Hermes 3 Llama 3.2 3B — Hardware Requirements & GPU Compatibility
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Hermes 3 Llama 3.2 3B is a 3-billion parameter instruction-tuned model by Nous Research, fine-tuned from Meta's Llama 3.2 3B base. It applies the Hermes training methodology to a compact model, targeting strong instruction following and conversational quality at minimal hardware cost. Despite its small size, this model benefits from the Hermes fine-tuning approach that emphasizes system prompt adherence and structured output. It can run on GPUs with as little as 4GB of VRAM when quantized, making it suitable for lightweight local deployments and resource-constrained environments.
77.3K downloads
175 likesSpecifications
- Publisher
- Nous Research
- Family
- Llama 3
- Parameters
- 3B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-12-03
- License
- Llama 3 Community
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HuggingFace
How Much VRAM Does Hermes 3 Llama 3.2 3B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.8 GB | 16.6 GB | 1.27 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.9 GB | 16.6 GB | 1.31 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 2 GB | 16.8 GB | 1.46 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.0 GB | 16.8 GB | 1.50 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 2.3 GB | 17.1 GB | 1.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 2.7 GB | 17.5 GB | 2.14 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 3.0 GB | 17.8 GB | 2.48 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 3.5 GB | 18.3 GB | 3.00 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Hermes 3 Llama 3.2 3B?
Q4_K_M · 2.3 GBHermes 3 Llama 3.2 3B (Q4_K_M) requires 2.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 131K context window can add up to 14.8 GB, bringing total usage to 17.1 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Hermes 3 Llama 3.2 3B?
Q4_K_M · 2.3 GB59 devices with unified memory can run Hermes 3 Llama 3.2 3B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Hermes 3 Llama 3.2 3B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Hermes 3 Llama 3.2 3B need?
Hermes 3 Llama 3.2 3B requires 2.3 GB of VRAM at Q4_K_M, or 6.5 GB at BF16. Full 131K context adds up to 14.8 GB (17.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3B × 4.8 bits ÷ 8 = 1.8 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 15.3 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M2.3 GBQ4_K_M + full context17.1 GB- What's the best quantization for Hermes 3 Llama 3.2 3B?
For Hermes 3 Llama 3.2 3B, Q4_K_M (2.3 GB) offers the best balance of quality and VRAM usage. Q4_K_L (2.4 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 1.6 GB.
VRAM requirement by quantization
IQ2_M1.6 GBQ3_K_M2.0 GBIQ4_NL2.2 GBQ4_K_M ★2.3 GBQ5_K_M2.7 GBBF166.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Hermes 3 Llama 3.2 3B on a Mac?
Hermes 3 Llama 3.2 3B requires at least 1.6 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 3.2 3B locally?
Yes — Hermes 3 Llama 3.2 3B can run locally on consumer hardware. At Q4_K_M quantization it needs 2.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Hermes 3 Llama 3.2 3B?
At Q4_K_M, Hermes 3 Llama 3.2 3B can reach ~1888 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~281 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 2.3 × 0.65 = ~2232 tok/s
Estimated speed at Q4_K_M (2.3 GB)
~2232 tok/s~281 tok/s~2232 tok/s~1888 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 3.2 3B?
At Q4_K_M, the download is about 1.80 GB. The full-precision BF16 version is 6.00 GB. The smallest option (IQ2_M) is 1.01 GB.
- Which GPUs can run Hermes 3 Llama 3.2 3B?
50 consumer GPUs can run Hermes 3 Llama 3.2 3B at Q4_K_M (2.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Hermes 3 Llama 3.2 3B?
59 devices with unified memory can run Hermes 3 Llama 3.2 3B at Q4_K_M (2.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.