Jamba 1.5 Large
Hybrid Mamba-Transformer-MoE model with native 256K context (effective beyond 140K). 94B active parameters out of 398B total. The state-space-model layers give it linear-time scaling with sequence length, making it interesting for very long contexts. Licensed under AI21's open model licence, which permits most commercial use.
- Parameters
- 398B
- Context length
- 256K
- Modality
- text
- Released
- 2024-08-22
Memory & hardware
- VRAM (fp16)
- 796 GB
- VRAM (Q4)
- 238.8 GB
- Recommended
- 8× H100 80GB
- Quantizations
- fp16, q8_0
License: Jamba Open Model License
- SPDX
- —
- Commercial use
- Yes
- Modification
- Yes
- Redistribution
- Yes
Benchmarks
Hosted inference pricing
USD per million tokens.
| Provider | Input | Output | |
|---|---|---|---|
| togetherCheapest | $2.00 | $8.00 |
Run it yourself
Drop-in commands for the three most common open-source inference paths. The Ollama tag is a best-effort match against the registry; verify the size variant before pulling.
vllm serve ai21labs/AI21-Jamba-1.5-Large
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("ai21labs/AI21-Jamba-1.5-Large")
model = AutoModelForCausalLM.from_pretrained(
"ai21labs/AI21-Jamba-1.5-Large", device_map="auto", torch_dtype="auto"
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