Install Kimi K3.1 Pro Locally: The Real 1.6 TB Download Size, the GPUs It Takes, vLLM and SGLang Setup, and What to Run Instead

Jake's LFM3-1.7B install from yesterday's guide had been running behind the counter for less than a day when the notification landed: Kimi K3.1 Pro, Moonshot AI's biggest open-weight model, had published its full weights on Hugging Face. Jake did what he always does. He texted Ethan: "The giant Kimi is free now. Can we upgrade the shop?" Ethan's reply took eleven minutes, which for Ethan is a monologue. It started with a laughing emoji and ended with "read this before you touch anything."

Here are the numbers that explain Ethan's eleven minutes. Kimi K3.1 Pro is a 2.8-trillion-parameter model with 104 billion parameters active per token and a one-million-token context window. Its weights ship as 96 files that together add up to about 1.6 terabytes — roughly 1,450 times the disk space of the 1.1 GB model Jake installed yesterday. Every single shard of it is about fifteen copies of that model. The download is free. The computer you need to run it is the actual price tag, and that is what this guide is about.

By the end of this post you will know exactly what installing Kimi K3.1 Pro locally means in practice: how to stage the 96-shard download on Windows 11, on Kali Linux, or straight onto a GPU server; how to serve it with vLLM or SGLang once you have the hardware; what the license lets you do; and what the whole exercise costs against simply using the API. Every file size, shard count, command, and GPU number in here is the real thing, so you can plan before you touch a terminal.

This is part of the run-AI-locally guide series, and it is the honest member of the family. If you want a frontier-class model that installs in ten minutes on a laptop, the LFM3-1.7B guide linked above is that post. If you want the API side of Kimi — keys, pricing, Claude Code and Codex wiring — our Kimi K3.1 Pro setup and pricing guide covers it in depth. This one is for the middle case: you have big GPUs, or you are seriously shopping for them, and you want the biggest open-weight Kimi running on your own iron.

What Kimi K3.1 Pro actually is, in plain English

Kimi K3.1 Pro is Moonshot AI's most capable open-weight model, first served through the Kimi apps and API on October 23, 2026, with the full weights landing on Hugging Face right at the start of November, a couple of days ahead of the promised date. It is a mixture-of-experts model: 2.8 trillion parameters in total, of which only 104 billion wake up for any given token, because a router picks 16 experts out of 896 for each one. That is the same trick that lets a model be enormous and fast at the same time — and, as we will see in the hardware section, it does nothing to shrink the memory bill.

The K3.1 generation is a post-training upgrade of Kimi K3 rather than a new build from scratch: same 2.8T skeleton, dramatically more reinforcement learning across hundreds of domains, and one architectural change worth knowing — the full-attention layers of K3 were replaced with Gated DeepSeek Sparse Attention layers, which is what lets a one-million-token context stay practical. It accepts text and images through a 401M-parameter vision encoder called MoonViT-V2, and it always thinks before it answers, with a reasoning effort dial you can set to low, high, or max.

The benchmark story is why people want this model on their own hardware. On Terminal-Bench 4.0, which grades coding agents on realistic terminal work, it scores 65.7% — ahead of GPT-6 Astra at 58.2% and a hair behind Claude Opus 5.5 at 66.4%. It sits fourth on the Artificial Analysis Intelligence Index with a score of 55, three points off first place. On financial work it improved over K3 by 7.2 points on modeling and 11.6 on research, its hallucination rate on stress tests dropped from 23.4% to 8.3%, and it handles full-project work in Blender, Unreal, Unity, Godot, and CAD. Jake's summary after reading the scores: "So it's the smart one." Ethan's: "It's the smart one that weighs 1.6 terabytes." Both true.

Kimi K3.1 Pro at a glance.

Property	Specification
Parameters	2.8T total, 104B active per token (16 of 896 experts)
Architecture	93 layers: 69 Kimi Delta Attention + 24 Gated DeepSeek Sparse Attention
Context window	1,048,576 tokens (1M), combined input and output
Inputs	Text and images, via the MoonViT-V2 vision encoder (401M params)
Weight format	MXFP4 weights with MXFP8 activations, quantization-aware trained, 96 safetensors shards
Total download	About 1.6 TB
Reasoning effort	low, high, or max; max is the default
License	Kimi K3.1-Pro license: commercial use, fine-tuning, and redistribution allowed, with revenue thresholds

What "open weights" means here, and what it does not

Open weights mean four concrete things. You can download the files without asking anyone's permission — the repository is public and ungated. You can inspect them, audit the tokenizer, and run your own evaluations without rate limits. You can fine-tune them on your own data, which is where most of the commercial value of an open frontier model lives. And you can serve them on infrastructure you control, which is the entire point for regulated industries, defense-adjacent work, and anyone whose data is not allowed to visit a third-party API. Jake's phrase for it, after Ethan explained: "So it's a buy, not a lease."

Now the three things open weights do not mean, because every one of them will bite someone this week. First, it does not mean "runs anywhere." There is no GGUF file for K3.1 Pro, no Ollama tag, nothing LM Studio can load; the checkpoint is 96 MXFP4 shards that only server-grade inference engines understand. Second, it does not mean there is free size savings waiting. Most models ship in 16-bit and the community squeezes them to 4-bit; K3.1 Pro was trained quantization-aware and ships already at 4-bit, so the 1.6 TB you download is the small version. Third, it does not mean the weights behave identically to the API model: Moonshot deliberately removed cyber-related training data before release, and publishes the cost — 81.0% on the offensive-security benchmark xdev-bench-hard for the shipped model, against 90.5% for a checkpoint trained without those measures. A self-hosted copy inherits that reduction. Open means inspectable, not unrestricted and not identical.

One more boundary worth drawing early, because the question will come up at every dinner table this model gets discussed at: this is a Kimi K3.1-Pro-specific license, not the K3 license and not MIT. The everyday reading is friendly — use it, sell products built on it, fine-tune it — but there are two thresholds that matter at scale, and the license section near the end walks both. If you are past them, the license is a business conversation, not a technical one.

Size required: the 1.6 TB download, shard by shard

The repository at huggingface.co/moonshotai/Kimi-K3.1-Pro holds 96 files named model-00001-of-000096.safetensors through model-00096-of-00096.safetensors. The first shard is 2.34 GB; after that they alternate between about 16.99 GB and 16.58 GB in a steady pattern, and the whole set sums to about 1.6 terabytes. Alongside the weights ride a handful of small but load-bearing files: config.json, the license, and the model's own Python implementation files such as configuration_kimi_k3.py and encoding_k3.py, which is why every load command in this guide carries --trust-remote-code. Grab the whole repository, not just the shards.

Plan for 2 TB of free disk on whatever machine stages the download. The weights are 1.6 TB, and the remaining headroom absorbs the Docker images, the serving engine's working files, and the inevitable second download you will run the day a shard arrives corrupted. NVMe over spinning disk is not a luxury here: the serving engine streams 96 shards into GPU memory at load time, and the difference between a fast and slow disk is the difference between a coffee break and a lunch break, every single time the model loads.

Time to download about 1.6 TB, by sustained connection speed.

Sustained speed	Time for the full download	Real-world note
100 Mbps	About 36 hours	Home fiber on a good day; start it Friday night
500 Mbps	About 7 hours	Office connection; one working day
1 Gbps	About 3.5 hours	Datacenter-class; still a real commitment

The download is resumable, which matters more than it sounds at these sizes. Jake, being Jake, started pulling shards onto the Kali laptop over the shop's Wi-Fi just to watch the progress bars. Forty minutes later he had two shards and a new appreciation for the arithmetic, and Ethan's staging advice became the plan: download once, onto the machine that will serve the model, and never move 1.6 terabytes twice. The step-by-step for that lives in the download section below, and it works identically on Windows 11, Kali Linux, and the GPU server itself.

The hardware bill: how much GPU memory 2.8 trillion parameters really eat

Start with the honest arithmetic, because it decides everything else. The weights alone are about 1.6 TB. Serving adds the KV cache and activations on top, so the practical floor lands near 1.7 TB of GPU memory before you have served a single token at long context. And here is the trap in the "104B active" number: mixture-of-experts saves compute, not memory. Only 16 experts compute per token, but all 896 of them must sit in memory waiting for the router to call them. There is no pay-for-what-you-use option for the weights. Jake's translation: "It's a gym where everyone has a membership, but they all need a parking spot even on days they don't show up."

The serving recipe for this model names its supported GPUs explicitly, and the list is short because MXFP4 is a datacenter format: NVIDIA H100 80G, H200 141G, B200 180G, GB200 NVL4 192G, GB300 268G, GB300 NVL4 288G, VR200 NVL4 288G, AMD MI300X 192G, MI325X 256G, MI355X 288G, plus the Zhenwu 810E 96G and M890 144G. The reference configurations are tensor-parallel 8 on two GB300 trays, one B300 node, or one MI355X node. What that means in plain shopping terms is in the table below, with the per-node totals multiplied out.

Eight-GPU nodes against the ~1.7 TB serving floor.

Node (8 GPUs)	Total GPU memory	Verdict
8 × H100 80 GB	640 GB	Not close; you need three or more such nodes
8 × H200 141 GB	1,128 GB	Short of the floor; two nodes minimum
8 × B200 180 GB	1,440 GB	Just under; plan on two linked nodes
8 × MI300X 192 GB	1,536 GB	Borderline; a second node buys breathing room
8 × GB300 268 GB	2,144 GB	Works; this is a reference configuration
8 × 288 GB cards (B300, GB300 NVL4, MI355X)	2,304 GB	Works on a single node; reference configuration

Two requirements ride along with the GPU count. Interconnect: tensor parallelism across 16 GPUs means the cards talk constantly, so you want NVLink inside each node and InfiniBand or equivalent between them; a slow link shows up as GPUs idling at each other. Host side: the CPU and system RAM requirements are almost boring by comparison — the usual server-class chip and enough RAM to buffer the shard streaming, with your 2 TB of fast NVMe doing the real work. If reading this section made you wince, that was its job; the sections on renting GPUs and on the API exist because of exactly that wince.

Before you download: the prep checklist

Every failed big-model install Ethan has ever been called in to rescue traces back to skipping one item on a boring list, so here is the list. Work through it in order and the actual install later is almost anticlimactic.

1. Reserve 2 TB of fast NVMe on the machine that will stage or serve the weights. One terabyte and change is not enough once you count working files and the Docker image.
2. Confirm your GPUs are on the supported list from the hardware section, and count total memory against the ~1.7 TB floor before you download anything. nvidia-smi or rocm-smi tells you in five seconds.
3. Install Docker and the NVIDIA Container Toolkit on every GPU node. The verified serving path is a Docker image, not a pip install, and the toolkit is what lets the container see the cards.
4. If you are going two-node: note both machines' IP addresses, pick the head node, and identify the network interface name each box uses for inter-node traffic — it goes into two environment variables at serve time.
5. Get a Hugging Face account and create an access token, even though the repo is ungated. Logged-in downloads get saner rate limits over a 36-hour pull, and the token is one hf auth login away.
6. Decide where the weights live. Best: on the GPU nodes themselves, or on shared storage they both mount at the same path. Worst: on your laptop, from which you will then upload 1.6 TB somewhere else.

Step one on any OS: downloading the weights on Windows 11 or Kali Linux

Downloading is the one part of this install that genuinely works on ordinary computers, so it deserves a proper walkthrough — you may be staging files for a server that arrives next week, or pulling them onto the Kali box you will later carry into the datacenter. The tool is the hf command from the huggingface_hub package, and the steps are the same everywhere; only the Python setup differs.

1. On Windows 11: install Python 3 from python.org, accept the PATH option, and open a new PowerShell window. Verify with python --version.
2. On Kali Linux: create a virtual environment first, because Kali's system Python is protected by the externally-managed-environment rule and pip will refuse to touch it. sudo apt install python3-full, then python3 -m venv kimi-env and source kimi-env/bin/activate. If pip still throws the externally-managed error, our dedicated guide to that fix covers every cause.
3. Install the download tool inside your Python environment or venv:

   pip install -U huggingface_hub

4. Log in with your token (optional but kind to your future self over a 36-hour pull):

   hf auth login

5. Pull the repository into a named folder:

   hf download moonshotai/Kimi-K3.1-Pro --local-dir Kimi-K3.1-Pro

   The command is resumable: if the connection drops at shard 61 of 96, run the same line again and it picks up where it stopped rather than starting over. When it finishes, you should count 96 model-*.safetensors files plus the config, license, and Python files.

A note on where to run this: if the GPU server already exists, run the download on it and skip the double transfer. If it does not, download to whichever machine has the 2 TB of NVMe and the patience. Jake's two-shard Wi-Fi experiment was scientifically valuable, in the way that touching a hot stove is educational. For the shape of the whole staging process on a smaller model — same commands, friendlier file sizes — the LFM3-1.7B guide linked at the top walks it with screenshots of every step's output.

Install path 1: vLLM on a GPU server, step by step

vLLM is the primary serving route, shipped as a purpose-built Docker image with version 0.34.0 or newer under the hood, and it ends with an OpenAI-compatible API on port 8000 that anything from curl to Claude Code can talk to. The walkthrough below is the two-node tensor-parallel-8 setup for machines that cannot hold the model on one node; if you are on a single 8-GPU node of 288 GB cards, drop the three multi-node flags and you are done in one command.

1. Prepare both nodes. Docker plus the NVIDIA Container Toolkit installed, the 2 TB of weights staged identically on each machine or on shared storage both mount at the same path, and nvidia-smi showing all eight GPUs on each box.
2. Pull the image on both nodes:

   docker pull vllm/vllm-openai:kimi-k31

3. Launch the head node (rank 0). Replace $IFACE_NAME with your network interface, for example eth0, and $HEAD_IP with this machine's own address:

   docker run --gpus all \
   --privileged --ipc=host -p 8000:8000 \
   -v ~/.cache/huggingface:/root/.cache/huggingface \
   -e GLOO_SOCKET_IFNAME=$IFACE_NAME \
   -e NCCL_SOCKET_IFNAME=$IFACE_NAME \
   -e VLLM_ENGINE_READY_TIMEOUT_S=3600 \
   vllm/vllm-openai:kimi-k31 moonshotai/Kimi-K3.1-Pro \
   --trust-remote-code \
   --tensor-parallel-size 8 \
   --nnodes 2 \
   --node-rank 0 \
   --master-addr $HEAD_IP \
   --load-format fastsafetensors \
   --enable-auto-tool-choice \
   --tool-call-parser kimi_k3 \
   --reasoning-parser kimi_k3

4. Launch the second node with the identical command, changing exactly one thing: --node-rank 1. Leave --master-addr pointing at the head node's IP.
5. Wait, genuinely. Streaming 1.6 TB of shards into 16 GPUs is not instant, which is why the command carries VLLM_ENGINE_READY_TIMEOUT_S=3600 — a full hour of patience is budgeted for load time. Watch the container logs; "engine ready" is the phrase that pays.
6. Test the endpoint from the head node:

   curl http://localhost:8000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{"model": "moonshotai/Kimi-K3.1-Pro",
   "messages": [{"role": "user", "content": "Say hello in one sentence."}]}'

Once replies are flowing, two dials shape the experience. The reasoning effort setting — low, high, or max, with max as the default — trades thinking depth for speed, and low is the right gear for chatty work while max earns its keep on hard analysis. For sampling, the evaluation configuration is temperature 1.0 with top-p 0.95, and the model was tuned with that profile in mind, so treat it as the known-good starting point before experimenting. Jake asked the obvious question about the thinking traces, and Ethan's answer doubles as the best description of the model's character: it always shows its work, which is either comforting or expensive depending on how many tokens you are burning.

The flags worth understanding rather than copy-pasting: --tensor-parallel-size 8 splits each node's eight GPUs into one cooperating team; --load-format fastsafetensors is the fast path for streaming those 96 shards; --trust-remote-code allows the model's own Python files that shipped in the repo; and the tool-call and reasoning parsers named kimi_k3 are what make function calling and the thinking traces come out as clean structured JSON instead of raw text. One behavioral note that saves an afternoon: in multi-turn conversations, pass the complete assistant messages back each time, including the reasoning_content and any tool_calls — trimming them confuses the model's own conversation format.

Install path 2: SGLang, the other first-class route

SGLang is the second engine named for serving this model, alongside TokenSpeed as a third option, and it is the one to reach for if your stack already standardizes on it. The shape of a single-node launch is the family standard:

pip install sglang
python3 -m sglang.launch_server \
--model-path moonshotai/Kimi-K3.1-Pro \
--tp 8 \
--trust-remote-code \
--host 0.0.0.0 \
--port 30000

Two honest caveats, because this path moves faster than any article can. Use a current SGLang build — support for a brand-new architecture is exactly the kind of thing that lands and improves release by release, and a stale install is the first suspect for any load failure. And treat multi-node flags, memory settings, and parser options as things to confirm against the model's own cookbook page, which carries the live, tested command set for this exact model. Ethan's rule, earned the hard way: for a model this fresh, the engine's own recipe wins every argument with a blog post, including this one.

What you get at the end is the same prize as the vLLM path: an OpenAI-compatible endpoint on your own hardware, serving a top-five intelligence model with a one-million-token context, no per-token bill, and no data leaving the building. From the application side, the two engines are interchangeable — point your client at whichever port is listening.

Install path 3: renting the GPUs instead of owning them

Almost nobody should buy sixteen H200s to try this. The sane middle path is renting a node from a GPU cloud for the hours you actually need, running the exact commands from the vLLM section, and releasing it when done. The prep differences are small but real.

1. Pick a provider that actually stocks the supported cards: B300, GB300-class, or MI355X for single-node simplicity, or H100/H200 instances you combine into a two-node cluster. This filters the market fast.
2. Size the boot disk at 2 TB of NVMe when you create the instance — the default disk on most GPU instances will not hold the weights, and discovering that after a 36-hour download is a special kind of pain.
3. Download inside the rented machine, not before it. Provider networks routinely pull at gigabit speeds, which turns the 1.6 TB fetch into an afternoon coffee rather than a weekend.
4. Run the vLLM commands as written, then do your fine-tuning prep, evaluation runs, or serving, and snapshot anything you want to keep before you tear the instance down.
5. Price it against the API before you rent twice. Hourly rates for this class of hardware move week to week and provider to provider, so check live pricing rather than trusting any number in an article — including this one.

The pattern that works for most teams: rent first, prove the workload, and only then decide whether owning hardware pencils out. Ethan's outfit ran a frontier open model this way for a quarter before a single purchase order was signed, and the benchmark numbers from the rented cluster were identical to the ones from the racks that eventually replaced it. Hardware is a financial decision wearing a technical costume.

The API alternative: when self-hosting loses the math

Here is the honest cost picture that Ethan wrote on the back of a receipt for Jake. The Kimi API serves K3.1 Pro at $2 per million input tokens and $10 per million output tokens, with a Highspeed variant that runs two to three times faster at the same intelligence tier. The same model, the same one-million-token context, none of the 1.6 terabytes.

What a month of Kimi K3.1 Pro costs on the API, at $2 in / $10 out per million tokens.

Monthly volume	API cost per month	What it means
5M in + 5M out	$60	A serious individual or small team; two dinner bills
50M in + 50M out	$600	A product with real traffic; still far below one supported GPU
500M in + 500M out	$6,000	The zone where clusters start to look reasonable

Read the table twice, because it is the whole business case in six numbers. Below roughly half a billion tokens a month, self-hosting does not save money — it spends more of it, plus your nights. The reasons to self-host anyway are the ones money cannot fix: data that legally cannot visit an API, air-gapped environments, custom fine-tunes you serve privately, or token volumes past the break-even zone. For everyone else, the API is the same brain at a rental price, and our Kimi K3.1 Pro setup and pricing guide walks the key setup, the reasoning effort dial, and the Claude Code and Codex wiring end to end.

What you can actually run locally today instead

If the hardware section closed a door, this one opens the windows. There is a real ladder of "Kimi-adjacent and local," and every rung on it is honest about what it is.

The realistic local options, from smallest to largest.

Option	Download size	Runs on	Best for
LFM3-1.7B (our guide)	1.1 GB	Any laptop or phone	Private everyday text, estimates, replies, tool calling
Whisper (our guide)	Under 1 GB	Any laptop	Offline speech-to-text, the other half of a local assistant
Kimi K2-line GGUFs	From about 304 GB	Big-RAM workstations, multi-GPU rigs	Frontier-ish Kimi quality in llama.cpp-compatible form
kimi-k3:cloud on Ollama	Cloud (tiny local client)	Any machine, needs an API key	Kimi quality with Ollama ergonomics, billed per token
Kimi K3 community GGUFs	466 GB to 1.56 TB	Half-terabyte-memory exotic rigs	The predecessor K3 in llama.cpp forks; not for the faint of disk

The K3 row deserves a sentence of explanation, because it is the best preview of K3.1 Pro's future: within days of K3's weights landing in July, community quantizations appeared running from 466 GB for a 1-bit build up to 1,561 GB, and they needed a specialized llama.cpp fork to run at all. Something similar will likely happen for K3.1 Pro eventually — but note what the floor looks like: even the most crushed K3 quant was still 466 GB, roughly 420 times the LFM3 file. No amount of quantization turns a 2.8T model into a laptop model; the ceiling of "local" just has more than one floor. Our GGUF quantization types explained piece covers what those quality levels actually cost you, and the LFM3-1.7B install guide is the one we would hand to a friend.

The license rules, in plain English

The K3.1-Pro license is friendlier than most people will fear and stricter than a few will hope. What you can do without asking anyone: download it, run it, serve it, fine-tune it, redistribute it, and sell products and services built on it, keeping the copyright notices in your copies. Jake's repair-shop estimate bot, a consultancy's fine-tuned contract reviewer, an app that answers questions over a company's own documents — all plainly inside the lines.

Two thresholds change the conversation. First, if you operate a model-as-a-service business and your aggregate revenue passes $20 million over any consecutive twelve months, you need a separate agreement with Moonshot before using the model commercially. Second, if you ship a commercial product or service with more than 100 million monthly active users, or more than $20 million in monthly revenue, the name "Kimi K3.1-Pro" must be displayed prominently in your user interface. Neither clause touches normal businesses or normal apps; both matter at platform scale. And the standing disclaimer that keeps this paragraph honest: this is an engineer's summary, not legal advice, and the license file ships in the repository you downloaded for exactly this reason.

Troubleshooting: the errors you will actually hit

The download dies at shard 40-something. Network hiccups over a 1.6 TB pull are a when, not an if. The fix is the same command again: hf download resumes rather than restarts. If a specific shard keeps failing, free disk space is the second suspect — you budgeted 2 TB for exactly this moment.

Docker says no space left during launch. The serving image plus 96 shards plus the engine's working files live on the same volume on most default setups. Confirm with df -h, and either move the Hugging Face cache to the big NVMe volume or clean old images before relaunching.

NCCL timeouts between the two nodes. The classic multi-node failure, and the reason the launch command sets GLOO_SOCKET_IFNAME and NCCL_SOCKET_IFNAME to a specific interface. If both boxes have several NICs, the libraries can pick the wrong one and spend forever talking to a network that leads nowhere. Set the variables to the interface that actually connects the nodes, and check that the head node's port is reachable from the worker.

Out of memory at load time. The model needs all 896 experts resident, so a node that "almost" fits will fail at shard 80-something, an hour into loading, which is the cruelest possible failure mode. Check total GPU memory against the ~1.7 TB floor from the hardware table before retrying, and do not be fooled by the 104B-active number — memory does not do pay-per-use.

Load errors mentioning unknown architecture or remote code. Two usual causes: serving engine out of date for so new a model (use the kimi-k31 image or a current SGLang build), or a partial download missing the small Python files that ride alongside the shards. Re-run the download command; it fills gaps for free.

Tool calls come back as soup. The parsers named kimi_k3 in the serve command exist to turn the model's function-calling output into structured JSON; leave those flags off and your application receives prose instead of calls. And in multi-turn tool conversations, return the complete assistant messages including reasoning content and tool calls — trimming history to plain text is the number one self-inflicted wound with this model.

"Why won't Ollama or LM Studio just open it?" Because there is nothing for them to open: no GGUF exists for K3.1 Pro at publish time, and the cloud-hosted kimi-k3:cloud on Ollama is exactly that — cloud-hosted, reached through an API key, never touching your disk. When community quants appear, the K3 row in the alternatives table is the realistic preview of their size class. Until then, the serving paths above are the only doors into these weights.

The Kimi K3.1 Pro local install FAQ: 16 questions

How much disk space do I need to install Kimi K3.1 Pro locally?

The weights are about 1.6 TB: 96 safetensors shards, mostly 16.99 GB and 16.58 GB each, with a 2.34 GB first shard. Plan for 2 TB of free NVMe so the Docker image, working files, and a resumed download never squeeze you. There is no smaller official download; the model ships already quantized to MXFP4, so 1.6 TB is the small version.

How much VRAM does Kimi K3.1 Pro need to run?

Roughly 1.7 TB of total GPU memory as a practical floor: about 1.6 TB of weights plus the KV cache and activations. In single-node terms that means eight 288 GB GPUs (B300, GB300 NVL4, or MI355X); with smaller supported cards you combine nodes, such as two eight-GPU GB300 trays. The 104B-active figure does not reduce memory, because all 896 experts must stay resident even though only 16 compute per token.

Is Kimi K3.1 Pro free to download and use?

The download is free from Hugging Face with no access request, and the Kimi K3.1-Pro license permits commercial use, fine-tuning, and redistribution. The two thresholds to know: model-as-a-service businesses past $20 million revenue over any rolling twelve months need a separate agreement, and products past 100 million monthly users or $20 million monthly revenue must display the model's name prominently. Running it costs only electricity — a meaningful amount of it.

Can I run Kimi K3.1 Pro on my laptop or desktop PC?

No consumer computer can hold it. The weights alone are about 1.6 TB, roughly 1,450 times the 1.1 GB LFM3-1.7B file from our other guide, and the serving floor is around 1.7 TB of GPU memory. You can absolutely download the shards on a laptop — the walkthrough works on Windows 11 and Kali Linux — but running them takes datacenter hardware or a rented GPU node.

Is Kimi K3.1 Pro available on Ollama or LM Studio?

Not as a local model. Ollama lists kimi-k3 as a cloud-only tag that runs on Ollama's servers behind an API key, and there is no K3.1 Pro entry at all. LM Studio cannot load the checkpoint either, because the release is 96 MXFP4 safetensors shards with no GGUF packaging. The local doors into K3.1 Pro are vLLM, SGLang, and TokenSpeed on server GPUs.

What is the difference between Kimi K3.1 Pro and Kimi K3?

Same 2.8T skeleton, very different finish. K3.1 Pro is K3 after far more reinforcement learning across hundreds of domains, plus one architectural swap: the full-attention layers became Gated DeepSeek Sparse Attention layers, which keeps the one-million-token context practical. The results show up as Terminal-Bench 4.0 at 65.7% against K3's 13%, hallucination down from 23.4% to 8.3% on stress tests, and large gains on financial modeling and research. Both are open weight; K3.1 Pro is the smarter one.

How long does the 1.6 TB download take?

At a sustained 100 Mbps, about 36 hours; at 500 Mbps, about 7 hours; at 1 Gbps, about 3.5 hours. The hf download command is resumable, so a dropped connection costs you nothing but time — run the same line again and it picks up at the shard where it stopped. Downloading on the GPU server or a rented node is usually fastest, since datacenter networks routinely sustain gigabit speeds.

vLLM or SGLang: which should I use to serve it?

vLLM is the most traveled road: a purpose-built Docker image, vllm/vllm-openai:kimi-k31 with vLLM 0.34.0 or newer, and a documented tensor-parallel-8 recipe across two nodes. SGLang is equally first-class and the right pick if your stack already uses it — just run a current build and check its cookbook for the live command set. TokenSpeed is the third named option. All three end at an OpenAI-compatible endpoint, so your application code does not care which you chose.

Does Kimi K3.1 Pro run fully offline once downloaded?

Yes. Once the 96 shards and the small Python files are on your machines, inference is arithmetic on your own GPUs — no calls home, no per-token billing, no account. The only online moments are the initial download and your Docker image pulls. That property is the entire reason regulated industries and air-gapped shops care about open weights in the first place.

What does the Kimi K3.1-Pro license allow?

Use, fine-tune, redistribute, and build commercial products on it, with copyright notices kept in place. Two thresholds apply at scale: a separate agreement if you run it as a model-as-a-service business above $20 million revenue over any consecutive twelve months, and prominent "Kimi K3.1-Pro" attribution in products above 100 million monthly active users or $20 million monthly revenue. Below those lines, nothing to negotiate. This is a summary, not legal advice — the license file ships in the repository.

Are the open weights identical to the API model?

Same model, deliberately different training data. Moonshot removed cyber-related content from both the mid-training corpus and the reinforcement-learning mix before release, and publishes the effect: 81.0% on the offensive-security benchmark xdev-bench-hard for the shipped model versus 90.5% for a checkpoint trained without those measures. A self-hosted copy inherits the reduced cyber capability by design. For coding, finance, and general work, the capability story is the one the benchmarks tell.

Can I download a smaller quantized version or GGUF?

Not at publish time. There is no official GGUF and no community quant yet, and the precedent from Kimi K3 sets expectations: its community GGUFs run from 466 GB at 1-bit to 1,561 GB, and they needed a specialized llama.cpp fork. When K3.1 Pro quants appear, expect the same size class — because the release is already 4-bit MXFP4, there is no 16-bit-to-4-bit saving left to harvest. Smaller Kimi exists in the K2 line, with GGUFs starting around 304 GB.

Is self-hosting cheaper than the Kimi API?

Usually not. The API costs $2 per million input and $10 per million output tokens, so 50 million of each runs $600 a month — far below the cost of owning or renting the 1.7 TB of GPU memory this model needs. Self-hosting starts to pencil out around half a billion tokens monthly, or when the real reasons apply: data that cannot leave your walls, air-gapped deployment, custom fine-tunes served privately. Our setup and pricing guide covers the API side in depth.

Does Kimi K3.1 Pro support images and vision?

Yes. It accepts text and images natively through the MoonViT-V2 vision encoder, a 401M-parameter component trained alongside the language model rather than bolted on afterward. In serving terms, you send image content in your messages the OpenAI-compatible way and the model reasons over it. Note that vision support in your chosen serving engine needs to be current — a fresh model plus a stale engine is the classic recipe for multimodal loading errors.

What can I run locally instead of K3.1 Pro today?

On ordinary hardware, our LFM3-1.7B guide gives you a 1.1 GB tool-calling model that runs on any laptop or phone, and Whisper covers offline speech-to-text in under a gigabyte. On big-RAM workstations, the Kimi K2 line's community GGUFs start around 304 GB. If it is specifically Kimi quality you want without the iron, the API or Ollama's cloud-hosted kimi-k3:cloud deliver it with zero disk commitment. Every one of those is a real, working local option at publish time.

Will Kimi K3.1 Pro come to Ollama or llama.cpp later?

Nobody has announced anything, but the K3 precedent gives a reasonable preview: community quantizations appeared within days of the weights, in GGUF sizes from 466 GB to 1.56 TB, requiring a specialized llama.cpp fork to run. Expect the K3.1 Pro story to rhyme — a community GGUF in the half-terabyte class, runnable only on exotic hardware. What will not happen is a laptop-sized version; quantization shrinks weights, but 2.8 trillion parameters have a floor, and it is nowhere near a MacBook.

The verdict: who should install it, and who should not

Install it yourself if you are already in one of three positions: you operate supported-GPU clusters and want a top-five intelligence model on your own racks; your compliance regime means an API is simply not an option; or you have a fine-tuning program where owning the weights is the whole point. For those teams, this is a genuinely historic release — frontier-class, one-million-token context, honest license — and the vLLM path in this guide is a weekend, not a quarter.

Everyone else should use the door that fits. Heavy but non-restricted usage: the API, with the setup and pricing guide as your map. Truly local, truly private, ordinary hardware: the LFM3-1.7B install, which Jake's counter PC is running right now. Jake's own closing take, delivered while watching a 16.99 GB shard crawl across the shop Wi-Fi: "The big Kimi is free like a piano is free. Somebody still has to carry it up the stairs." Ethan, who has carried pianos, approved the metaphor — and then pointed out that his employer's cluster had the whole model loaded before Jake finished his coffee. Both of them are in this post, and so is the ladder between them.