Llama 3.1 8B — Hardware Requirements & GPU Compatibility
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Meta Llama 3.1 8B is an 8-billion parameter base (pretrained) model from the Llama 3.1 family. It is not instruction-tuned and is intended for fine-tuning, research, and custom downstream applications. Compared to Llama 3 8B, it extends the context window to 128K tokens and benefits from improved training data and methodology. The model uses grouped-query attention and was trained on a multilingual corpus. It is released under the Llama 3.1 Community License and is widely used as a foundation for community fine-tunes and specialized models.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~124 tok/s · 32K)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 Llama 3.1 8B
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/mradermacher/Meta-Llama-3.1-8B-i1-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 16.1 GB free · 16K context
Download: Meta-Llama-3.1-8B.i1-Q4_K_M.gguf4.92 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (4 GB headroom) · Q4_K_M
- Generation speed
- ~112 tok/s
- generation speed
- Cost per 1M output tokens
- $0.30
- per 1M output tokens
How Much VRAM Does Llama 3.1 8B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.8 GB | 9.3 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~175 tok/s | |
| 3.9 GB | 9.4 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~170 tok/s | |
| 4.3 GB | 9.8 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~152 tok/s | |
| 4.4 GB | 9.9 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~148 tok/s | |
| 5.3 GB | 10.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~124 tok/s | |
| 6.3 GB | 11.8 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~104 tok/s | |
| 7.3 GB | 12.8 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~90 tok/s | |
| 8.8 GB | 14.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~74 tok/s |
Selected Q4_K_M
4-bit medium quantization7.9GBat 16K context
- Weights
- 4.6 GB
- KV cache
- 2.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.1 GB free · ~124 tok/s
≥50 tok/s needs ≥407 GB/s · RTX 5090 ~220, RTX 3090 Ti ~124
Which Hardware Can Run Llama 3.1 8B?
Q4_K_M · 5.3 GBLlama 3.1 8B (Q4_K_M) requires 5.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 151 devices with unified memory can run Llama 3.1 8B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 0.1 GB free · up to 16K ctx
16 GPUs · 1 device family
RTX 3080~93Simulate output on RTX 3080RTX 2080 Ti~76Simulate output on RTX 2080 TiRTX 3070 Ti~75Simulate output on RTX 3070 TiOrin Nano~13Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.1 GB free · up to 32K ctx
1 GPU
RX 7900 XT~91Simulate output on RX 7900 XT32–40 GB
fits · 24.1 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~220Simulate output on RTX 5090M5 Max36 GB~61Simulate 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)~17Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Llama 3.1 8B?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
This is the single board showing results for this model; badges show who ran the test.
- MMLU-Pro
TIGER-LabIndependent
Independent36.6%
#110/146 open
Alternatives to Llama 3.1 8B
Llama 3 family · 65 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
- Meta
- Family
- Llama 3
- Parameters
- 8.0B
- Release Date
- 2024-07-14
- License
- Llama 3.1 Community
Frequently Asked Questions
How much VRAM does Llama 3.1 8B need?
Llama 3.1 8B requires 5.3 GB of VRAM at Q4_K_M, or 17.7 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 8.0B × 4.8 bits ÷ 8 = 4.8 GB
KV Cache + Overhead ≈ 0.5 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.
VRAM usage by quantization
What's the best quantization for Llama 3.1 8B?
For Llama 3.1 8B, Q4_K_M (5.3 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Llama 3.1 8B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Llama 3.1 8B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Llama 3.1 8B locally?
Yes — Llama 3.1 8B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 3.1 8B?
At Q4_K_M, Llama 3.1 8B can reach ~906 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~124 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 ÷ 5.3 × 0.65 = ~981 tok/s
Estimated speed at Q4_K_M (5.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Llama 3.1 8B?
At Q4_K_M, the download is about 4.82 GB. The full-precision BF16 version is 16.06 GB. The smallest option (IQ2_XXS) is 2.21 GB.
Which GPUs can run Llama 3.1 8B?
52 consumer GPUs can run Llama 3.1 8B at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Llama 3.1 8B?
151 devices with unified memory can run Llama 3.1 8B at Q4_K_M (5.3 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.