Kimi-K2-Instruct GPU Requirements: VRAM & Cheapest GPU
Kimi-K2-Instruct has about 1026B parameters. See exactly how much GPU memory it needs at FP16, INT8, and INT4, and the cheapest GPU to run it, with live hourly pricing from 5+ data center partners.
- Parameters
- 1026B
- Min VRAM (INT4)
- 559 GB
- Cheapest (INT4)
- $9.52/hr
- Deploy
- < 2 min
To run Kimi-K2-Instruct for inference at FP16, you need roughly 2238 GB of VRAM. The cheapest fit on Spheron AI is 8x B300 288GB at about $59.76/hr. Quantize to INT4 to run it on a smaller, cheaper GPU.
VRAM required
Estimated peak VRAM including weights, activations, and KV cache. Add 10% headroom for production traffic.
- 01$7.47/hr$59.76/hr8× B300 288GBCHEAPESTBlackwell Ultra · HBM3e
Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.
VRAM required to run Kimi-K2-Instruct
Estimated peak VRAM at context length 4,096 and batch size 1, including weights, activations, and KV cache. Quantizing to INT8 (Q8) or INT4 (Q4) cuts memory roughly in half and in quarter.
| Precision | Inference | LoRA fine-tune | Full fine-tune |
|---|---|---|---|
| FP16 | 2238 GB | 3357 GB | 8951 GB |
| INT8 | 1119 GB | 1678 GB | 4475 GB |
| INT4 | 559 GB | 839 GB | 2238 GB |
Cheapest GPU to run Kimi-K2-Instruct by precision
2238GB
Full precision. Best quality, highest memory.
1119GB
8-bit quantized. ~2x smaller, minimal quality loss.
559GB
4-bit quantized. ~4x smaller, runs on smaller GPUs.
Inference vs fine-tuning Kimi-K2-Instruct
Inference only holds the model weights plus a KV cache, so it is the cheapest setup. LoRA fine-tuning adds a small adapter and roughly 50% more memory. Full fine-tuning holds gradients and optimizer state on top of the weights, which is about 4x the inference footprint, so it often needs multiple GPUs even when inference fits on one. For Kimi-K2-Instruct, an on-demand B300 288GB instance covers inference and LoRA, while a full fine-tune needs several times that memory and often spans multiple GPUs. Check the live GPU pricing for current rates.
Similar models
Compare GPU requirements for models in the same class.
Kimi-K2-Instruct GPU questions
VRAM, GPU choice and cost questions for running Kimi-K2-Instruct on cloud GPUs.
Kimi-K2-Instruct has about 1026B parameters. At FP16 it needs roughly 2238 GB of VRAM for inference, including weights, activations, and KV cache. Quantized to INT4 that drops to around 559 GB. Leave about 10% headroom for production traffic.
For FP16 inference, the cheapest fit on Spheron AI is 8x B300 288GB at about $59.76/hr. If you quantize to INT4, 8x A100 80GB from $9.52/hr runs it for less. Pricing is aggregated live from 5+ data center partners with per-minute billing.
LoRA fine-tuning adds roughly 50% on top of inference memory, so it usually fits the same class of GPU. Full fine-tuning holds gradients and optimizer state and needs about 4x the inference VRAM, which often means multiple GPUs for Kimi-K2-Instruct. The VRAM matrix above shows the exact estimate for each setup.
We read the parameter count directly from the model's safetensors metadata on HuggingFace, then estimate peak VRAM from weights, activations, KV cache, and framework overhead at your chosen precision. The estimate lands within about 15% of real-world use for most transformer models.
Quantized to INT4, Kimi-K2-Instruct needs about 559 GB of VRAM, so it is too large for a single 24 GB RTX 4090 and needs a bigger card or multiple GPUs. At FP16 it needs roughly 2238 GB, which usually means a data center GPU. The precision picks above list the cheapest GPU that fits each setup.
No. Even quantized to INT4, Kimi-K2-Instruct needs about 559 GB of VRAM, so it takes 7 x 80 GB GPUs at minimum, and 28 x 80 GB GPUs at FP16. The precision picks above show the cheapest GPU that fits each setup on Spheron AI.
Kimi-K2-Instruct needs 28 x 80 GB GPUs at FP16 (about 2238 GB of VRAM), 14 x 80 GB GPUs at INT8 (1119 GB) and 7 x 80 GB GPUs at INT4 (559 GB). At FP16 that is more than one 8-GPU node, so plan for a multi-node cluster with a fast interconnect. Smaller cards such as 48 GB or 24 GB GPUs need proportionally more of them.
Kimi-K2-Instruct needs about 1119 GB of VRAM at INT8, roughly half its FP16 footprint of 2238 GB. The cheapest INT8 fit on Spheron AI is 4x B300 288GB at about $29.88/hr. INT8 is the usual middle ground: half the memory of FP16, with a smaller quality drop than 4-bit quantization.
Running Kimi-K2-Instruct around the clock at FP16 costs about $43,625 a month on 8x B300 288GB (730 hours at $59.76/hr). Quantized to INT4 it costs about $6,950 a month on 8x A100 80GB (730 hours at $9.52/hr at the spot rate, which can be reclaimed). Spheron AI bills per minute, so stopping the instance when it is idle cuts that figure directly.
Kimi-K2-Instruct needs about 2238 GB at FP16, 1119 GB at INT8 and 559 GB at INT4. Use FP16 when output quality matters most or you plan to fine-tune, INT8 for most production inference, and INT4 when cost or GPU size is the constraint: the cheapest INT4 fit is 8x A100 80GB from $9.52/hr.