Meta · Chat and reasoning · 109B parameters · 17B active · Llama 4 Community License
Llama 4 Scout 17B 16E Instruct
Needs 131 GB at FP8 / INT8. The smallest real configuration today is 8× Tesla V100 32GB at Vast.ai, $0.54 per hour, about $391 a month at 730 hours.
Parameters109B17B active per token
Context10M tokensmaximum published
Checkpoint217 GBBF16, as published
LicenseLlama 4 Community Licensecheck before commercial use
ReleasedApr 5, 2025documented date
Smallest configuration · FP8 / INT8
8× Tesla V100 32GB256 GB VRAM · 51% used
- Per hour
- $0.54
- Per month
- $391
- Cheapest at
Vast.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 | 1M context |
|---|---|---|---|---|---|---|---|
| BF16published checkpoint | 217 GB | 261 GB | 262 GB | 268 GB | 287 GB | 313 GB | 467 GB |
| FP8 / INT8estimated | 109 GB | 131 GB | 132 GB | 138 GB | 157 GB | 183 GB | 337 GB |
| INT4 / MXFP4estimated | 65 GB | 79 GB | 80 GB | 85 GB | 105 GB | 131 GB | 285 GB |
| KV cacheone sequence, FP16 cache, included above | — | — | 1 GB | 7 GB | 26 GB | 52 GB | 207 GB |
Cross-checked on Hugging Face Oct 28, 2026: 108.6B parameters in the safetensors headers, 217.3 GB of weight files, attention layout from config.json (48 layers, 8 KV heads × 128). 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 × 48 full-attention layers (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 |
|---|---|---|---|---|---|
| Tesla V100 32GBBest fit | 8× 32 GB256 GB total | 51% | $0.54 | $391 | |
| A800 80GB PCIe | 2× 80 GB160 GB total | 82% | $0.96 | $702 | |
| Quadro RTX 8000 | 4× 48 GB192 GB total | 68% | $1.01 | $741 | |
| RTX 3090 | 8× 24 GB192 GB total | 68% | $1.17 | $851 | |
| A100 80GB | 2× 80 GB160 GB total | 82% | $1.20 | $878 | |
| L40S | 4× 48 GB192 GB total | 68% | $1.52 | $1,110 | |
| A100 40GB | 4× 40 GB160 GB total | 82% | $1.60 | $1,170 | |
| MI300X | 1× 192 GB192 GB total | 68% | $1.65 | $1,205 | |
| RTX PRO 6000 | 2× 96 GB192 GB total | 68% | $1.67 | $1,218 | |
| RTX A6000 | 4× 48 GB192 GB total | 68% | $1.68 | $1,226 | |
| RTX PRO 5000 | 4× 48 GB192 GB total | 68% | $1.92 | $1,403 | |
| RTX PRO 4000 | 8× 24 GB192 GB total | 68% | $1.93 | $1,406 | |
| H100 NVL | 2× 94 GB188 GB total | 70% | $2.08 | $1,521 | |
| H200 | 1× 141 GB141 GB total | 93% tight | $2.09 | $1,526 | |
| Gaudi 2 | 2× 96 GB192 GB total | 68% | $2.11 | $1,538 | |
| RTX 4000 Ada | 8× 20 GB160 GB total | 82% | $2.14 | $1,559 | |
| MI325X | 1× 256 GB256 GB total | 51% | $2.25 | $1,643 | |
| A40 | 4× 48 GB192 GB total | 68% | $2.30 | $1,676 | |
| A100X | 2× 80 GB160 GB total | 82% | $2.56 | $1,870 | |
| RTX 6000 Ada | 4× 48 GB192 GB total | 68% | $2.60 | $1,898 | |
| L40 | 4× 48 GB192 GB total | 68% | $2.65 | $1,932 | |
| H200 NVL | 1× 141 GB141 GB total | 93% tight | $2.67 | $1,948 | |
| RTX 4090 | 8× 24 GB192 GB total | 68% | $3.07 | $2,241 | |
| H100 | 2× 80 GB160 GB total | 82% | $3.42 | $2,496 | |
| The 24 cheapest of 38 GPU types that fit. Every GPU, any precision and context, in the finder | |||||
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 | 1 | FP8 | 70.0 | $0.397 |
| B200 | 1 | FP8 | 121 | $0.532 |
| H100 SXM | 2 | FP8 | 65.9 | $0.681 |
| RTX PRO 6000 | 2 | FP8 | 39.0 | $0.850 |
| H200 | 1 | FP8 | 82.3 | $0.889 |
| H100 PCIe | 2 | FP8 | 39.1 | $0.894 |
| L40S | 4 | FP8 | 27.1 | $1.03 |
| A100 80GB | 4 | BF16 | 39.7 | $1.30 |
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 →
