Hermes 3 (Llama 3.1 8B)
State-of-the-art generalist instruction and roleplay model by Nous Research. Superb function calling, code generation, and multi-turn planning.
The brutal truth
The best community fine-tune of Llama 3.1 8B. Highly steerable, great at structured tool calling, and free of annoying canned refusals.
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-3.1-8B /tree/main ↗Inspect /tree/main Repository ↗Decision matrix
✓ RUN / USE THIS IF…
- •You want an uncensored, obedient agent model on 8GB VRAM
- •You build complex multi-agent workflows
- •Your 24GB GPU comfortably fits the model weights (~5GB VRAM) with dedicated headroom for inference.
- •You require 100% offline data privacy with zero third-party API logging, subscriptions, or prompt retention.
✕ WALK AWAY IF…
- •You require corporate IBM-style indemnity
- •Your GPU has less than 5GB 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
State-of-the-art generalist instruction and roleplay model by Nous Research. Superb function calling, code generation, and multi-turn planning.
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. Unconstrained reasoning & multi-turn agent planning runs on your own hardware — no per-token bill, no third-party data handling.
Key features
Remarkable neutral steerability and un-neutered reasoning
Trained on advanced multi-step agent scratchpads
128K context window
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 | 5 GB | Standard · ✓ Fits (19GB spare) | ollama run hermes3:8b | |
| Q5_K_M | 6 GB | Standard · ✓ Fits (18GB spare) | ollama run hermes3:8b | |
| Q8_0 | 9 GB | Standard · ✓ Fits (15GB spare) | ollama run hermes3:8b | |
| FP16 | 16 GB | Standard · ✓ Fits (8GB spare) | ollama run hermes3:8b |
Buyer doubts, cleared
Can I actually run Hermes 3 (Llama 3.1 8B) on my 24GB GPU?
What's the fine print on the license?
Does the 128K tokens actually hold up in practice?
User reviews
Clean interface, consistent results once you land on a prompt you like. Wish there were more export presets for client handoffs.
The Q4_K_M build is the sweet spot. Anything larger needs serious hardware for marginal quality gains.
Used Hermes 3 (Llama 3.1 8B) for a client project. Documentation was solid, though the fine-tuning path is less polished than base inference.
Hermes 3 (Llama 3.1 8B) handled our batch jobs without surprises. The API is well documented and the rate limits are reasonable.
