Models / MiniMax M2.7

MiniMax M2.7

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The MiniMax workhorse that quietly passed a million downloads. M2.7 (April 2026) is the generation before M3: 62 layers, 256 routed experts with 8 active per token, GQA 8x128 attention, 200K context, ~115B total parameters in 230GB of bf16 weights, text-only. 1.14 million downloads on Hugging Face against M3’s 168k says which one the local crowd actually runs.

What it is for. Coding and agent work at the 96GB tier: the unsloth Dynamic quants put UD-Q4_K_XL at 140.8GB (512GB-class) and UD-Q2_K_XL at 75.3GB, which fits an M5 Ultra 96GB or a single RTX PRO 6000 box with KV headroom (KV runs 254MB per 1,000 tokens at fp16). Nothing in the M2.7 line clears a 32GB Mac or a 24GB 3090 for quality runs - the 1-bit UD-IQ1_M at 60.7GB is a degraded last resort, and a 64GB M4 Max cannot hold it with cache.

The ecosystem is real: unsloth, bartowski, and mudler all ship GGUF sets, including an abliterated community variant. If you want the current-generation upgrade path, M3 adds multimodal input, 1M context via sparse attention, and 4x the experts - at 4x the memory.

coding agentic
Context
200k
License
minimax community
Developer
MiniMax
Released
Apr 2026

Related models

MiniMax M3
premier 128GB min RAM Jun 2, 2026

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Run it locally

Per-quant memory needs and a static "can you run it?" reference - no rig entry required

UD-IQ1_M
60.7GB 60.7GB min 88.0GB rec
Quantized build
UD-Q2_K_XL
75.3GB 75.3GB min 96.0GB rec
Unsloth Dynamic 2-bit XL - best quality-per-GB in the Dynamic line
UD-Q4_K_XL
140.8GB 140.8GB min 168.0GB rec
Unsloth Dynamic 4-bit XL - lossless at Q4 size

The reference hardware

Schematic of the NVIDIA Jetson Orin NX 16GB reference rig - 16GB unified memory, 102 GB/s aggregate bandwidth
Schematic of the Jetson AGX Orin 64GB reference rig - 64GB unified memory, 204 GB/s aggregate bandwidth
Schematic of the Single GTX 1080 Ti (11GB) reference rig - 11GB VRAM, 484 GB/s aggregate bandwidth
Schematic of the Single RTX 4090 (24GB) reference rig - 24GB VRAM, 1008 GB/s aggregate bandwidth
Schematic of the Single RTX 5090 (32GB) reference rig - 32GB VRAM, 1792 GB/s aggregate bandwidth
Schematic of the 2x RTX 3090 (48GB) reference rig - 48GB VRAM, 1872 GB/s aggregate bandwidth
Schematic of the 2x RTX 5090 (64GB) reference rig - 64GB VRAM, 3584 GB/s aggregate bandwidth
Schematic of the 4x H100 80GB (320GB) reference rig - 320GB VRAM, 13400 GB/s aggregate bandwidth
Schematic of the NVIDIA DGX Station 748GB reference rig - 748GB unified memory, 8000 GB/s aggregate bandwidth
Schematic of the 8x RTX 3090 rack (192GB) reference rig - 192GB VRAM, 7489 GB/s aggregate bandwidth
Schematic of the 4x RTX 5090 (128GB) reference rig - 128GB VRAM, 7168 GB/s aggregate bandwidth
Schematic of the AMD Instinct MI300X (192GB) reference rig - 192GB VRAM, 5324 GB/s aggregate bandwidth
Schematic of the 4x RTX 4090 (96GB) reference rig - 96GB VRAM, 4032 GB/s aggregate bandwidth
Schematic of the RTX PRO 6000 Blackwell (96GB) reference rig - 96GB VRAM, 1792 GB/s aggregate bandwidth
Schematic of the Mac Studio M4 Ultra 192GB reference rig - 192GB unified memory, 1092 GB/s aggregate bandwidth
Schematic of the Mac Studio M4 Ultra 512GB reference rig - 512GB unified memory, 1092 GB/s aggregate bandwidth
Schematic of the MacBook Pro M5 Max 128GB reference rig - 128GB unified memory, 614 GB/s aggregate bandwidth
Schematic of the Dual EPYC 9004 + 768GB DDR5-4800 reference rig - 768GB unified memory, 460 GB/s aggregate bandwidth
Schematic of the DGX Spark 128GB unified reference rig - 128GB unified memory, 273 GB/s aggregate bandwidth
Schematic of the Ryzen AI Max+ 395 128GB reference rig - 128GB unified memory, 256 GB/s aggregate bandwidth
Schematic of the Epyc + 512GB DDR4-3200 + 2x RTX 3090 reference rig - 560GB unified memory, 204 GB/s aggregate bandwidth
Schematic of the Epyc + 512GB DDR4-2400 + 2x RTX 3090 reference rig - 560GB unified memory, 153 GB/s aggregate bandwidth

22 reference configs, drawn in-house. Scroll for more.

Can you run it? - reference rigs

Rig UD-IQ1_M UD-Q2_K_XL UD-Q4_K_XL
NVIDIA Jetson Orin NX 16GB no -> cloud no -> cloud no -> cloud
Jetson AGX Orin 64GB tight no -> cloud no -> cloud
Single GTX 1080 Ti (11GB) no -> cloud no -> cloud no -> cloud
Single RTX 4090 (24GB) no -> cloud no -> cloud no -> cloud
Single RTX 5090 (32GB) no -> cloud no -> cloud no -> cloud
2x RTX 3090 (48GB) offload offload no -> cloud
2x RTX 5090 (64GB) tight offload no -> cloud
4x H100 80GB (320GB) fast 119.4t/s fast 96.6t/s fast 52.0t/s
NVIDIA DGX Station 748GB fast 71.3t/s fast 57.7t/s fast 31.0t/s
8x RTX 3090 rack (192GB) fast 66.8t/s fast 54.0t/s fast 29.1t/s
4x RTX 5090 (128GB) fast 63.9t/s fast 51.7t/s offload
AMD Instinct MI300X (192GB) fast 47.5t/s fast 38.4t/s fast 20.7t/s
4x RTX 4090 (96GB) fast 35.9t/s fast 29.1t/s offload
RTX PRO 6000 Blackwell (96GB) ok 16.0t/s ok 12.9t/s offload
Mac Studio M4 Ultra 192GB ok 10.6t/s ok 8.6t/s slow 4.6t/s
Mac Studio M4 Ultra 512GB ok 10.6t/s ok 8.6t/s slow 4.6t/s
MacBook Pro M5 Max 128GB slow 6.0t/s slow 4.8t/s no -> cloud
Dual EPYC 9004 + 768GB DDR5-4800 slow 4.1t/s slow 3.3t/s slow 1.8t/s
DGX Spark 128GB unified slow 2.4t/s slow 2.0t/s no -> cloud
Ryzen AI Max+ 395 128GB slow 2.3t/s slow 1.8t/s no -> cloud
Epyc + 512GB DDR4-3200 + 2x RTX 3090 slow 1.8t/s slow 1.5t/s slow 0.8t/s
Epyc + 512GB DDR4-2400 + 2x RTX 3090 slow 1.4t/s slow 1.1t/s slow 0.6t/s

Fit tiers use the same will-it-run logic as the rig finder. For comfortable fits, the badge reflects decode speed: fast >=20 t/s, ok 8-20 t/s, slow <8 t/s. t/s is a bandwidth estimate, not a measured benchmark.

Download options

UD-IQ1_M Unsloth - local-optimized
60.7GB dl 60.7GB min 88.0GB rec
REC RAM vs largest quant
60.7GB unsloth 1-bit weights (UD-IQ1_M, file sum of 4 shards) + KV; degraded, ~88GB to run with working cache
GGUF on HF →
UD-Q2_K_XL Unsloth - local-optimized
75.3GB dl 75.3GB min 96.0GB rec
REC RAM vs largest quant
75.3GB unsloth Dynamic 2-bit weights (UD-Q2_K_XL, file sum of 4 shards) + KV at 254MB per 1k tokens; ~96GB to run
GGUF on HF →
UD-Q4_K_XL Unsloth - local-optimized
140.8GB dl 140.8GB min 168.0GB rec
REC RAM vs largest quant
140.8GB unsloth Dynamic 4-bit weights (UD-Q4_K_XL, file sum of 4 shards) + KV; ~168GB to run
GGUF on HF →

Or run it in the cloud

No per-token API provider pricing tracked for MiniMax M2.7 yet. For flagship list prices, see the calculator.

PRICE HISTORY

Inference cost over time

Data accumulates from the first daily sync - longer ranges populate over time. Prices come from OpenRouter snapshots, not a historical API.

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