How Much VRAM to Run Kimi K3 and K3.1 Pro? RAM, GPU and GGUF Table (2026)

How much VRAM do you need to run Kimi K3? Short answer: far more than any gaming PC has.

Kimi K3 and the new Kimi K3.1 Pro are 2.8 trillion parameter models, and even the smallest usable 1-bit GGUF needs about 610 GB of combined RAM and VRAM. Near full quality needs about 1.6 TB.

Below I give the memory table for every quant, the math behind it, which real hardware can actually run it (and how fast), and what K3.1 Pro’s open weights, released on November 4, 2026, change.

The second half covers K3.1 Pro’s specs, benchmarks, pricing and API, since it is live today in Kimi’s apps and trades blows with Claude Opus 5.5 on coding tests.

Kimi K3 VRAM and RAM Requirements by Quant

These are Unsloth’s published GGUF sizes for Kimi K3, with their recommended total memory. “Total memory” means system RAM plus GPU VRAM on a PC, or unified memory on a Mac. Kimi K3.1 Pro uses the same architecture, so expect its files to be within a few GB of these.

Unsloth’s rule of thumb is simple: RAM + VRAM should be about the size of the quant file. With less, llama.cpp still runs by streaming weights from disk, but it becomes painfully slow.

Notice that Q4_K_XL and Q8_K_XL are almost the same size. Most of the model, the expert weights, is already stored at about 4 bits in Moonshot’s release, so the “8-bit” quant mainly raises precision on the small shared layers. In practice, about 1.5 to 1.6 TB is “full quality” for Kimi K3.

Why Kimi K3 Needs So Much Memory (the Math)

Memory for weights is roughly parameters x bytes per parameter. Kimi K3 has about 2.8 trillion parameters:

Be careful with online calculators that list Kimi K3 at “594 GB in BF16” or “325 GB at Q4”. Those numbers cannot be right for a 2.8T model: 594 GB is the size of the 1-bit GGUF, not BF16.

The good news is speed. Kimi K3 only activates about 104B parameters per token (16 of 896 experts). Once everything sits in fast memory, each token costs roughly what a 100B dense model costs.

The bad news is that all 2.8T weights still have to be loaded, because a different set of experts is picked for every token.

VRAM vs RAM: Why a Small GPU Can Still Help

People search “how much VRAM”, but for mixture-of-experts models the better question is “how much total memory”. llama.cpp has two options for this:

• --cpu-moe keeps all expert tensors in system RAM and puts attention, shared layers and the KV cache on the GPU.
• --n-cpu-moe N keeps the experts of the first N layers in RAM, so you can fill your GPU with the rest.

With --cpu-moe, one 24 to 80 GB GPU covers the GPU part, and the other 600+ GB lives in RAM. That is why the realistic local setup is a big-RAM server with one or two GPUs, not a stack of gaming GPUs.

The catch is speed: generation is then limited by RAM bandwidth, because about 104B active parameters (around 22 GB at 1-bit) are read for every token.

As a rough upper bound, tokens per second is about memory bandwidth divided by bytes read per token. An 8-channel DDR5-4800 server gives about 300 GB/s, so the theoretical best at 1-bit is around 10 to 14 tokens per second. Real results are much lower, as the next sections show.

Which Hardware Can Actually Run Kimi K3?

For comparison, my own PC (2x RTX 5080 16 GB plus 128 GB RAM) runs big MoE models like Qwen3.8-Flash-Next fine, but by this math it is nowhere near enough for Kimi K3. If you are in the same boat, a smaller open model is the realistic choice.

Real-World Speed: What to Expect

• Unsloth, B200 GPUs, model fully in VRAM: about 20 tokens per second generation and over 120 tokens per second throughput.
• ComputingForGeeks, 4x A100 40 GB + 2 TB RAM, UD-IQ1_S with --cpu-moe: about 6 to 13 tokens per second for prompt processing but only about 0.1 tokens per second for generation, and about 9 minutes to load. Their attempt with --n-cpu-moe 78 failed with a CUDA out-of-memory error, because llama.cpp put too many layers on the last GPU.

The lesson: a CPU-offload setup works, but speed depends heavily on RAM bandwidth, NUMA layout and how the layers are split. Plan for slow, batch-style use unless the whole model fits in GPU memory.

Extra Memory for Long Context

The tables above cover the weights only. The KV cache for your context comes on top.

Kimi K3 uses Kimi Delta Attention, a hybrid of linear and full attention, which keeps the cache much smaller than a classic transformer of this size, but it still grows with context. Moonshot and Unsloth have not published exact KV cache sizes, so:

• Start with --ctx-size 8192 to 32768, then raise it while watching memory.
• Keep 20 to 40 GB spare beyond the file size for the cache, buffers and the operating system.
• The full 1M token context is only realistic on multi-GPU servers.

The Cheapest Ways to Use Kimi K3 Without the Hardware

• Kimi.ai app: free chat access to K3.1 Pro.
• Moonshot API: $2 per million input tokens and $10 per million output tokens for K3.1 Pro (details below).
• Rented GPUs: 8x B200 spot instances start around $42 per hour on some GPU clouds, which only makes sense for short tests or batch jobs.
• Smaller local models: for a 16-32 GB GPU, see my guides to the best local LLMs for consumer GPUs, MiniCPM-V 4.7 and which GGUF file to download.

What Is Kimi K3.1 Pro?

Kimi K3.1 Pro is the newest flagship from Moonshot AI, the Beijing lab behind the Kimi chatbot. It is post-trained from Kimi K3, which Moonshot open-sourced in July 2026, and it keeps the same giant mixture-of-experts design.

MarkTechPost reports that it uses Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), and activates 16 of 896 experts for each token through a framework Moonshot calls Stable LatentMoE.

Moonshot pitches it as “our most capable 3T-class open-weight model”, with a focus on full-project coding (games in Unreal, Unity and Godot), parametric 3D modelling in Blender and CAD, and more reliable finance and office work.

The Decoder headline sums up the reaction: Kimi K3.1 Pro is “closing in on” Claude Opus 5.5, and if the weights land as promised it would become the highest-scoring open-weights model available.

One more detail that got attention: Wccftech reports that Moonshot stripped cyber-offence data from training. Its own cyber benchmark shows the shipped model at 81.0% on xdev-bench-hard (pass@3), versus 90.5% for an unfiltered checkpoint.

Kimi K3.1 Pro Specs at a Glance

The number that matters for speed is the 104B active parameters. Once the model is fully in memory, each token costs roughly what a 100B dense model costs, not a 2.8T one.

The number that matters for local use is the 2.8T total, because every one of those weights still has to sit somewhere in memory.

Benchmarks vs Claude Opus 5.5 and GPT-6 Astra

These scores are Moonshot’s own, run at max reasoning effort and mostly inside a Claude Code-style harness, as reported by The Decoder. Treat them as a starting point until independent tests arrive.

A few takeaways:

• Long coding tasks are its strength. SWE-Marathon and DeepSWE both favour Kimi, which fits Moonshot’s “full-project coding” pitch.
• Science and CAD lag a little. On Terminal-Bench Science 0.1 it scores 38.1, against 63.3 for Opus 5.5 and 68.1 for GPT-6 Astra.
• Hallucinations are much lower than K3. On Moonshot’s internal Hallu Bench Hard, claim-level hallucination fell from 23.4% (K3) to 8.3%. GPT-6 Astra scores 5.5% on the same test.
• Independent index: On the Artificial Analysis Intelligence Index, K3.1 Pro scores 55 against Opus 5.5’s 58.

Pricing and the Highspeed Version

That is a 33% price cut compared with K3, for a model that scores higher. Moonshot says Highspeed runs two to three times faster, which is worth it for agent loops where latency adds up.

Since thinking is always on and the default output limit is 131K tokens, watch your output spend: long reasoning traces are billed as output.

How to Use Kimi K3.1 Pro Today

You do not need to wait for the weights. It is available now in four places:

• Kimi.ai: the free chat app on web and mobile.
• Kimi Work: Moonshot’s agent workspace for documents, slides and research.
• Kimi Code: the coding agent and terminal tool.
• Kimi API: an OpenAI-compatible endpoint you can drop into existing tools.

To get API access, create an account on Moonshot’s platform, top up at least $1 (the minimum, according to the quickstart), and create an API key.

API Example (OpenAI-Compatible)

Because the endpoint is OpenAI-compatible, the standard openai Python package works. Replace the base URL with the one shown in your Moonshot console.

pip install openai

Then in Python:

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_MOONSHOT_API_KEY",
    base_url="https://api.moonshot.ai/v1",  # check your console for the exact URL
)

resp = client.chat.completions.create(
    model="kimi-k3.1-pro",            # or "kimi-k3.1-pro-highspeed"
    messages=[{"role": "user", "content": "Write a Godot 4 script for a 2D player with double jump."}],
    reasoning_effort="high",          # low, high or max (default max)
)
print(resp.choices[0].message.content)

Settings to know, from MarkTechPost’s summary of the quickstart:

• Temperature is fixed at 1.0 and top_p at 0.95. Sending other values will not change behaviour.
• Thinking cannot be turned off. Use reasoning_effort="low" for quick answers.
• Image input does not accept public URLs. Send images as base64 or as uploaded file IDs.

You can also point coding agents that accept an OpenAI-compatible endpoint, such as Cline, Roo Code or Aider, at the same base URL and model ID.

Kimi K3.1 Pro Open Weights Are Out: Download, Size and Licence

Moonshot kept its promise. The full weights went up on Hugging Face on 4 November 2026 as moonshotai/Kimi-K3.1-Pro, with no form or waitlist.

The licence in plain English

It is a custom Kimi K3.1-Pro License, not Apache or MIT. As summarised from the model card:

• Most users: free to use, fine-tune and build on, keeping the licence notices.
• Model-as-a-service providers: above USD 20 million revenue over any 12 months, you need a separate agreement with Moonshot.
• Very large products: over 100M monthly active users or USD 20M monthly revenue must show “Kimi K3.1-Pro” in the UI.

Note: read the LICENSE file in the repo before commercial use. The summary above comes from early write-ups of the model card.

Is there a Kimi K3.1 Pro GGUF yet?

Not yet. llama.cpp does not support the new kimi_k31 architecture, so no GGUF can be made or loaded until that lands. Unsloth said on launch day it would look at it.

Ollama only has a kimi-k3:cloud tag (paid cloud), and LM Studio lists K3 only as a cloud model in Bionic. For a truly local build today, vLLM on a multi-GPU server is the only route.

Can You Run Kimi K3.1 Pro Locally?

Technically yes, practically only on server-class hardware. K3.1 Pro has the same 2.8T / 104B shape as Kimi K3, so K3’s published GGUF sizes are the best guide we have. Here is Unsloth’s table for Kimi K3:

Expect K3.1 Pro quants to land very close to these numbers. What that means in real hardware:

• Gaming PC (16 to 32 GB VRAM, 64 to 128 GB RAM): no. Even the smallest quant needs about four to five times more memory than a well-equipped desktop. My 2x RTX 5080 plus 128 GB RAM (160 GB total) is not in the conversation. See the hardware table above.
• One 512 GB Mac Studio: not enough for the 594 GB file without slow disk offload.
• Two networked 512 GB Mac Studios (1 TB): enough for 1-bit and 2-bit quants.
• Dual-socket server with 768 GB to 1 TB of DDR5: the cheapest way to run 1-bit or 2-bit quants on CPU, slowly, ideally with one or two GPUs holding the attention layers.
• Full quality: Moonshot recommended supernodes of 64 or more accelerators for K3. That is datacenter territory.

Unsloth’s docs say llama.cpp will still run if your memory is smaller than the file by streaming from disk, but generation becomes much slower. For context, Unsloth reports about 20 tokens per second for K3 on B200 GPUs when it fits in memory.

If you want strong local coding models on a normal PC, smaller open models are the realistic route. I have guides for MiniCPM-V 4.7 on a 16 GB GPU and the hardware needs for Muse Spark Open.

How Local Setup Will Likely Work (llama.cpp)

Until the K3.1 Pro files exist, here is how Unsloth runs Kimi K3 today. Expect the K3.1 commands to differ only in the repo name.

# build llama.cpp (Unsloth's fork with Kimi K3 vision support)
git clone https://github.com/unslothai/llama.cpp
cd llama.cpp
git fetch origin pull/48/head:kimi-k3-fullsize-vision
git checkout kimi-k3-fullsize-vision
cd ..
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --clean-first --target llama-cli llama-mtmd-cli llama-server llama-gguf-split
cp llama.cpp/build/bin/llama-* llama.cpp

# download only the 1-bit quant plus the vision projector
hf download unsloth/Kimi-K3-GGUF --local-dir unsloth/Kimi-K3-GGUF \
  --include "*mmproj-BF16*" --include "*UD-IQ1_S*"

# run
./llama.cpp/llama-cli \
  --model unsloth/Kimi-K3-GGUF/UD-IQ1_S/Kimi-K3-UD-IQ1_S-00001-of-00014.gguf \
  --mmproj unsloth/Kimi-K3-GGUF/mmproj-BF16.gguf \
  --temp 1.0 --top-p 0.95

Notes:

• Use -DGGML_CUDA=OFF for a CPU-only build. Metal is on by default on Macs.
• Check the actual shard filename after download; Unsloth’s page lists 14 shards in one place and 15 in another.
• Recommended sampling is temperature 1.0 and top_p 0.95, or top_p 1.0 for agentic work.
• Start with a 32K or 64K context. The full 1M context needs a huge KV cache on top of the weights.

Kimi K3.1 Pro vs Other Giant Open Models

If you can only run one of these locally, Muse Spark Open is by far the easiest, at roughly a third of the memory.

Kimi K3.1 Pro is the one to watch if you want the strongest open model and have the hardware, or you plan to use a hosted provider once the weights are out. See my Mistral Large 4 open weights post for that model’s specs.

Who Should Use It

• Developers on a budget: at $2 / $10 per million tokens, it is a cheap alternative to frontier closed models for long coding sessions.
• Game and 3D makers: Moonshot specifically trained for Unreal, Unity, Godot and Blender work.
• Companies that need self-hosting: once the weights are out, it is one of the few near-frontier models you can run on your own servers.
• Hobbyists with a single GPU: use the free Kimi.ai app, not a local install.

Frequently Asked Questions

How much VRAM do I need to run Kimi K3?

You need about 610 GB of combined RAM and VRAM for the smallest 1-bit GGUF (594 GB file), about 880 GB for good 2-bit quality and about 1.6 TB for near-lossless quality. Because it is a mixture-of-experts model, most of that can be system RAM, with one GPU holding the shared layers.

Can I run Kimi K3 on an RTX 4090 or RTX 5090?

No. A 24 or 32 GB GPU plus a normal 64 to 128 GB of RAM is about 4 to 10 times short of the 610 GB minimum. It would only run by streaming from disk, which is far too slow to use.

Can Kimi K3 run on a Mac Studio?

A single 512 GB Mac Studio is just short of the 594 GB 1-bit file. Two 512 GB Mac Studios connected together (1 TB) can run the 1-bit and small 2-bit quants.

Does Kimi K3.1 Pro need more VRAM than Kimi K3?

No. Both are 2.8T total and 104B active parameters, so expect the same memory needs. The weights are out (1.56 TB in MXFP4), but no GGUF exists yet, so final quant sizes are still to come.

Is Kimi K3.1 Pro open source?

It is open-weight. The files are on Hugging Face under a custom Kimi K3.1-Pro License, which adds terms for large model-as-a-service providers and very large products.

When were Kimi K3.1 Pro weights released?

On November 4, 2026, as moonshotai/Kimi-K3.1-Pro on Hugging Face, with no access gate.

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

Based on Kimi K3’s GGUF files, at least about 610 GB of combined RAM and VRAM for the smallest 1-bit quant, about 880 GB for 2-bit, and about 1.6 TB near full quality.

Is Kimi K3.1 Pro better than Claude Opus 5.5?

On Moonshot’s numbers it wins some long-horizon coding and agent tests and loses others, such as Terminal-Bench 4.0 (by 0.7 points), BenchCAD and science tasks. Artificial Analysis rates Opus 5.5 slightly higher overall (58 vs 55).

Is Kimi K3.1 Pro free?

The Kimi.ai chat app has free access. The API is paid, starting at $2 per million input tokens and $10 per million output tokens, with a $1 minimum top-up.

Can I turn off thinking?

No. Thinking is always on. Set reasoning_effort to low for faster, cheaper answers.

Final Thoughts

Kimi K3.1 Pro is a big moment for open AI. A model that trades blows with Claude Opus 5.5 on coding, at a fraction of the price, with weights promised within two weeks, is exactly what the open model community hoped for this year. The reality check is size: at 2.8T parameters, “open weights” means “you can self-host it on a server”, not “you can run it on your gaming PC”. For most people, the API or the free app is the way to try it today.

I will update this guide with download links, quant sizes and setup steps as soon as the weights are published. In the meantime, see how other giant open models compare in my Muse Spark Open local guide and Mistral Large 4 breakdown.

Sources

• MarkTechPost: Moonshot AI releases Kimi K3.1 Pro
• The Decoder: Kimi K3.1 Pro is closing in on Claude Opus 5.5
• Wccftech: cyber data stripped from training
• Kimi.ai announcement on X
• Unsloth: Kimi K3 GGUF sizes and llama.cpp guide
• ComputingForGeeks: running Kimi K3 locally on 4x A100 + 2 TB RAM
• Hugging Face: moonshotai/Kimi-K3.1-Pro (weights and licence)
• ModemGuides: K3.1 weights, GGUF status and llama.cpp support
• Kimi K3.1 Pro tech blog
• Spheron GPU guide for Kimi K3 (8x B200 pricing)