Kimi K2 Instruct (1T-A32B) on A800 80GB SXM (80GB) — VRAM, KV Cache & Throughput

Kimi K2 Instruct (1T-A32B) (1000B total, 32B active, Multi-head Latent Attention (MLA) MoE) uses 565.08 GiB of 4-bit weights (Q4_K_M), requiring 566.19 GiB total at 8K context and 567.79 GiB at 32K on A800 80GB SXM (~3.2 tok/s decode).

Interactive Sizing Workbench

Full Compatibility Report →

Open-Weight Model1000B / 32B

Weights & QuantizationQ4_K_M · 565.08 GiB

Context Window8,192 (8K)

Target Hardware

80 GB · 2039 GB/s

GPU Count (Tensor Parallel)

Concurrent Batch Size

KV Cache Precision

Inference Engine

System RAM Offload (CPU)0%

Exceeds Available GPU Memory

Config-derived formula HF config.json ↗

Memory Allocation Breakdown (GiB) 566.19 GiB (607.94 GB) / 80 GiB (708%)

Model Weights: 565.08 GiB KV Cache & State: 0.54 GiB Runtime & Activation Buffers: 0.57 GiB System RAM Offload: 0 GiB

Required GPU Memory

566.19 GiB

607.94 GB (decimal)

Usable Hardware Memory

76 GiB

80 GB (physical)

Safe Headroom

-490.19 GiB

708% utilization

Est. Decode Throughput

~3.2 tok/s

32B active params

Est. Prefill Throughput

~2535 tok/s

4K Prompt TTFT: ~1616 ms

Est. Cost / 1M Tokens

$306.94

Multi-head Latent Attention (MLA) MoE · Modified MIT

Recommended Hardware for Current Configuration

Best Single GPU

Multi-GPU Required

—

Best Value / Used GPU

4x B200 180GB

720GB · ~$160,000

Apple Unified Memory

Exceeds 512 GB

—

Cloud From

Vast.ai · $3.54 / hr

8x A100 80GB SXM

Production Launch Commands

llama.cpp (GGUF)

llama-server -hf unsloth/Kimi-K2-Instruct-GGUF:Q4_K_M \
  --ctx-size 8192 -ngl 999 --flash-attn

vLLM

vllm serve moonshotai/Kimi-K2-Instruct \
  --max-model-len 8192 --gpu-memory-utilization 0.92

Live Token Speed Simulator (TTFT ~1616ms · ~3.2 tok/s)

[Kimi K2 Instruct (1T-A32B) · 566.19 GiB] allocated on target GPU. Prefill completed in TTFT window; streaming autoregressive tokens at memory-bandwidth-bound rate...

Cloud GPU Comparison & Rent-vs-Buy TCO

Verified public rates across RunPod, Vast.ai, Vultr, and DigitalOcean.

Verified: 2026-11-04

Active usage (hours / day)4 h / day

Local hardware budget ($)

Electricity rate ($/kWh)

Purchase break-even

~10 months

Est. local power: $8.51 / mo

Provider	Recommended Instance	Total VRAM	Hourly Rate	Monthly at Selected Hours	Billing Mode	Deploy
RunPod	8x A100 80GB SXM (Community Cloud · PCIe)	640 GB	$9.52 / hr	$1157.63 / mo	Per-second	Deploy GPU ↗
Vast.ai	8x A100 80GB SXM (Verified Host · SXM4)	640 GB	$3.54 / hr	$429.98 / mo	Spot / On-demand	Deploy GPU ↗
*Vultr ($300 Free Credit)**	8x A100 80GB SXM (Dedicated Instance · $300 Credit*)	640 GB	$19.18 / hr	$2331.80 / mo	Hourly	Deploy GPU ↗
Vultr Cloud GPU	8x A100 80GB SXM (Dedicated Instance)	640 GB	$19.18 / hr	$2331.80 / mo	Hourly	Deploy GPU ↗
DigitalOcean	8x Instinct MI300X 192GB (Dedicated Instance)	1536 GB	$20.72 / hr	$2519.55 / mo	Per-second	Deploy GPU ↗

Rates reflect public listings on the verification date and fluctuate with regional inventory.

Fine-Tuning VRAM Requirements (QLoRA / LoRA / Full) — Kimi K2 Instruct (1T-A32B)

QLoRA (4b) Exceeds Available GPU Memory

570.73 GiB

Model Weights: 565.08 GiB · Gradients + Optimizer: 4.48 GiB · Act: 1.17 GiB

Recommended GPU: 4x B200 180GB (720GB)

LoRA (16b) Exceeds Available GPU Memory

1871.21 GiB

Model Weights: 1862.6 GiB · Gradients + Optimizer: 7.04 GiB · Act: 1.57 GiB

Recommended GPU: Multi-GPU Required

Full BF16 Exceeds Available GPU Memory

7639.43 GiB

Model Weights: 1862.6 GiB · Gradients + Optimizer: 5774.06 GiB · Act: 2.77 GiB

Recommended GPU: Multi-GPU Required

Engineering Sizing Notes & Optimization Playbook

Quick Hardware Verdict

A single A800 80GB SXM (80GB) cannot hold 4-bit Kimi K2 Instruct (1T-A32B) (566.19 GiB at 8K) entirely in VRAM. Use multi-GPU tensor parallelism, offload layers to system RAM, or deploy on a larger cloud GPU.

KV Cache & Runtime Tuning

Kimi K2 Instruct (1T-A32B) uses Multi-head Latent Attention (MLA) MoE (1000B total, 32B active per token). Moving from 8K to 32K context expands FP16 KV cache from 0.54 GiB to 2.14 GiB; enabling 8-bit KV cache (llama.cpp -ctk q8_0 -ctv q8_0 or vLLM --kv-cache-dtype fp8) saves 1.07 GiB at 32K (reducing total VRAM to 566.72 GiB).

VRAM Scaling Across Context Windows (4K to 128K, 4-bit Weights)

Model weights remain constant while attention KV cache grows with context length and batch size.

Quantization Precision & Memory Matrix — Kimi K2 Instruct (1T-A32B) @ 8K

Quantization Format	Weights (GiB)	KV Cache	Total VRAM (GiB)	Decode Speed	Compatibility (A800 80GB SXM)	Recommended GPU	Data Source
Native Weights (BF16 / FP8 / MXFP4 — Lossless) (FP8)	1862.6 GiB	0.54 GiB	1863.71 GiB (2001.14 GB)	~0.9 tok/s	Exceeds Available GPU Memory	Multi-GPU Required	Official checkpoint size
FP8 / GGUF Q8_0 (8-bit — Near-Lossless) (Q8_0)	989.53 GiB	0.54 GiB	990.64 GiB (1063.69 GB)	~1.7 tok/s	Exceeds Available GPU Memory	Multi-GPU Required	Config-derived formula
GGUF Q6_K (6-bit — High Precision) (Q6_K)	763.68 GiB	0.54 GiB	764.79 GiB (821.19 GB)	~2.3 tok/s	Exceeds Available GPU Memory	Multi-GPU Required	Config-derived formula
GGUF Q5_K_M (5-bit — Balanced) (Q5_K_M)	662.64 GiB	0.54 GiB	663.75 GiB (712.7 GB)	~2.7 tok/s	Exceeds Available GPU Memory	Multi-GPU Required	Config-derived formula
Q4_K_M / UD-Q4_K_XL / AWQ (4-bit — Recommended) (Q4_K_M)	565.08 GiB	0.54 GiB	566.19 GiB (607.94 GB)	~3.2 tok/s	Exceeds Available GPU Memory	Multi-GPU Required	Config-derived formula
GGUF IQ4_XS (4.25-bit — Compact) (IQ4_XS)	495.11 GiB	0.54 GiB	496.22 GiB (532.81 GB)	~3.7 tok/s	Exceeds Available GPU Memory	Multi-GPU Required	Config-derived formula
GGUF Q3_K_M (3-bit — Low VRAM) (Q3_K_M)	455.48 GiB	0.54 GiB	456.59 GiB (490.26 GB)	~4 tok/s	Exceeds Available GPU Memory	Multi-GPU Required	Config-derived formula
GGUF UD-Q2_K_XL (2-bit — Extreme MoE) (UD-Q2_K_XL)	320.62 GiB	0.54 GiB	321.73 GiB (345.45 GB)	~6.1 tok/s	Exceeds Available GPU Memory	Multi-GPU Required	Config-derived formula

Technical Specifications & FAQ

Does Kimi K2 Instruct (1T-A32B) fit on A800 80GB SXM (80GB)?

In 4-bit (Q4_K_M) at 8,192 context, Kimi K2 Instruct (1T-A32B) requires 566.19 GiB total GPU memory (565.08 GiB weights + 0.54 GiB KV cache + 0.57 GiB runtime buffers) against 76 GiB usable on A800 80GB SXM, delivering ~3.2 tok/s decode.

How much VRAM does Kimi K2 Instruct (1T-A32B) need at 32K context?

At 32,768 context tokens, Kimi K2 Instruct (1T-A32B) (Multi-head Latent Attention (MLA) MoE) allocates 2.14 GiB for KV cache and state, bringing total 4-bit VRAM to 567.79 GiB (609.66 GB decimal).

Evaluate This Model on Other GPUs

RTX 3060 12GB (12GB)RTX 4060 Ti 16GB (16GB)RTX 4070 Ti Super (16GB)RTX 4080 Super (16GB)RTX 5070 12GB (12GB)RTX 5070 Ti (16GB)RTX 5080 (16GB)RTX 3090 (24GB)RTX 4090 (24GB)RTX 4090 D (24GB)RTX 5090 (32GB)RTX 5090 D (32GB)RTX A6000 (48GB)RTX 6000 Ada (48GB)RTX PRO 6000 Blackwell (96GB)DGX Spark (GB10 128GB) (128GB)L4 24GB (24GB)A40 48GB (48GB)L20 48GB (48GB)L40S 48GB (48GB)A100 80GB SXM (80GB)H800 80GB SXM (80GB)H100 80GB SXM (80GB)H20 96GB HBM3 (96GB)H20 141GB HBM3e (141GB)H200 141GB SXM (141GB)B200 180GB (180GB)B300 288GB Blackwell Ultra (288GB)Ascend 910B 64GB (昇腾) (64GB)Ascend 910C 128GB (昇腾) (128GB)Atlas 300I Duo 96GB (310P) (96GB)MLU590 80GB (寒武纪思元) (80GB)DCU K100-AI 64GB (海光深算) (64GB)MetaX C500 64GB (沐曦曦云) (64GB)Kunlunxin P800 64GB (昆仑芯) (64GB)MTT S4000 48GB (摩尔线程) (48GB)Radeon RX 7900 XTX 24GB (24GB)Radeon AI PRO R9700 32GB (32GB)Ryzen AI Max+ 395 128GB (128GB)Instinct MI300X 192GB (192GB)Instinct MI325X 256GB (256GB)Instinct MI355X 288GB (288GB)Arc Pro B60 24GB (24GB)Gaudi 3 128GB HBM2e (128GB)Mac mini M4 Pro 64GB (64GB)MacBook Pro M4 Max 128GB (128GB)Mac Studio M4 Ultra 256GB (256GB)Mac Studio M3 Ultra 512GB (512GB)

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