MiniMax M2.7
MiniMax · moe · 228.7B parameters · 204,800 context
Parameters
228.7B
Context Window
200K tokens
Architecture
MoE
Best GPU
B200 SXM
Cheapest API
$1.20/M
Intelligence Brief
MiniMax M2.7 is a 228.7B parameter Mixture-of-Experts (256 experts, 8 active) model from MiniMax, featuring Grouped Query Attention (GQA) with 62 layers and 3,072 hidden dimensions. With a 204,800 token context window, it supports code. The most cost-effective API deployment is via openrouter at $1.20/M output tokens. For self-hosted inference, B200 SXM delivers optimal throughput at $8522/month.
Provider pricing
1 provider · canonical: openrouter| Provider | Input $/M | Output $/M ▲ | Notes |
|---|---|---|---|
| openroutercanonical | $0.300 | $1.20 | cheapest input · cheapest output |
Prices update via the nightly pricing cron + admin approvals at /admin/ingest-queue. The leaderboard's Input/Output cells show the canonical rate above; this table shows the full spread.
Recent changes
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Picks: same family first, then same vendor within ±2× params, then top tag-overlap matches. Price shown is the cheapest Output $/M across providers — the row's page shows the canonical anchor.
Architecture Details
Memory Requirements
BF16 Weights
426.0 GB
FP8 Weights
213.0 GB
INT4 Weights
106.5 GB
Fits on (single GPU) — most practical first
Fits on (multi-GPU with Tensor Parallelism)
Multi-GPU configurations use Tensor Parallelism (TP) to split model layers across GPUs. Requires NVLink or NVSwitch interconnect for optimal performance.
GPU Compatibility Matrix
MiniMax M2.7 is compatible with 8% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
FP8 · 2 GPUs · tensorrt-llm
100/100
score
Throughput
280.0 tok/s
Latency (ITL)
3.6ms
Est. TTFT
1ms
Cost/Month
$8522
Cost/M Tokens
$11.58
FP8 · 2 GPUs · tensorrt-llm
100/100
score
Throughput
280.0 tok/s
Latency (ITL)
3.6ms
Est. TTFT
1ms
Cost/Month
$8541
Cost/M Tokens
$11.61
FP8 · 2 GPUs · tensorrt-llm
100/100
score
Throughput
280.0 tok/s
Latency (ITL)
3.6ms
Est. TTFT
1ms
Cost/Month
$12337
Cost/M Tokens
$16.77
Deployment Options
API Deployment
openrouter
$1.20/M
output tokens
Single GPU
B200 NVL (pair)
$9965/mo
Min VRAM: 213 GB
Multi-GPU
B200 SXM x2
280.0 tok/s
TP· $8522/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| openrouter | $0.30 | $1.20 | Cheapest |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| openrouterBest Value | $0.30 | $1.20 | $8 |
Cost per 1,000 Requests
Short (500 tok)
$0.39
via openrouter
Medium (2K tok)
$1.56
via openrouter
Long (8K tok)
$4.80
via openrouter
Performance Estimates
Throughput by GPU
VRAM Breakdown (B200 SXM, FP8)
Precision Impact
bf16
228.7 GB
weights/GPU
fp8
114.3 GB
weights/GPU
~280.0 tok/s
int4
57.2 GB
weights/GPU
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy MiniMax M2.7
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does MiniMax M2.7 need for inference?
MiniMax M2.7 requires approximately 426.0 GB of VRAM at BF16 precision, 213.0 GB at FP8, or 106.5 GB at INT4 quantization. Additional VRAM is needed for KV-cache (253952 bytes per token) and activations (~0.00 GB).
What is the best GPU for MiniMax M2.7?
The top recommended GPU for MiniMax M2.7 is the B200 SXM (x2) using FP8 precision. It achieves approximately 280.0 tokens/sec at an estimated cost of $8522/month ($11.58/M tokens). Score: 100/100.
How much does MiniMax M2.7 inference cost?
MiniMax M2.7 API inference starts from $0.30/M input tokens and $1.20/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.