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
No provider we track publishes a per-token price for this model today. What each one used to offer is listed below.
No longer listed
Providers that used to serve this model. We don't republish their old rates — the dates below are the provider's own.
- togetherNot in Together AI’s serverless catalogue when we checked on 20 September 2026.Source ↗
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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