Cheapest way to run Kimi K3
Quick answer
At 4-bit with an 8K-token conversation, Kimi K3 needs about 1508.9 GB.
| Option | What you need | Cost | Notes |
|---|---|---|---|
| Your own GPU | Doesn't fit any single consumer card in our list (largest 32 GB) at 4-bit with 8K context | – | – |
| Rent a GPU | No listed rental in our price data is big enough | – | – |
Fit rule: the Q4_K_M file (or the highest precision that fits) plus an 8K-token FP16 KV cache under 90% of memory, same as the calculator. Sizes and sources: Kimi K3 VRAM page. Rental prices: GPU prices (checked 2026-11-09). Hourly and per-token prices measure different things, so which is cheaper depends on how much you use it.
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Precision ladder: what each quant costs
| Precision | Needs at 8K | Smallest consumer GPU | Smallest Mac | Cheapest single-GPU rental | Cheapest rental overall (any GPU count) |
|---|---|---|---|---|---|
| 16-bit (FP16/BF16) | No published file at this precision | ||||
| 8-bit (Q8_0) | 1561.4 GB | none (max 32 GB) | none in our Mac list | none big enough in our price data | none big enough in our price data |
| 4-bit (Q4_K_M) | 1508.9 GB | none (max 32 GB) | none in our Mac list | none big enough in our price data | none big enough in our price data |
Every row names its precision, Mac rows included (Macs share unified memory with the system, so the same 90% rule applies). Single-GPU and multi-GPU rentals are listed separately: a multi-GPU rental needs software that splits the model across cards (llama.cpp, vLLM tensor parallel). Prices checked 2026-11-09.
How context length changes the bill (4-bit)
| Context | Needs | Cheapest rental overall |
|---|---|---|
| 8K tokens | 1508.9 GB | none big enough in our price data |
| 32K tokens | 1509.6 GB | none big enough in our price data |
| 128K tokens | 1512.3 GB | none big enough in our price data |
What $100 of rental buys you
We list no GPU purchase prices, and no rental in our price data is big enough for this model, so there is no rental-hours figure here.
Fit quirks for this model
- Mixture-of-experts: about 104.0B parameters are active per token, but every expert must still be in memory, so VRAM follows the total size, not the active size.
- Our estimate: only the 24 latent-attention (MLA) layers grow with context (24 × (512 + 64) × 2 bytes); the 69 linear-attention layers keep a fixed state.
- No Q4_K_M or Q8_0 published; 4-bit = Unsloth UD-Q4_K_XL, 8-bit = Unsloth UD-Q8_K_XL.
- Maximum context is 1,048,576 tokens; longer settings are capped.
Model data checked 2026-11-02; rental prices checked 2026-11-09. For file sizes, sources and the full fit table see the Kimi K3 VRAM requirements page; this page is only about cost.
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