llmrun
MetaLlama 3

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.

8.0B params · dense131K contextLlama 3.1 Community4.3M downloads8.8K likes1.1M quant downloads
Based on Meta Llama 3.1 8B
S

Runs great on a 24 GB GPU

Runs from8 GB Q4_K_M · 16KBest on24 GB Q4_K_M · ~124 tok/s · 64KMacs from16 GB Q4_K_M · ~26 tok/s

Score 62.5#104 of 106 open#35 of 36 that fit 24 GBLlama 3.1 Community

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

AOn your GTX 1080 Ti: Q4_K_M fits · 3.1 GB free · ~59 tok/s · up to 32K contextGTX 1080 Ti page →

llmrun Composite Score →

62.5

Benchmarks · tier chart ↓
Among 106 open modelsHow the Composite Score is built

Run Llama 3.1 8B Instruct

Best fit for your GTX 1080 Ti. 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 for your GTX 1080 Ti

Ollama
one command · needs Ollama installed
$ollama run llama3.1:8b

Q4_K_M fits fully · 3.1 GB free · 16K context

Download: Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf4.92 GBbartowski

install, OS, other quants

Ways to runOther ways

each opens step-by-step

Run in cloud

Your own private copy · Q4_K_M · 24 GB GPU

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How Much VRAM Does Llama 3.1 8B Instruct Need?

Select a quantization level; the listed hardware adheres to it.

QuantizationVRAM+32KOn your device
3.8 GB9.3 GB2-bit · K-quant — 2-bit quantization with K-quant improvementsFits: 32K context · ~84 tok/s
3.9 GB9.4 GB3-bit small — 3-bit small quantizationFits: 32K context · ~82 tok/s
4.3 GB9.8 GB3-bit medium — 3-bit medium quantizationFits: 32K context · ~73 tok/s
4.4 GB9.9 GB4-bit · legacy — 4-bit legacy quantizationFits: 32K context · ~71 tok/s
5.3 GB10.8 GB4-bit medium — 4-bit medium quantization — most popular sweet spotFits: 32K context · ~59 tok/s
6.3 GB11.8 GB5-bit medium — 5-bit medium quantization — good quality/size tradeoffFits: 16K context · ~50 tok/s
7.3 GB12.8 GB6-bit, very good — 6-bit quantization, very good qualityFits: 16K context · ~43 tok/s
8.8 GB14.3 GB8-bit, near-lossless — 8-bit quantization, near-losslessFits: 8K context · ~36 tok/s

Selected Q4_K_M

4-bit medium quantization

7.9GBat 16K context

Weights
4.6 GB
KV cache
2.9 GB
Overhead
0.3 GB

Fits GTX 1080 Ti · 3.1 GB free · ~59 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 GB

Llama 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. 151 devices with unified memory can run Llama 3.1 8B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M
8 GB · RTX 4060Q4_K_M · 7.9 GB · ~33 tok/s · 16K8 GB tier →
12 GB · RTX 3060 12GBQ4_K_M · 7.9 GB · ~44 tok/s · 32K12 GB tier →
16 GB · RTX 5060 Ti 16GBQ4_K_M · 7.9 GB · ~55 tok/s · 32K16 GB tier →
24 GB · RTX 4090referenceQ4_K_M · 7.9 GB · ~124 tok/s · 64K24 GB tier →
Mac 32 GB · Mac Mini M4Q4_K_M · 7.9 GB · ~16 tok/s · 128KMacs →
Mac 64 GB · Mac Studio M4 MaxQ4_K_M · 7.9 GB · ~72 tok/s · 128KMacs →

How good is Llama 3.1 8B Instruct?

The llmrun Score merges 9 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

#35 of 36 that fit 24 GB10 of 36 fit your GTX 1080 Ti (11 GB)

Each dot is a model at the best quant that fits a 24 GB GPU. The line steps up to the best Score available at each amount of 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 15 with a result for this model; badges show who ran the test.

  • GPQA Diamond

    Epoch AI · Jul 2024Independent

    27.0%

    #89/100 open

    Independent
  • LMArena Text

    LMArena · Oct 2026Arena

    1211.2

    #143/188 open

    Arena
  • AIME 2024/2025

    Epoch AI · Jul 2024Independent

    1.7%

    #79/88 open

    Independent
  • MMLU-Pro

    TIGER-LabIndependent

    44.3%

    #88/146 open

    Independent
  • SciCode

    Epoch AI · Jul 2024Lab-reported

    13.2%

    #71/72 open

    Lab-reported
  • MATH Level 5

    Epoch AI · Jul 2024Independent

    22.9%

    #28/39 open

    Independent
All 15 Llama 3.1 8B Instruct benchmarks

In the 24 GB class

36 models fit 24 GB

Same Score, only models that fit 24 GB at their best quant: the ranking a 24 GB owner actually chooses from.

  1. #1

    Qwen3.8 27BAlibaba

    122.0

    ~38 tok/s · 17.9 GB

  2. #2

    Qwen3.6 27BAlibaba

    111.2

    ~38 tok/s · 17.9 GB

  3. #3

    Qwen3.6 35B A3BAlibaba

    106.8

    ~151 tok/s · 22.0 GB

  4. #4

    Gemma 4 31B ITGoogle

    106.7

    ~32 tok/s · 21.7 GB

  5. #5

    Gemma 4 26B A4B ITGoogle

    105.8

    ~171 tok/s · 16.5 GB

  6. #6

    Qwen3.5 35B A3BAlibaba

    105.8

    ~151 tok/s · 22.0 GB

Full 24 GB ranking →RTX 4090 page →

Alternatives to Llama 3.1 8B Instruct

Llama 3 family · 65 models →

Official models with an llmrun Score, each answering one question; numbers are at the best quant that fits a 24 GB GPU.

Similar models

10
ModelScoreVRAMtok/sWhy it is similar
Qwen3 14BAlibaba98.011.9 GB~69similar score
Qwen3 8BAlibaba93.97.6 GB~119similar score
Phi 4Microsoft83.912.5 GB~69similar score
Gemma 3 12B ITGoogle74.210.9 GB~82similar score
Gemma 2 9B ITGoogle68.07.3 GB~107similar score
Mistral Nemo Instruct 2407Mistral AI65.411 GB~81similar score
Qwen2.5 7B InstructAlibaba65.15.8 GB~131similar score
Gemma 3 4B ITGoogle64.05 GB~231similar score
ALoDLM 8BAmazon—7.6 GB~119same size
Llm Jp 4.1 8B Thinkingllm-jp—7.6 GB~115same size
Fine-tunes & variants(8)

Specifications

Publisher
Meta
Family
Llama 3
Parameters
8.0B
Context Length
131,072 tokens
Release Date
2024-07-18
License
Llama 3.1 Community

Resources

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

5.3 GB

Learn more about VRAM estimation →

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

IQ2_XXS
2.4 GB
Q2_K
3.8 GB
IQ4_XS
4.8 GB
Q4_K_M ★
5.3 GB
Q5_0
5.5 GB
BF16
17.7 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Llama 3.1 8B Instruct on a Mac?

Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Llama 3.1 8B Instruct. 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 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)

~981 tok/s
~124 tok/s
~981 tok/s
~906 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

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?

151 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, 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.