What it costs to run Kimi K3 (MoE)
Trying to decide what to buy? The best GPU for Kimi K3 (MoE) →
Memory needed, by quantisation
At 16k context. Weights + KV cache + ~0.8 GB overhead.
| Quant | Weights | KV cache | Total | Quality |
|---|---|---|---|---|
| F16 | 5178.0 GB | 0.4 GB | 5179.2 GB | Full precision. Reference quality, twice the size of Q8 for no practical gain in most chat use. |
| Q8_0 | 2750.8 GB | 0.4 GB | 2752.0 GB | Effectively lossless. Use when VRAM is not the constraint. |
| Q6_K | 2123.0 GB | 0.4 GB | 2124.2 GB | Very close to Q8 at meaningfully less memory. A safe high-quality pick. |
| Q5_K_M | 1838.2 GB | 0.4 GB | 1839.4 GB | Small, hard-to-notice quality loss. Good balance. |
| Q5_0 | 1792.9 GB | 0.4 GB | 1794.1 GB | Older-style 5-bit. Q5_K_M is usually the better pick at the same size. |
| Q4_K_M | 1569.6 GB | 0.4 GB | 1570.8 GB | The community default. Best quality-per-gigabyte for most people. |
| Q4_0 | 1472.5 GB | 0.4 GB | 1473.7 GB | Older-style 4-bit, measurably worse than Q4_K_M at a similar size. Avoid unless required. |
| IQ4_XS | 1375.4 GB | 0.4 GB | 1376.6 GB | Newer 4-bit, smaller than Q4_K_M with comparable quality. Needs a recent llama.cpp. |
| Q3_K_M | 1265.4 GB | 0.4 GB | 1266.6 GB | Noticeable degradation. Use to fit a larger model that would otherwise not run. |
| Q2_K | 1084.1 GB | 0.4 GB | 1085.4 GB | Heavy degradation. Almost always better to run a smaller model at Q4_K_M instead. |
| IQ3_XS | 1068.0 GB | 0.4 GB | 1069.2 GB | Aggressive. Usually better than a smaller model at Q4, but test before trusting it. |
How these numbers are produced
- Model shape comes from the model's own config.json — 24 layers, 96 attention heads, 1 KV heads.
- Weight size is parameters × effective bits-per-weight. Those constants are checked against real published quantised file sizes — median error 0.7% across 40 measurements.
- KV cache is 2 × layers × KV-heads × head-dim × context × 2 bytes. Using KV-heads rather than attention heads is what makes this correct for grouped-query attention; treating a GQA model as multi-head overstates the cache by up to 8×.
- Tokens/sec is an estimate, not a benchmark. Generation is memory-bandwidth-bound, so this is bandwidth ÷ weight-bytes derated to 75%. Real throughput depends on your runtime, batch size and kernels. We don't run our own hardware tests — see our editorial policy.
- This is a mixture-of-experts model. Generation reads only the experts active for each token (16 of 896), so throughput is scored on ~104.0B active parameters, not the full 2780B. Memory is the opposite: every expert must still be resident in VRAM, so the figures above use the full weight set.Hybrid attention: 93 layers = 69 KDA (Kimi Delta Attention, linear: a fixed-size state, not a growing cache) + 24 gated MLA full-attention layers (model card). `layers`/kv_heads/head_dim = the 24 MLA layers: kv_lora_rank 512 + qk_rope_head_dim 64 = 576 cached numbers per token per layer, mapped as kv_heads 1 x head_dim 288 x 2 = 576 (same convention as GLM-5.3). KDA state is fixed, about 69 layers x 96 heads x 128x128 = ~0.2 GB per sequence at bf16, not counted. Active = vendor 104B; params = HF safetensors total (~2.78T; card says 2.8T), routed experts ship in MXFP4 (~1.56 TB of files); includes the vision encoder. Release date = first public commit on HF (27 Jul 2026; the repo itself was created 13 Jun). HF config.json + model card read 29 Oct 2026.
- Rental prices are pulled hourly from provider APIs (last updated Thu, 29 Oct 2026 04:20:06 GMT). Spot/interruptible pricing can change or vanish without notice.
Try it interactively
Drag the context slider and watch the KV cache fill the card
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