Athene v2 Chat — Hardware Requirements & GPU Compatibility
Chat
Athene-V2-Chat-72B is Nexusflow's 72-billion-parameter chat model, fine-tuned from Qwen2.5-72B-Instruct through reinforcement learning from human feedback (RLHF) to specialize in chat, math, and coding. On Chatbot Arena the card reports it beating GPT-4o-0513 in the hard-prompts and math categories and matching it on coding, instruction-following, and multi-turn chat. A sister model, Athene-V2-Agent-72B, is fine-tuned separately for function calling and agentic tasks. At 72B parameters, it needs a multi-GPU workstation or server to run, even quantized. Context length is 32,768 tokens. It is released under the Nexusflow Research License, a custom license restricted to personal, non-profit, non-commercial use, with commercial use requiring separate permission from Nexusflow, and was published in November 2024.
4.5K downloads
294 likesSpecifications
- Publisher
- Nexusflow
- Parameters
- 72.7B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-11-12
- License
- Other
Get Started
HuggingFace
How Much VRAM Does Athene v2 Chat Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 31.9 GB | 41.9 GB | 30.90 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 32.8 GB | 42.9 GB | 31.81 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 36.4 GB | 46.5 GB | 35.44 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 37.3 GB | 47.4 GB | 36.35 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 44.6 GB | 54.7 GB | 43.62 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 52.8 GB | 62.8 GB | 51.80 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 61.0 GB | 71.0 GB | 59.98 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 73.7 GB | 83.7 GB | 72.71 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 Athene v2 Chat?
Q4_K_M · 44.6 GBAthene v2 Chat (Q4_K_M) requires 44.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 58+ GB is recommended. Using the full 33K context window can add up to 10.1 GB, bringing total usage to 54.7 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Athene v2 Chat?
Q4_K_M · 44.6 GB26 devices with unified memory can run Athene v2 Chat, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Where to Download Athene v2 Chat
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Athene v2 Chat need?
Athene v2 Chat requires 44.6 GB of VRAM at Q4_K_M, or 146.4 GB at BF16. Full 33K context adds up to 10.1 GB (54.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 72.7B × 4.8 bits ÷ 8 = 43.6 GB
KV Cache + Overhead ≈ 1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 11.1 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M44.6 GBQ4_K_M + full context54.7 GB- Can NVIDIA GeForce RTX 4090 run Athene v2 Chat?
Yes, at IQ2_S (23.7 GB) or lower. Higher quantizations like IQ2_M (25.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Athene v2 Chat?
For Athene v2 Chat, Q4_K_M (44.6 GB) offers the best balance of quality and VRAM usage. Q4_K_L (45.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 21.0 GB.
VRAM requirement by quantization
IQ2_XXS21.0 GBIQ3_S31.9 GBQ4_037.3 GBQ4_K_M ★44.6 GBQ5_K_S51.0 GBBF16146.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Athene v2 Chat on a Mac?
Athene v2 Chat requires at least 21.0 GB at IQ2_XXS, 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 Athene v2 Chat locally?
Yes — Athene v2 Chat can run locally on consumer hardware. At Q4_K_M quantization it needs 44.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Athene v2 Chat?
At Q4_K_M, Athene v2 Chat can reach ~108 tok/s on AMD Instinct MI350X. 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 ÷ 44.6 × 0.65 = ~117 tok/s
Estimated speed at Q4_K_M (44.6 GB)
~117 tok/s~117 tok/s~108 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Athene v2 Chat?
At Q4_K_M, the download is about 43.62 GB. The full-precision BF16 version is 145.41 GB. The smallest option (IQ2_XXS) is 19.99 GB.
- Which GPUs can run Athene v2 Chat?
No single consumer GPU has enough VRAM to run Athene v2 Chat at Q4_K_M (44.6 GB). Multi-GPU or professional hardware is required.
- Which devices can run Athene v2 Chat?
27 devices with unified memory can run Athene v2 Chat at Q4_K_M (44.6 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.