Arcee · Chat and reasoning · 399B parameters · 13B active · OpenMDW-1.1
Trinity Large Thinking
Needs 479 GB at FP8 / INT8. The smallest real configuration today is 2× MI325X at Cyfuture AI, $6.21 per hour, about $4,536 a month at 730 hours.
Parameters399B13B active per token
Context256k tokensmaximum published
Checkpoint797 GBBF16, as published
LicenseOpenMDW-1.1check before commercial use
Released—documented date
Smallest configuration · FP8 / INT8
2× MI325X512 GB VRAM · 94% used
- Per hour
- $6.21
- Per month
- $4,536
- Cheapest at
Cyfuture AI
How much VRAM it needs
Weights plus runtime overhead, then the KV cache that grows with the context you serve.
| Precision | Weights | Weights + headroom | 4k context | 32k context | 128k context | 256k context |
|---|---|---|---|---|---|---|
| BF16published checkpoint | 797 GB | 957 GB | 958 GB | 960 GB | 966 GB | 974 GB |
| FP8 / INT8estimated | 399 GB | 479 GB | 480 GB | 482 GB | 488 GB | 496 GB |
| INT4 / MXFP4estimated | 239 GB | 288 GB | 289 GB | 291 GB | 297 GB | 305 GB |
| KV cacheone sequence, FP16 cache, included above | — | — | 2 GB | 3 GB | 9 GB | 17 GB |
Cross-checked on Hugging Face Oct 28, 2026: 398.6B parameters in the safetensors headers, 797.3 GB of weight files, attention layout from config.json (60 layers, 8 KV heads × 128, 45 sliding-window layers). Method: weights as published at the native precision, verified on Hugging Face 2026-10-28; other precisions estimated at 1 (8-bit) and 0.6 (4-bit) bytes per parameter; × 1.2 runtime headroom; KV cache = 4096 bytes per token per layer × 15 full-attention layers + 45 sliding-window layers capped at 4096 tokens (FP16 cache, one sequence).config.json ↗
What it costs to run, GPU by GPU
The fewest GPUs of one type whose total memory fits the model at short context, priced from the cheapest eligible on-demand offer for exactly that count (45-day window, newest reading per provider), with the next providers beside it. Monthly = 730 hours.
| GPU | Smallest config | VRAM used | $ / hour | $ / month | Cheapest at |
|---|---|---|---|---|---|
| MI325XBest fit | 2× 256 GB512 GB total | 94% tight | $6.21 | $4,536 | |
| RTX PRO 6000 | 8× 96 GB768 GB total | 62% | $7.78 | $5,676 | |
| Gaudi 2 | 8× 96 GB768 GB total | 62% | $8.27 | $6,036 | |
| A100 80GB | 8× 80 GB640 GB total | 75% | $9.82 | $7,166 | Edgevananot scored · read Nov 1 · receipt · also GPU.ai $10.00, io.net $10.50 |
| H200 NVL | 4× 141 GB564 GB total | 85% | $10.67 | $7,788 | |
| H200 | 4× 141 GB564 GB total | 85% | $11.05 | $8,070 | |
| MI300X | 4× 192 GB768 GB total | 62% | $11.31 | $8,257 | |
| B300 | 2× 288 GB576 GB total | 83% | $11.65 | $8,506 | |
| H100 | 8× 80 GB640 GB total | 75% | $13.67 | $9,982 | |
| B200 | 4× 180 GB720 GB total | 67% | $16.46 | $12,013 | |
| MI355X | 8× 288 GB2304 GB total | 21% | $23.60 | $17,228 | |
| GB300 | 2× 288 GB576 GB total | 83% | $26.11 | $19,060 | |
| B200 CC | 4× 180 GB720 GB total | 67% | $36.19 | $26,419 | |
| B300 CC | 4× 288 GB1152 GB total | 42% | $46.31 | $33,806 | |
| A800 80GB PCIe | 8× 80 GB640 GB total | 75% | $50.80 | $37,088 | |
| H100 NVL | 8× 94 GB752 GB total | 64% | $51.79 | $37,809 |
Tokens per second, and what a million costs
Chat and reasoning. Speed estimated from memory bandwidth and model size, never measured. The cost per 1M output tokens is an estimate at the prices read on Oct 4, 2026; it is not recomputed here.
| GPU | Cards | Precision | tok/s per user | per 1M output tokens · estimate |
|---|---|---|---|---|
| MI300X | 4 | FP8 | 43.9 | $1.67 |
| H200 | 4 | FP8 | 56.9 | $2.06 |
| B200 | 4 | FP8 | 75.8 | $2.22 |
| H100 SXM | 8 | FP8 | 46.3 | $2.92 |
| RTX PRO 6000 | 8 | FP8 | 30.0 | $4.15 |
| H100 PCIe | 8 | FP8 | 27.9 | $4.60 |
cards, precision and speed estimated from memory bandwidth and model size; cost recomputed by the site from verified prices · cheapest per output at the prices of Oct 4, 2026: MI300XWhy estimates, and their limits →
