need a host that can hold most of that, and every token would get slower. Kimi K3's compressed MLA key-value cache helps with long context, but the parameter pool is still too large for consumer GPUs.
What is MLA and why does Kimi K3's KV cache matter?
Multi-head Latent Attention compresses the KV cache by projecting key and value vectors into a smaller latent space before storing them, which cuts the VRAM cost of long context. It's the same family of trick DeepSeek uses, and it's Kimi K3's main memory story. The weight pool still dominates the bill at 2.8T total parameters, but MLA is what keeps the long-context cache small.

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