Kimi K3 VRAM Requirements (2026)

Kimi K3 needs about 1.4 TB of memory at the smallest quantization worth running, and 474 GB at the smallest file that exists. We measured those numbers ourselves from the community GGUF repository on September 3, 2026 — they are not the output of a sizing formula. No Mac and no single GPU holds either figure. This guide gives the real file sizes, the machine class each tier actually implies, and the smaller sparse models that deliver most of the workload on hardware you can buy.

TL;DR: Plan on about 1.4 TB for a Q4-class Kimi K3 build — an 8x MI350X 288GB node, or a dual-node 1TB EPYC server. A maxed-out 512GB Mac Studio almost loads the 474 GB ternary quant, but almost is not a fit, and we do not recommend 1-bit builds anyway. For everyone else, ollama run kimi-k3:cloud streams it, and GPT-OSS 120B is the strongest MoE that genuinely runs on one machine.

What is Kimi K3, exactly?

Kimi K3 is a 2.8-trillion-parameter mixture-of-experts model from Moonshot AI: 104B parameters active per token, 93 layers, and 896 experts of which 16 fire at a time, per the official Kimi K3 model card. It reads text, images and video natively, carries a 1-million-token context window, and ships under a custom Kimi K3 License rather than MIT or Apache.

Two details shape the memory story more than the headline size. First, the official checkpoint is already quantized: Moonshot trained with MXFP4 weights, so the 96 safetensors shards total about 1.56 TB and there is no FP16 release to compress later. Second, the 104B active figure means decode speed is far better than 2.8T suggests. Capacity is the whole problem — you are not buying speed, you are buying somewhere to put the weights.

How much VRAM does Kimi K3 need at each quant?

Between 474 GB and 1,454 GB depending on the build. We measured the unsloth/Kimi-K3-GGUF repository directly on September 3, 2026, summing the file sizes per quant, because sizing guides that print one tidy Q4 number for this model are extrapolating a bytes-per-parameter formula. These are the files that actually exist:

Notice what is missing: there is no plain Q4_K_M at all. The ecosystem only ships extreme quants for K3 because the target hardware is extreme. Our standing rule applies more here than anywhere — below Q3, a model this sparse degrades fast, and a wrong answer from a 2.8T model is still wrong.

Context is the one bright spot. Kimi Delta Attention is a linear-attention variant, so the KV cache per token is a fraction of a dense model's. Even a million-token session adds little next to 1.4 TB of weights.

Is any Mac or GPU enough for Kimi K3?

No single machine you can order today holds Kimi K3 at a usable quant. Consider the largest unified-memory machine Apple sells: the Mac Studio M5 Ultra 512GB exposes roughly 435-476 GB of usable memory to the GPU — and the smallest K3 file is 474 GB. That brushes the ceiling with zero room left for context, macOS, or the slightest overhead.

We call that a no, not a yes. A model that loads and then cannot hold a conversation is not running. The gap to comfort is large: the smallest build we recommend (1,405 GB) needs about three times what the biggest Mac offers.

What does it actually take to run Kimi K3?

The practical floor is a used dual-socket EPYC server with 1TB of DDR4, which holds the 802 GB Q2_K_XL build and costs about $7,300 in parts. Our Kimi K3 build guide prices that machine part by part and tracks the llama.cpp support status, which has been the other moving piece of this story since July.

Comfortable Q4-class operation is a different budget: about 1.5 TB of fast memory, which means an 8x AMD Instinct MI350X 288GB node (2.3 TB total) or a dual-node EPYC cluster linked over Thunderbolt or InfiniBand. That is inference-lab money, and it buys tens of tokens per second at best. So why do sizing guides still end with a purchase recommendation for this model? Because a formula that divides parameters by bits does not know what a datacenter looks like.

What is the fastest way to use Kimi K3 today?

The hosted API, full stop. Moonshot serves K3 through kimi.com and its developer API, and we verified the Ollama Cloud tag against the registry while writing this: ollama run kimi-k3:cloud streams the model from Ollama's backend instead of loading anything locally.

For agentic coding at this quality tier, the API is the rational default in 2026. The compute to self-host K3 costs more than years of API bills for all but the heaviest users. And the July llama.cpp situation showed the software stack trails the releases by weeks anyway — self-hosting frontier models means living on unmerged pull requests.

What runs locally instead?

If the goal is a strong open MoE on one machine, four models cover the territory, with ModelFit engine estimates at the recommended quant:

Each has a full hardware breakdown: GPT-OSS 120B, Qwen 3.5 122B-A10B, Nemotron 3.5 Lightning, GPT-OSS 20B. Qwen 3.5 122B-A10B is the closest in spirit to K3: a 122B MoE with about 10B active per token — the same sparsity trick at 4% of the size.

The honest framing: none of these match K3 at the frontier. What they match is the workload — long-horizon coding, research agents, long-document reasoning — at a quality level most teams will not distinguish in daily use, on hardware that fits under a desk. If you do hit the ceiling of a 120B-class local model, that is the moment to open the API tab, not to browse server parts.

FAQ

Can a 512GB Mac Studio run Kimi K3?

Not in practice. The smallest published build (474 GB, ternary 1-bit) leaves almost no headroom against the roughly 435-476 GB of usable unified memory, and its quality does not survive the compression. The smallest build worth running needs about 1.4 TB.

Is there a Q4_K_M quant of Kimi K3?

No. The official checkpoint is already MXFP4 (~1.56 TB), and community GGUF builds jump from 802 GB (Q2_K_XL) straight to 1,405 GB (Q4_K_XL). Sites quoting a single "Q4_K_M" figure for K3 are extrapolating a formula, not measuring a file.

How much VRAM for Kimi K3 at Q4?

About 1.4 TB for the weights, which means an 8x MI350X 288GB node or a dual-node 1TB EPYC server. No single GPU sold to consumers holds it at any quantization.

Is Kimi K3 open source?

The weights are public on Hugging Face under a custom Kimi K3 License, not MIT or Apache. It is open-weight with conditions, so read the license before commercial deployment.

What is the closest model I can run on one machine?

GPT-OSS 120B on a 96 GB Mac Studio (~43 tok/s est.) or Qwen 3.5 122B-A10B on the same machine (~27 tok/s est.). Both are sparse MoE designs with real coding strength, sized for hardware that exists at a desk.

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Every number in this guide is either measured from a linked repository or derived from the ModelFit engine with the estimate labeled. How we work: @@MODELFIT_11@@. Corrections welcome: @@MODELFIT_12@@.