Hermes 3 Llama 3.1 8B — Hardware Requirements & GPU Compatibility
ChatRoleplay
Hermes 3 Llama 3.1 8B is an 8-billion parameter instruction-tuned model by Nous Research, built on Meta's Llama 3.1 8B base. It is fine-tuned for advanced instruction following, multi-turn conversation, structured output, and creative roleplay scenarios. The Hermes series is known for producing highly steerable models that respond well to system prompts. This model supports a 128K token context window inherited from the Llama 3.1 architecture and runs efficiently on consumer GPUs with 8GB or more of VRAM. It is a popular choice among local inference enthusiasts who value strong instruction adherence and versatile conversational ability.
471.6K downloads
393 likesSpecifications
- Publisher
- Nous Research
- Family
- Llama 3
- Parameters
- 8.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-09-08
- License
- Llama 3 Community
Get Started
HuggingFace
How Much VRAM Does Hermes 3 Llama 3.1 8B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_M | 2.70 | 3.3 GB | 20.2 GB | 2.71 GB | Importance-weighted 2-bit, medium |
| IQ3_XS | 3.30 | 3.9 GB | 20.8 GB | 3.31 GB | Importance-weighted 3-bit, extra small |
| IQ3_S | 3.40 | 4.0 GB | 20.9 GB | 3.41 GB | Importance-weighted 3-bit, small |
| Q2_K | 3.40 | 4.0 GB | 20.9 GB | 3.41 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.1 GB | 21.0 GB | 3.51 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 4.2 GB | 21.1 GB | 3.61 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 4.5 GB | 21.4 GB | 3.91 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 4.7 GB | 21.6 GB | 4.12 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 4.9 GB | 21.8 GB | 4.32 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 5.1 GB | 22 GB | 4.52 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 5.4 GB | 22.3 GB | 4.82 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 5.5 GB | 22.4 GB | 4.92 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 6.1 GB | 23 GB | 5.52 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 6.3 GB | 23.2 GB | 5.72 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 6.4 GB | 23.3 GB | 5.82 GB | 5-bit large quantization |
| Q6_K | 6.60 | 7.2 GB | 24.1 GB | 6.62 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.6 GB | 25.5 GB | 8.03 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Hermes 3 Llama 3.1 8B?
Q4_K_M · 5.4 GBHermes 3 Llama 3.1 8B (Q4_K_M) requires 5.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 131K context window can add up to 16.9 GB, bringing total usage to 22.3 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Hermes 3 Llama 3.1 8B?
Q4_K_M · 5.4 GB33 devices with unified memory can run Hermes 3 Llama 3.1 8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Derivatives (4)
Frequently Asked Questions
- How much VRAM does Hermes 3 Llama 3.1 8B need?
Hermes 3 Llama 3.1 8B requires 5.4 GB of VRAM at Q4_K_M, or 8.6 GB at Q8_0. Full 131K context adds up to 16.9 GB (22.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.0B × 4.8 bits ÷ 8 = 4.8 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 17.5 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M5.4 GBQ4_K_M + full context22.3 GB- What's the best quantization for Hermes 3 Llama 3.1 8B?
For Hermes 3 Llama 3.1 8B, Q4_K_M (5.4 GB) offers the best balance of quality and VRAM usage. Q4_K_L (5.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 3.3 GB.
VRAM requirement by quantization
IQ2_M3.3 GB~62%Q3_K_S4.1 GB~77%IQ4_XS4.9 GB~87%Q4_K_M ★5.4 GB~89%Q5_K_S6.1 GB~92%Q8_08.6 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Hermes 3 Llama 3.1 8B on a Mac?
Hermes 3 Llama 3.1 8B requires at least 3.3 GB at IQ2_M, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Hermes 3 Llama 3.1 8B locally?
Yes — Hermes 3 Llama 3.1 8B can run locally on consumer hardware. At Q4_K_M quantization it needs 5.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Hermes 3 Llama 3.1 8B?
At Q4_K_M, Hermes 3 Llama 3.1 8B can reach ~541 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~122 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 5.4 × 0.55 = ~541 tok/s
Estimated speed at Q4_K_M (5.4 GB)
AMD Instinct MI300X~541 tok/sNVIDIA GeForce RTX 4090~122 tok/sNVIDIA H100 SXM~404 tok/sAMD Instinct MI250X~334 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Hermes 3 Llama 3.1 8B?
At Q4_K_M, the download is about 4.82 GB. The full-precision Q8_0 version is 8.03 GB. The smallest option (IQ2_M) is 2.71 GB.