MiniMax M2.5 (229B-A10B) on RTX 4090 D (24GB) — VRAM, KV Cache & Throughput
MiniMax M2.5 (229B-A10B) (228.69B total, 10B active, Sparse MoE + GQA) uses 128.84 GiB of 4-bit weights (Q4_K_M), requiring 131.34 GiB total at 8K context and 137.15 GiB at 32K on RTX 4090 D (~9.1 tok/s decode).
Exceeds Available GPU Memory
Measured GGUF shard size
HF config.json ↗
Required GPU Memory
131.34 GiB
141.03 GB (decimal)
Usable Hardware Memory
22.8 GiB
24 GB (physical)
Safe Headroom
-108.54 GiB
547% utilization
Est. Decode Throughput
~9.1 tok/s
10B active params
Est. Prefill Throughput
~3770 tok/s
4K Prompt TTFT: ~1086 ms
Est. Cost / 1M Tokens
$79.06
Sparse MoE + GQA · Custom
Recommended Hardware for Current Configuration
Best Single GPU
Instinct MI300X 192GB (192GB)
~$15,000
Best Value / Used GPU
2x A100 80GB SXM
160GB · ~$24,000
Apple Unified Memory
Mac Studio M4 Ultra 256GB
~$5,999
Cloud From
DigitalOcean · $2.59 / hr
1x Instinct MI300X 192GB
Production Launch Commands
llama.cpp (GGUF)
llama-server -hf unsloth/MiniMax-M2.5-GGUF:Q4_K_M \ --ctx-size 8192 -ngl 999 --flash-attn
vLLM
vllm serve MiniMaxAI/MiniMax-M2.5 \ --max-model-len 8192 --gpu-memory-utilization 0.92
Live Token Speed Simulator (TTFT ~1086ms · ~9.1 tok/s)
[MiniMax M2.5 (229B-A10B) · 131.34 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.
Purchase break-even
~21.1 months
Est. local power: $15.96 / mo
| Provider | Recommended Instance | Total VRAM | Hourly Rate | Monthly at Selected Hours | Billing Mode | Deploy |
|---|---|---|---|---|---|---|
| RunPod | 1x B200 180GB (Community Cloud) | 180 GB | $5.98 / hr | $727.17 / mo | Per-second | Deploy GPU ↗ |
| Vast.ai | 1x B200 180GB (Verified Host) | 180 GB | $7.50 / hr | $912.24 / mo | Spot / On-demand | Deploy GPU ↗ |
| Vultr ($300 Free Credit*) | 4x L40S 48GB (Dedicated Instance · $300 Credit*) | 192 GB | $6.68 / hr | $812.77 / mo | Hourly | Deploy GPU ↗ |
| Vultr Cloud GPU | 4x L40S 48GB (Dedicated Instance) | 192 GB | $6.68 / hr | $812.77 / mo | Hourly | Deploy GPU ↗ |
| DigitalOcean | 1x Instinct MI300X 192GB (Dedicated Instance) | 192 GB | $2.59 / hr | $314.94 / 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) — MiniMax M2.5 (229B-A10B)
QLoRA (4b)
Exceeds Available GPU Memory
131.28 GiB
Model Weights: 128.84 GiB · Gradients + Optimizer: 1.4 GiB · Act: 1.04 GiB
Recommended GPU: Instinct MI300X 192GB (192GB)
LoRA (16b)
Exceeds Available GPU Memory
429.66 GiB
Model Weights: 426.07 GiB · Gradients + Optimizer: 2.2 GiB · Act: 1.39 GiB
Recommended GPU: 4x H200 141GB SXM (564GB)
Full BF16
Exceeds Available GPU Memory
1749.33 GiB
Model Weights: 426.07 GiB · Gradients + Optimizer: 1320.82 GiB · Act: 2.44 GiB
Recommended GPU: Multi-GPU Required
Engineering Sizing Notes & Optimization Playbook
Quick Hardware Verdict
A single RTX 4090 D (24GB) cannot hold 4-bit MiniMax M2.5 (229B-A10B) (131.34 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
MiniMax M2.5 (229B-A10B) uses Sparse MoE + GQA (228.69B total, 10B active per token). Moving from 8K to 32K context expands FP16 KV cache from 1.94 GiB to 7.75 GiB; enabling 8-bit KV cache (llama.cpp -ctk q8_0 -ctv q8_0 or vLLM --kv-cache-dtype fp8) saves 3.87 GiB at 32K (reducing total VRAM to 133.28 GiB).
VRAM Scaling Across Context Windows (4K to 128K, 4-bit Weights)
Quantization Precision & Memory Matrix — MiniMax M2.5 (229B-A10B) @ 8K
| Quantization Format | Weights (GiB) | KV Cache | Total VRAM (GiB) | Decode Speed | Compatibility (RTX 4090 D) | Recommended GPU | Data Source |
|---|---|---|---|---|---|---|---|
| Native Weights (BF16 / FP8 / MXFP4 — Lossless) (FP8) | 426.07 GiB | 1.94 GiB | 428.57 GiB (460.17 GB) | ~2.8 tok/s | Exceeds Available GPU Memory | Multi-GPU Required | Official checkpoint size |
| FP8 / GGUF Q8_0 (8-bit — Near-Lossless) (Q8_0) | 226.44 GiB | 1.94 GiB | 228.94 GiB (245.82 GB) | ~5.2 tok/s | Exceeds Available GPU Memory | Instinct MI325X 256GB (256GB) | Measured GGUF shard size |
| GGUF Q6_K (6-bit — High Precision) (Q6_K) | 174.87 GiB | 1.94 GiB | 177.37 GiB (190.45 GB) | ~6.7 tok/s | Exceeds Available GPU Memory | Instinct MI325X 256GB (256GB) | Measured GGUF shard size |
| GGUF Q5_K_M (5-bit — Balanced) (Q5_K_M) | 151.16 GiB | 1.94 GiB | 153.66 GiB (164.99 GB) | ~7.7 tok/s | Exceeds Available GPU Memory | Instinct MI300X 192GB (192GB) | Measured GGUF shard size |
| Q4_K_M / UD-Q4_K_XL / AWQ (4-bit — Recommended) (Q4_K_M) | 128.84 GiB | 1.94 GiB | 131.34 GiB (141.03 GB) | ~9.1 tok/s | Exceeds Available GPU Memory | Instinct MI300X 192GB (192GB) | Measured GGUF shard size |
| GGUF IQ4_XS (4.25-bit — Compact) (IQ4_XS) | 113.53 GiB | 1.94 GiB | 116.03 GiB (124.59 GB) | ~10.3 tok/s | Exceeds Available GPU Memory | Instinct MI300X 192GB (192GB) | Measured GGUF shard size |
| GGUF Q3_K_M (3-bit — Low VRAM) (Q3_K_M) | 101.77 GiB | 1.94 GiB | 104.27 GiB (111.96 GB) | ~11.5 tok/s | Exceeds Available GPU Memory | Instinct MI300X 192GB (192GB) | Measured GGUF shard size |
| GGUF UD-Q2_K_XL (2-bit — Extreme MoE) (UD-Q2_K_XL) | 80.02 GiB | 1.94 GiB | 82.52 GiB (88.61 GB) | ~14.8 tok/s | Exceeds Available GPU Memory | RTX PRO 6000 Blackwell (96GB) | Measured GGUF shard size |
Technical Specifications & FAQ
Does MiniMax M2.5 (229B-A10B) fit on RTX 4090 D (24GB)?
In 4-bit (Q4_K_M) at 8,192 context, MiniMax M2.5 (229B-A10B) requires 131.34 GiB total GPU memory (128.84 GiB weights + 1.94 GiB KV cache + 0.56 GiB runtime buffers) against 22.8 GiB usable on RTX 4090 D, delivering ~9.1 tok/s decode.
How much VRAM does MiniMax M2.5 (229B-A10B) need at 32K context?
At 32,768 context tokens, MiniMax M2.5 (229B-A10B) (Sparse MoE + GQA) allocates 7.75 GiB for KV cache and state, bringing total 4-bit VRAM to 137.15 GiB (147.26 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 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)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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