Bella Bartender 8B Llama3.1 — Hardware Requirements & GPU Compatibility
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Bella Bartender 8B Llama3.1 is a 8.0B-parameter open language model from juiceb0xc0de 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.
3.7K downloads
5 likesSpecifications
- Publisher
- juiceb0xc0de
- Family
- Llama 3
- Parameters
- 8.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2026-03-09
- License
- Llama 3.1 Community
Get Started
HuggingFace
How Much VRAM Does Bella Bartender 8B Llama3.1 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XS | 2.40 | 3.0 GB | 19.9 GB | 2.41 GB | Importance-weighted 2-bit, extra small |
| Q2_Kest. | 3.40 | 4.0 GB | 20.9 GB | 3.41 GB | 2-bit quantization with K-quant improvements |
| 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 |
| IQ4_XS | 4.30 | 4.9 GB | 21.8 GB | 4.32 GB | Importance-weighted 4-bit, compact |
| 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 |
| BF16 | 16.00 | 16.6 GB | 33.5 GB | 16.06 GB | Brain floating point 16 — preferred for training |
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 Bella Bartender 8B Llama3.1?
Q4_K_M · 5.4 GBBella Bartender 8B Llama3.1 (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. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Bella Bartender 8B Llama3.1?
Q4_K_M · 5.4 GB58 devices with unified memory can run Bella Bartender 8B Llama3.1, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Bella Bartender 8B Llama3.1 need?
Bella Bartender 8B Llama3.1 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 Bella Bartender 8B Llama3.1?
For Bella Bartender 8B Llama3.1, Q4_K_M (5.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (6.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 3.0 GB.
VRAM requirement by quantization
IQ2_XS3.0 GBIQ3_M4.2 GBQ4_K_M ★5.4 GBQ5_K_M6.3 GBQ6_K7.2 GBBF1616.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Bella Bartender 8B Llama3.1 on a Mac?
Bella Bartender 8B Llama3.1 requires at least 3.0 GB at IQ2_XS, 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 Bella Bartender 8B Llama3.1 locally?
Yes — Bella Bartender 8B Llama3.1 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 Bella Bartender 8B Llama3.1?
At Q4_K_M, Bella Bartender 8B Llama3.1 can reach ~816 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 5.4 × 0.65 = ~965 tok/s
Estimated speed at Q4_K_M (5.4 GB)
~965 tok/s~122 tok/s~965 tok/s~816 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Bella Bartender 8B Llama3.1?
At Q4_K_M, the download is about 4.82 GB. The full-precision BF16 version is 16.06 GB. The smallest option (IQ2_XS) is 2.41 GB.
- Which GPUs can run Bella Bartender 8B Llama3.1?
50 consumer GPUs can run Bella Bartender 8B Llama3.1 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. 39 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Bella Bartender 8B Llama3.1?
59 devices with unified memory can run Bella Bartender 8B Llama3.1 at Q4_K_M (5.4 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.