Meta Llama 3.1 8B Instruct Abliterated — Hardware Requirements & GPU Compatibility
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Meta Llama 3.1 8B Instruct Abliterated is a 8.0B-parameter open language model from mlabonne 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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~122 tok/s · 128K)Macs from 16 GB (Q4_K_M · ~26 tok/s)
Llama 3.1 Community
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Meta Llama 3.1 8B Instruct Abliterated
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/bartowski/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 16.7 GB free · 16K context
Download: Meta-Llama-3.1-8B-Instruct-abliterated-Q4_K_M.gguf4.92 GBbartowski
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (4 GB headroom) · Q4_K_M
- Generation speed
- ~43 tok/s
- generation speed
- Cost per 1M output tokens
- $0.44
- per 1M output tokens
How Much VRAM Does Meta Llama 3.1 8B Instruct Abliterated Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.3 GB | 7.3 GB | 2-bit · i-quant medium — Importance-weighted 2-bit, medium | Fits: 128K context · ~200 tok/s | |
| 4.0 GB | 8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~165 tok/s | |
| 4.1 GB | 8.1 GB | 3-bit small — 3-bit small quantization | Fits: 128K context · ~161 tok/s | |
| 4.5 GB | 8.5 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~146 tok/s | |
| 5.4 GB | 9.4 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~122 tok/s | |
| 6.3 GB | 10.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~104 tok/s | |
| 7.2 GB | 11.2 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 64K context · ~91 tok/s | |
| 8.6 GB | 12.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 64K context · ~76 tok/s |
Selected Q4_K_M
4-bit medium quantization7.3GBat 16K context
- Weights
- 4.8 GB
- KV cache
- 2.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.7 GB free · ~122 tok/s
≥50 tok/s needs ≥414 GB/s · RTX 5090 ~216, RTX 3090 Ti ~122
Which Hardware Can Run Meta Llama 3.1 8B Instruct Abliterated?
Q4_K_M · 5.4 GBMeta Llama 3.1 8B Instruct Abliterated (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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Meta Llama 3.1 8B Instruct Abliterated, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 0.7 GB free · up to 16K ctx
16 GPUs · 1 device family
RTX 3080~92Simulate output on RTX 3080RTX 2080 Ti~74Simulate output on RTX 2080 TiRTX 3070 Ti~73Simulate output on RTX 3070 TiOrin Nano~12Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.7 GB free · up to 64K ctx
1 GPU
RX 7900 XT~89Simulate output on RX 7900 XT32–40 GB
fits · 24.7 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~216Simulate output on RTX 5090M5 Max36 GB~60Simulate output on Mac Studio M5 Max (36 GB)M532 GB~20Simulate output on MacBook Air 13" M5 (32 GB)Orin~25Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~16Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Meta Llama 3.1 8B Instruct Abliterated
Llama 3 family · 64 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| ALoDLM 8BAmazon | — | 7.6 GB | ~119 | same size |
| Llm Jp 4.1 8B Thinkingllm-jp | — | 7.6 GB | ~115 | same size |
| Ling 3.0 TinyInclusion AI | — | 5.5 GB | ~364 | same size |
| LFM2.5 8B A1BLiquid AI | — | 5.6 GB | ~335 | same size |
| BitCPM CANN 8BOpenBMB | — | 5.6 GB | ~127 | same size |
| Gemma 4 E4BGoogle | — | 5.6 GB | ~123 | same size |
Specifications
- Publisher
- mlabonne
- Family
- Llama 3
- Parameters
- 8.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-07-24
- License
- Llama 3.1 Community
Frequently Asked Questions
How much VRAM does Meta Llama 3.1 8B Instruct Abliterated need?
Meta Llama 3.1 8B Instruct Abliterated 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 17.5 GB (at full 131K context)
VRAM usage by quantization
What's the best quantization for Meta Llama 3.1 8B Instruct Abliterated?
For Meta Llama 3.1 8B Instruct Abliterated, 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
★ Recommended — best balance of quality and VRAM usage.
Can I run Meta Llama 3.1 8B Instruct Abliterated on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Meta Llama 3.1 8B Instruct Abliterated. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.4 GB of usable unified memory (RAM minus macOS overhead).
Can I run Meta Llama 3.1 8B Instruct Abliterated locally?
Yes — Meta Llama 3.1 8B Instruct Abliterated can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Meta Llama 3.1 8B Instruct Abliterated?
At Q4_K_M, Meta Llama 3.1 8B Instruct Abliterated can reach ~891 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 ÷ 5.39 × 0.65 = ~965 tok/s
Estimated speed at Q4_K_M (5.4 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Meta Llama 3.1 8B Instruct Abliterated?
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 Meta Llama 3.1 8B Instruct Abliterated?
52 consumer GPUs can run Meta Llama 3.1 8B Instruct Abliterated at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Meta Llama 3.1 8B Instruct Abliterated?
151 devices with unified memory can run Meta Llama 3.1 8B Instruct Abliterated at Q4_K_M (5.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, 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.