Hermes 3 (Llama 3.1 70B)
Flagship 70B agentic foundation model by Nous Research. Excels at long-form agent execution, complex JSON schemas, and nuanced judgment with 128K context.
The brutal truth
If you have a 48GB workstation or 64GB Mac, this is the most capable, least constrained 70B model you can run for autonomous workflows.
Files and Versions
Direct inspected tree artifacts from Hugging Face. Verified filenames, exact byte sizes, model weight shards, and configuration definitions.
Files and versions are hosted on Hugging Face. You can view all branches, commits, model weights, and tokenizer artifacts directly.
Inspect NousResearch/Hermes-3-Llama-3.1-70B /tree/main ↗Inspect /tree/main Repository ↗Decision matrix
✓ RUN / USE THIS IF…
- •You have 48GB+ VRAM
- •You want zero refusal guardrail interference
- •You have 40GB+ VRAM or can allocate Unified Memory for full offline model execution.
- •You require 100% offline data privacy with zero third-party API logging, subscriptions, or prompt retention.
✕ WALK AWAY IF…
- •You only have a 24GB card (use Qwen3-Coder or R1-32B)
- •Your GPU has less than 40GB VRAM and you cannot tolerate slow CPU/system RAM offload paging.
- •You prefer a plug-and-play web app interface and don't want to manage local model checkpoints, drivers, or runtimes.
- •You need specialized frontier reasoning that exceeds this model's parameter envelope.
True cost of ownership
About
Flagship 70B agentic foundation model by Nous Research. Excels at long-form agent execution, complex JSON schemas, and nuanced judgment with 128K context.
What it does
Download the weights from Hugging Face, pick a quantisation that matches your GPU, and run it locally with Ollama, llama.cpp, vLLM, or a compatible runtime. Complex multi-step RFC drafting & agent orchestration runs on your own hardware — no per-token bill, no third-party data handling.
Key features
Peerless steerability and complex agent scratchpad execution
128K context window with high recall
Strong creative and analytical synthesis
Use cases
Enrich new leads and route them to the right owner.
Chain tools and models into repeatable workflows.
Classify and draft responses to inbound tickets.
Pull metrics and generate weekly summaries automatically.
Quantization & Hardware Fit
VRAM Hardware Compatibility & Memory Allocation
| Quant | VRAM | Quality / Fit on 24GB | Install / Run Command (OLLAMA) | |
|---|---|---|---|---|
| Q4_K_M● ACTIVE | 40 GB | Standard · ✕ Needs 40GB | ollama run hermes3:70b | |
| Q5_K_M | 48 GB | Standard · ✕ Needs 48GB | ollama run hermes3:70b | |
| Q8_0 | 75 GB | Standard · ✕ Needs 75GB | ollama run hermes3:70b | |
| FP16 | 140 GB | Standard · ✕ Needs 140GB | ollama run hermes3:70b |
Buyer doubts, cleared
Can I actually run Hermes 3 (Llama 3.1 70B) on my 24GB GPU?
What's the fine print on the license?
Does the 128K tokens actually hold up in practice?
User reviews
Reasoning quality is close to the bigger models for structured tasks. Open-ended planning still needs a frontier model.
The Q4_K_M build is the sweet spot. Anything larger needs serious hardware for marginal quality gains.
Switched our internal workflow to Hermes 3 (Llama 3.1 70B) after the last pricing change. Nobody noticed a quality difference, and the bill dropped by half.
Clean interface, consistent results once you land on a prompt you like. Wish there were more export presets for client handoffs.
