Qwen2.5 72B Instruct
The flagship Qwen 2.5 release. Competes with Llama 3.1 405B on many benchmarks at one-fifth the parameter count. Note the 72B specifically uses the Qwen License (commercial use up to 100M MAU) — the smaller Qwen2.5 sizes are Apache 2.0.
- Parameters
- 72B
- Context length
- 128K
- Modality
- text
- Released
- 2024-09-18
- Tokenizer
- Qwen2Tokenizer
Memory & hardware
- VRAM (fp16)
- 144 GB
- VRAM (Q4)
- 43.2 GB
- Recommended
- 2× A100 80GB or H100 80GB (Q4)
- Quantizations
- fp16, fp8, q8_0, q5_k_m, q4_k_m
Benchmarks
Hosted inference pricing
USD per million tokens.
| Provider | Input | Output | |
|---|---|---|---|
| openrouterCheapest | $0.36 | $0.40 | Visit ↗ |
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.
ollama run qwen2.5:72b
vllm serve Qwen/Qwen2.5-72B-Instruct
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-72B-Instruct")
model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen2.5-72B-Instruct", device_map="auto", torch_dtype="auto"
)Qwen/Qwen2.5-72B-Instruct Related models
Same family or similar size — useful when shopping around.
The flagship Qwen 3 release: a 235B-total MoE with 22B active parameters per token. Competitive with DeepSeek V3 and Llama 4 Maverick on reasoning benchmarks while being smaller total. Apache 2.0 — one of the most permissively licenced frontier-class models.
- Context
- 128K
- License
- apache-2-0
- VRAM Q4
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Meta's December 2024 refresh of Llama 3 70B that closes most of the gap with Llama 3.1 405B for chat workloads while remaining tractable on a single H100. Strong instruction following, robust tool-use behaviour, and a 128K context window make it the default choice for production chat at 70B scale. The 3.3 release was trained on a refreshed instruction-tuning data mix and benefits from Meta's most recent alignment work. It outperforms the much larger 3.1 405B on several reasoning benchmarks at a fraction of inference cost. The licence is the Llama 3 Community License, which permits commercial use unless your service exceeds 700M monthly active users. Good pick for: production chat at scale, RAG over long documents, agentic workflows where tool use matters, and any 70B-tier replacement for closed proprietary models.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 42 GB
The pre-3.3 70B workhorse. Same base architecture as Llama 3.3 70B but the earlier instruction-tuning recipe. Still widely referenced as a baseline in papers and provider docs, and still the default 70B on some hosted providers.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 42 GB
NVIDIA's RLHF-tuned Llama 3.1 70B. Tops several Arena-style human-preference leaderboards and shipped with NVIDIA's reward-model research. Inherits the Llama 3 community licence.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 42 GB
Flagship Llama 2 release. Fundamentally superseded by Llama 3 70B on every benchmark, but relevant historically: the model that made 'open-weights chat model at frontier scale' credible for enterprise workloads.
- Context
- 4K
- License
- llama-2
- VRAM Q4
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Larger Hermes 3 variant on top of Llama 3.1 70B. Widely used in agent-heavy workloads that need strong tool use combined with reliable function-calling schemas.
- Context
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- License
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- VRAM Q4
- 42 GB