Llama 3.1 8B Instruct — Hardware Requirements & GPU Compatibility
ChatFunctions
Meta Llama 3.1 8B Instruct is an 8-billion parameter instruction-tuned language model from Meta. Part of the Llama 3.1 release, it supports a 128K token context window and is fine-tuned for conversational use, tool calling, and general assistant tasks. Its compact size makes it well-suited for local deployment on modern consumer GPUs with 8GB or more of VRAM. Llama 3.1 8B Instruct delivers strong performance for its parameter class across benchmarks in reasoning, coding, and multilingual understanding. It is released under the Llama 3.1 Community License and is widely supported by inference frameworks such as llama.cpp, vLLM, and Ollama.
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 · 64K)Macs from 32 GB (~16 tok/s)
Score 62.5#88 of 92 open#31 of 34 that fit 24 GBLlama 3.1 Community
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
62.5
- 90% range 55.5–69.6
- #88 of 92 open models
- #31 of 34 that fit 24 GB
Run Llama 3.1 8B Instruct
Best fit for your MacBook Pro 16" M2 Pro (16 GB). Every other method, OS and quant is in the step-by-step guide.
Recommended for your MacBook Pro 16" M2 Pro (16 GB)
mlx_lm.chat --model mlx-community/Llama-3.1-8B-Instruct-4bitMLX repo with its own quantisation
Download: Meta-Llama-3.1-8B-Instruct-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
- ~44 tok/s
- generation speed
- Cost per 1M output tokens
- $0.43
- per 1M output tokens
How Much VRAM Does Llama 3.1 8B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On your device | |
|---|---|---|---|---|
| 3.8 GB | 9.3 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~37 tok/s | |
| 3.9 GB | 9.4 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~36 tok/s | |
| 4.3 GB | 9.8 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~33 tok/s | |
| 4.4 GB | 9.9 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~32 tok/s | |
| 5.3 GB | 10.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~26 tok/s | |
| 6.3 GB | 11.8 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~22 tok/s | |
| 7.3 GB | 12.8 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 16K context · ~19 tok/s | |
| 8.8 GB | 14.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 16K context · ~16 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 MacBook Pro 16" M2 Pro (16 GB) · 4.6 GB free · ~26 tok/s
≥50 tok/s needs ≥407 GB/s · RTX 5090 ~220, RTX 3090 Ti ~124
Which Hardware Can Run Llama 3.1 8B Instruct?
Q4_K_M · 5.3 GBLlama 3.1 8B Instruct (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. 159 devices with unified memory can run Llama 3.1 8B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Apple iPhone 17 Pro.
≤ 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)12 GB
12 GB tier →fits · 4.1 GB free · up to 32K ctx
10 GPUs · 1 device family
RTX 3080 Ti~112Simulate output on RTX 3080 TiRTX 5070~82Simulate output on RTX 5070RTX 4070~62Simulate output on RTX 4070iPhone~10Simulate output on Apple iPhone 17 Pro20 GB
fits · 12.1 GB free · up to 64K ctx
1 GPU
RX 7900 XT~91Simulate output on RX 7900 XT
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Llama 3.1 8B Instruct?
The llmrun Score merges 10 public benchmark boards into a single figure, where 100 represents the average for open models.
Score vs VRAM in the 24 GB tier
- Llama 3.1 8B Instruct
- Other models
- Best Score for its VRAM
#31 of 34 that fit 24 GB12 of 34 fit your MacBook Pro 16" M2 Pro (16 GB) (16 GB)
Each dot is a model at the best quant that fits a 24 GB GPU. The line joins the models with the best Score for their VRAM; a dot below it is beaten by a model that needs no more memory.
Full 24 GB ranking →Benchmarks
The 6 boards that weigh most in the llmrun Score, of 16 with a result for this model; badges show who ran the test.
- GPQA Diamond
Epoch AI · Jul 2024Independent
Independent27.0%
#86/97 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1211.3
#141/186 open
- AIME 2024/2025
Epoch AI · Jul 2024Independent
Independent1.7%
#76/85 open
- MMLU-Pro
TIGER-LabIndependent
Independent44.3%
#87/144 open
- SciCode
Epoch AI · Jul 2024Lab-reported
Lab-reported13.2%
#64/65 open
- MATH Level 5
Epoch AI · Jul 2024Independent
Independent22.9%
#28/39 open
In the 24 GB class
34 models fit 24 GBSame Score, only models that fit 24 GB at their best quant: the ranking a 24 GB owner actually chooses from.
- #1
Qwen3.8 27BAlibaba
121.5
~38 tok/s · 17.9 GB
- #2
Qwen3.6 27BAlibaba
111.4
~38 tok/s · 17.9 GB
- #3
Qwen3.6 35B A3BAlibaba
106.9
~151 tok/s · 22.0 GB
- #4
Gemma 4 31B ITGoogle
106.1
~32 tok/s · 21.7 GB
- #5
Qwen3.5 35B A3BAlibaba
105.6
~151 tok/s · 22.0 GB
- #6
Gemma 4 26B A4B ITGoogle
105.4
~171 tok/s · 16.5 GB
Alternatives to Llama 3.1 8B Instruct
Llama 3 family · 64 models →Official models with an llmrun Score, each answering one question; numbers are at the best quant that fits a 24 GB GPU.
Best alternative at this size
Qwen3.5 9B
Alibaba
- 36.9 higher Score
- 1.2 GB less VRAM
- 21 tok/s slower
Score 99.4 · 6.6 GB · ~103 tok/s
Much faster, similar quality
GPT OSS 20B
OpenAI
- 33.7 higher Score
- 5.3 GB more VRAM
- 66 tok/s faster
Score 96.2 · 13.1 GB · ~190 tok/s
Other vendor
Qwen3 8B
Alibaba
- 31.3 higher Score
- 0.2 GB less VRAM
- 5 tok/s slower
Score 93.8 · 7.6 GB · ~119 tok/s
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3 14BAlibaba | 97.9 | 11.9 GB | ~69 | similar score |
| Phi 4Microsoft | 83.8 | 12.5 GB | ~69 | similar score |
| Gemma 3 12B ITGoogle | 74.2 | 10.9 GB | ~82 | similar score |
| Mistral Nemo Instruct 2407Mistral AI | 65.4 | 11 GB | ~81 | similar score |
| Qwen2.5 7B InstructAlibaba | 65.1 | 5.8 GB | ~131 | similar score |
| Gemma 3 4B ITGoogle | 64.0 | 5 GB | ~231 | similar score |
| 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 |
Specifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 8.0B
- Context Length
- 131,072 tokens
- Release Date
- 2024-07-18
- License
- Llama 3.1 Community
Frequently Asked Questions
How much VRAM does Llama 3.1 8B Instruct need?
Llama 3.1 8B Instruct 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 Instruct?
For Llama 3.1 8B Instruct, Q4_K_M (5.3 GB) offers the best balance of quality and VRAM usage. Q4_K_L (5.4 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 Instruct on a Mac?
Yes, but only at lower quantizations. The smallest Mac that can run Llama 3.1 8B Instruct is Mac Mini M1 (8 GB) at IQ2_XXS; 156 of the 156 Macs we list can run it at some quantization. For Q4_K_M (5.3 GB) you need a Mac with more unified memory.
Can I run Llama 3.1 8B Instruct locally?
Yes — Llama 3.1 8B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 3.1 8B Instruct?
At Q4_K_M, Llama 3.1 8B Instruct 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 Instruct?
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 Instruct?
52 consumer GPUs can run Llama 3.1 8B Instruct 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 Instruct?
159 devices with unified memory can run Llama 3.1 8B Instruct at Q4_K_M (5.3 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.