MiniCPM5 2B (面壁) on RTX 5090 D (32GB) — VRAM, KV Cache & Throughput

MiniCPM5 2B (面壁) (2.3B total, 2.3B active, Dense GQA) uses 1.41 GiB of 4-bit weights (Q4_K_M), requiring 2.3 GiB total at 8K context and 3.28 GiB at 32K on RTX 5090 D (~380 tok/s decode).

Interactive Sizing Workbench

Full Compatibility Report →

Open-Weight Model2.3B / 2.3B

Weights & QuantizationQ4_K_M · 1.41 GiB

Context Window8,192 (8K)

Target Hardware

32 GB · 1792 GB/s

GPU Count (Tensor Parallel)

Concurrent Batch Size

KV Cache Precision

Inference Engine

System RAM Offload (CPU)0%

Fits with Safe Headroom

Config-derived formula HF config.json ↗

Memory Allocation Breakdown (GiB) 2.3 GiB (2.47 GB) / 32 GiB (7%)

Model Weights: 1.41 GiB KV Cache & State: 0.33 GiB Runtime & Activation Buffers: 0.56 GiB System RAM Offload: 0 GiB

Required GPU Memory

2.3 GiB

2.47 GB (decimal)

Usable Hardware Memory

30.4 GiB

32 GB (physical)

Safe Headroom

+28.1 GiB

7% utilization

Est. Decode Throughput

~380 tok/s

2.3B active params

Est. Prefill Throughput

~20913 tok/s

4K Prompt TTFT: ~196 ms

Est. Cost / 1M Tokens

$0.04

Dense GQA · Apache-2.0

Recommended Hardware for Current Configuration

Best Single GPU

RTX 4060 Ti 16GB (16GB)

~$430

Best Value / Used GPU

RTX 3060 12GB (12GB)

~$280

Apple Unified Memory

Ryzen AI Max+ 395 128GB

~$1,999

Cloud From

Vast.ai · $0.06 / hr

1x RTX 3060 12GB

Production Launch Commands

llama.cpp (GGUF)

llama-server -hf openbmb/MiniCPM5-2B-GGUF:Q4_K_M \
  --ctx-size 8192 -ngl 999 --flash-attn

vLLM

vllm serve openbmb/MiniCPM5-2B \
  --max-model-len 8192 --gpu-memory-utilization 0.92

Live Token Speed Simulator (TTFT ~196ms · ~380 tok/s)

[MiniCPM5 2B (面壁) · 2.3 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

~18.5 months

Est. local power: $3.51 / mo

Provider	Recommended Instance	Total VRAM	Hourly Rate	Monthly at Selected Hours	Billing Mode	Deploy
RunPod	1x RTX 3090 (Community Cloud)	24 GB	$0.22 / hr	$26.75 / mo	Per-second	Deploy GPU ↗
Vast.ai	1x RTX 3060 12GB (Verified Host)	12 GB	$0.06 / hr	$7.42 / mo	Spot / On-demand	Deploy GPU ↗
*Vultr ($300 Free Credit)**	1x L40S 48GB (Dedicated Instance · $300 Credit*)	48 GB	$1.67 / hr	$203.19 / mo	Hourly	Deploy GPU ↗
Vultr Cloud GPU	1x L40S 48GB (Dedicated Instance)	48 GB	$1.67 / hr	$203.19 / mo	Hourly	Deploy GPU ↗
DigitalOcean	1x RTX 6000 Ada (Dedicated Instance)	48 GB	$1.57 / hr	$190.91 / 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) — MiniCPM5 2B (面壁)

QLoRA (4b) Fits with Safe Headroom

3.62 GiB

Model Weights: 1.41 GiB · Gradients + Optimizer: 1.2 GiB · Act: 1.01 GiB

Recommended GPU: RTX 4060 Ti 16GB (16GB)

LoRA (16b) Fits with Safe Headroom

7.42 GiB

Model Weights: 4.28 GiB · Gradients + Optimizer: 1.8 GiB · Act: 1.34 GiB

Recommended GPU: RTX 4060 Ti 16GB (16GB)

Full BF16 Fits with Safe Headroom

19.91 GiB

Model Weights: 4.28 GiB · Gradients + Optimizer: 13.27 GiB · Act: 2.36 GiB

Recommended GPU: Arc Pro B60 24GB (24GB)

Engineering Sizing Notes & Optimization Playbook

Quick Hardware Verdict

RTX 5090 D (32GB physical, 30.4 GiB usable) runs 4-bit (Q4_K_M) MiniCPM5 2B (面壁) at 8K context using 2.3 GiB with 28.1 GiB of safe headroom (~380 tok/s single-stream decode).

KV Cache & Runtime Tuning

MiniCPM5 2B (面壁) uses Dense GQA (2.3B total, 2.3B active per token). Moving from 8K to 32K context expands FP16 KV cache from 0.33 GiB to 1.31 GiB; enabling 8-bit KV cache (llama.cpp -ctk q8_0 -ctv q8_0 or vLLM --kv-cache-dtype fp8) saves 0.65 GiB at 32K (reducing total VRAM to 2.63 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 — MiniCPM5 2B (面壁) @ 8K

Quantization Format	Weights (GiB)	KV Cache	Total VRAM (GiB)	Decode Speed	Compatibility (RTX 5090 D)	Recommended GPU	Data Source
Native Weights (BF16 / FP8 / MXFP4 — Lossless) (BF16)	4.28 GiB	0.33 GiB	5.17 GiB (5.55 GB)	~211.6 tok/s	Fits with Safe Headroom	RTX 4060 Ti 16GB (16GB)	Official checkpoint size
FP8 / GGUF Q8_0 (8-bit — Near-Lossless) (Q8_0)	2.28 GiB	0.33 GiB	3.17 GiB (3.4 GB)	~341.7 tok/s	Fits with Safe Headroom	RTX 4060 Ti 16GB (16GB)	Config-derived formula
GGUF Q6_K (6-bit — High Precision) (Q6_K)	1.76 GiB	0.33 GiB	2.65 GiB (2.85 GB)	~380 tok/s	Fits with Safe Headroom	RTX 4060 Ti 16GB (16GB)	Config-derived formula
GGUF Q5_K_M (5-bit — Balanced) (Q5_K_M)	1.58 GiB	0.33 GiB	2.47 GiB (2.65 GB)	~380 tok/s	Fits with Safe Headroom	RTX 4060 Ti 16GB (16GB)	Config-derived formula
Q4_K_M / UD-Q4_K_XL / AWQ (4-bit — Recommended) (Q4_K_M)	1.41 GiB	0.33 GiB	2.3 GiB (2.47 GB)	~380 tok/s	Fits with Safe Headroom	RTX 4060 Ti 16GB (16GB)	Config-derived formula
GGUF IQ4_XS (4.25-bit — Compact) (IQ4_XS)	1.22 GiB	0.33 GiB	2.11 GiB (2.27 GB)	~380 tok/s	Fits with Safe Headroom	RTX 4060 Ti 16GB (16GB)	Config-derived formula
GGUF Q3_K_M (3-bit — Low VRAM) (Q3_K_M)	1.11 GiB	0.33 GiB	2 GiB (2.15 GB)	~380 tok/s	Fits with Safe Headroom	RTX 4060 Ti 16GB (16GB)	Config-derived formula
GGUF UD-Q2_K_XL (2-bit — Extreme MoE) (UD-Q2_K_XL)	0.85 GiB	0.33 GiB	1.74 GiB (1.87 GB)	~380 tok/s	Fits with Safe Headroom	RTX 4060 Ti 16GB (16GB)	Config-derived formula

Technical Specifications & FAQ

Does MiniCPM5 2B (面壁) fit on RTX 5090 D (32GB)?

In 4-bit (Q4_K_M) at 8,192 context, MiniCPM5 2B (面壁) requires 2.3 GiB total GPU memory (1.41 GiB weights + 0.33 GiB KV cache + 0.56 GiB runtime buffers) against 30.4 GiB usable on RTX 5090 D, delivering ~380 tok/s decode.

How much VRAM does MiniCPM5 2B (面壁) need at 32K context?

At 32,768 context tokens, MiniCPM5 2B (面壁) (Dense GQA) allocates 1.31 GiB for KV cache and state, bringing total 4-bit VRAM to 3.28 GiB (3.52 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 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)A800 80GB SXM (80GB)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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