Llama 3.1 SuperNova Lite — Hardware Requirements & GPU Compatibility
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Llama 3.1 SuperNova Lite is a 8.0B-parameter open language model from Arcee AI in the Llama 3 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 5.39 GB of VRAM — see which GPUs and Macs can run it below.
1.4K downloads
198 likesSpecifications
- Publisher
- Arcee AI
- Family
- Llama 3
- Parameters
- 8.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-09-10
- License
- Llama 3 Community
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HuggingFace
How Much VRAM Does Llama 3.1 SuperNova Lite Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| 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 |
| Q3_K_M | 3.90 | 4.5 GB | 21.4 GB | 3.91 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.6 GB | 21.5 GB | 4.02 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.4 GB | 22.3 GB | 4.82 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.3 GB | 23.2 GB | 5.72 GB | 5-bit medium quantization — good quality/size tradeoff |
| 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 |
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 Llama 3.1 SuperNova Lite?
Q4_K_M · 5.4 GBLlama 3.1 SuperNova Lite (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 Llama 3.1 SuperNova Lite?
Q4_K_M · 5.4 GB33 devices with unified memory can run Llama 3.1 SuperNova Lite, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Llama 3.1 SuperNova Lite
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 Llama 3.1 SuperNova Lite need?
Llama 3.1 SuperNova Lite requires 5.4 GB of VRAM at Q4_K_M, or 16.6 GB at BF16. 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 Llama 3.1 SuperNova Lite?
For Llama 3.1 SuperNova Lite, 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 GBIQ3_M4.2 GBQ4_K_S5.1 GBQ4_K_M ★5.4 GBQ5_K_M6.3 GBBF1616.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 3.1 SuperNova Lite on a Mac?
Llama 3.1 SuperNova Lite 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 Llama 3.1 SuperNova Lite locally?
Yes — Llama 3.1 SuperNova Lite 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 Llama 3.1 SuperNova Lite?
At Q4_K_M, Llama 3.1 SuperNova Lite 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)
~541 tok/s~122 tok/s~404 tok/s~334 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama 3.1 SuperNova Lite?
At Q4_K_M, the download is about 4.82 GB. The full-precision BF16 version is 16.06 GB. The smallest option (IQ2_M) is 2.71 GB.
- Which GPUs can run Llama 3.1 SuperNova Lite?
35 consumer GPUs can run Llama 3.1 SuperNova Lite at Q4_K_M (5.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 28 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Llama 3.1 SuperNova Lite?
33 devices with unified memory can run Llama 3.1 SuperNova Lite at Q4_K_M (5.4 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.