llmrun
MetaLlama 3

Llama 3.1 70B Instruct — Hardware Requirements & GPU Compatibility

ChatFunctions

Meta Llama 3.1 70B Instruct is a 70.6-billion parameter instruction-tuned model from Meta's Llama 3.1 family. It features a 128K token context window and is optimized for chat, tool use, and complex reasoning tasks. The 70B size offers a strong balance between capability and hardware requirements, running well on multi-GPU setups or high-VRAM workstation cards. This model was trained on over 15 trillion tokens and fine-tuned with reinforcement learning from human feedback (RLHF). It excels at coding assistance, mathematical reasoning, and multilingual dialogue. Released under the Llama 3.1 Community License.

70.6B params · dense131K contextLlama 3.1 Community91.6K downloads1.0K likes206.4K quant downloads

Doesn't fit a 24 GB GPU

Smallest consumer fit → Mac Studio M1 Max (32 GB) · IQ2_XS · 28 GB · ~12 tok/s

Macs from32 GB IQ2_XS · ~12 tok/s

Score 77.9#93 of 106 open#39 of 51 that fit ≤64 GBLlama 3.1 Community

Not set up for it?

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

llmrun Composite Score →

77.9

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

Run Llama 3.1 70B Instruct

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
one command · needs Ollama installed
$ollama run llama3.1:70b

Q4_K_M · needs ~51.3 GB · 16K context

Download: Meta-Llama-3.1-70B-Instruct-Q4_K_M.gguf42.52 GBbartowski

install, OS, other quants

Ways to runOther ways

each opens step-by-step

Run in cloud

Your own private copy · Q4_K_M · 96 GB GPU

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How Much VRAM Does Llama 3.1 70B Instruct Need?

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

QuantizationVRAM+32KOn RTX 4090 (reference)
33.0 GB43.1 GB2-bit · K-quant — 2-bit quantization with K-quant improvementsDoes not fit: needs 37.7 GB
34.0 GB44.1 GB3-bit small — 3-bit small quantizationDoes not fit: needs 38.7 GB
37.8 GB48 GB3-bit medium — 3-bit medium quantizationDoes not fit: needs 42.6 GB
38.8 GB48.9 GB4-bit · legacy — 4-bit legacy quantizationDoes not fit: needs 43.5 GB
46.6 GB56.7 GB4-bit medium — 4-bit medium quantization — most popular sweet spotDoes not fit: needs 51.3 GB
55.3 GB65.4 GB5-bit medium — 5-bit medium quantization — good quality/size tradeoffDoes not fit: needs 60 GB
64.0 GB74.2 GB6-bit, very good — 6-bit quantization, very good qualityDoes not fit: needs 68.8 GB
77.6 GB87.7 GB8-bit, near-lossless — 8-bit quantization, near-losslessDoes not fit: needs 82.3 GB
est.calculated, no published file yet

Selected Q4_K_M

4-bit medium quantization

51.3GBat 16K context

Weights
45.6 GB
KV cache
5.4 GB
Overhead
0.3 GB

Doesn't fit RTX 4090 (reference) · needs 27.3 GB more

≥10 tok/s needs ≥713 GB/s · M3 Ultra ~12, M2 Ultra ~12

Or .

Which Hardware Can Run Llama 3.1 70B Instruct?

Q4_K_M · 46.6 GB

Llama 3.1 70B Instruct (Q4_K_M) requires 46.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 61+ GB is recommended. No consumer GPU has enough memory. 52 devices with unified memory can run Llama 3.1 70B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (64 GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M
8–16 GB · RTX 4060, RTX 3060 12GB, RTX 5060 Ti 16GBDoesn't fit — smallest quant needs 26.1 GB8 GB tier →12 GB tier →16 GB tier →
24 GB · RTX 4090referenceDoesn't fit — smallest quant needs 26.1 GB24 GB tier →
Mac 32 GB · Mac Mini M4IQ2_XS · 28 GB · ~4 tok/s · 16KMacs →
Mac 64 GB · Mac Studio M4 MaxQ4_K_M · 51.3 GB · ~8 tok/s · 32KMacs →

How good is Llama 3.1 70B Instruct?

The llmrun Score merges 7 public benchmark boards into a single figure, where 100 represents the average for open models.

Score vs VRAM in the ≤64 GB tier

  • Llama 3.1 70B Instruct
  • Other models
  • Best Score for its VRAM

#39 of 51 that fit ≤64 GB

Each dot is a model at the best quant that fits ≤64 GB. 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 ≤64 GB ranking →

Benchmarks

The 6 boards that weigh most in the llmrun Score, of 10 with a result for this model; badges show who ran the test.

  • GPQA Diamond

    Epoch AI · Jul 2024Independent

    44.2%

    #68/100 open

    Independent
  • LMArena Text

    LMArena · Oct 2026Arena

    1293.4

    #110/188 open

    Arena
  • AIME 2024/2025

    Epoch AI · Jul 2024Independent

    3.6%

    #73/88 open

    Independent
  • MMLU-Pro

    TIGER-LabIndependent

    62.8%

    #53/146 open

    Independent
  • MATH Level 5

    Epoch AI · Jul 2024Independent

    36.7%

    #23/39 open

    Independent
  • MMLU

    Epoch AI · Jul 2024Lab-reported

    80.1%

    #10/85 open

    Lab-reported
All 10 Llama 3.1 70B Instruct benchmarks

In the ≤64 GB class

51 models fit ≤64 GB

Same Score, only models that fit ≤64 GB at their best quant: the ranking a ≤64 GB owner actually chooses from.

  1. #1

    Qwen3.8 27BAlibaba

    122.0

    ~56 tok/s · 17.9 GB

  2. #2

    Qwen3.6 27BAlibaba

    111.2

    ~56 tok/s · 17.9 GB

  3. #3

    Qwen3.5 122B A10BAlibaba

    109.4

    ~62 tok/s · 63.0 GB

  4. #4

    Qwen3.6 35B A3BAlibaba

    106.8

    ~91 tok/s · 22.0 GB

  5. #5

    Gemma 4 31B ITGoogle

    106.7

    ~48 tok/s · 21.7 GB

  6. #6

    Gemma 4 26B A4B ITGoogle

    105.8

    ~114 tok/s · 16.5 GB

Full ≤64 GB ranking →AMD Instinct MI210 page →

Alternatives to Llama 3.1 70B 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
Qwen3.5 122B A10BAlibaba109.475.5 GB—needs > 24 GBsimilar score
GLM 4.5 AirZ.ai98.667.6 GB—needs > 24 GBsimilar score
GPT OSS 120BOpenAI97.570.8 GB—needs > 24 GBsimilar score
NVIDIA Nemotron 3 Super 120B A12B BF16NVIDIA95.874.7 GB—needs > 24 GBsimilar score
Mistral Large Instruct 2411Mistral AI82.579.8 GB—needs > 24 GBsimilar score
Llama 4 Scout 17B 16E InstructMeta82.177 GB—needs > 24 GBsimilar score
Mistral Large Instruct 2407Mistral AI81.786.4 GB—needs > 24 GBsimilar score
C4ai Command A 03 2025Cohere79.878.7 GB—needs > 24 GBsimilar score
Gemma 3 27B ITGoogle79.421.7 GB~36similar score
Mistral Small 3.1 24B Instruct 2503Mistral AI79.018.1 GB~43similar score
Fine-tunes & variants(1)

Specifications

Publisher
Meta
Family
Llama 3
Parameters
70.6B
Context Length
131,072 tokens
Release Date
2024-07-16
License
Llama 3.1 Community

Resources

Frequently Asked Questions

How much VRAM does Llama 3.1 70B Instruct need?

Llama 3.1 70B Instruct requires 46.6 GB of VRAM at Q4_K_M, or 155.2 GB at BF16.

VRAM = Weights + KV Cache + Overhead

Weights = 70.6B × 4.8 bits ÷ 8 = 42.3 GB

KV Cache + Overhead ≈ 4.3 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

46.6 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 4090 run Llama 3.1 70B Instruct?

Yes, at IQ2_XS (23.3 GB) or lower. Higher quantizations like IQ2_S (24.3 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Llama 3.1 70B Instruct?

For Llama 3.1 70B Instruct, Q4_K_M (46.6 GB) offers the best balance of quality and VRAM usage. Q4_K_L (47.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 21.3 GB.

VRAM requirement by quantization

IQ2_XXS
21.3 GB
Q2_K
33.0 GB
Q3_K_L
39.8 GB
Q4_K_M ★
46.6 GB
Q5_0
48.5 GB
BF16
155.2 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Llama 3.1 70B Instruct on a Mac?

Yes — Mac Studio M1 Max (32 GB) and 84 other Macs can run Llama 3.1 70B Instruct, the smallest at IQ2_XS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 46.6 GB of usable unified memory (RAM minus macOS overhead), from Mac Pro M2 Ultra (64 GB).

Can I run Llama 3.1 70B Instruct locally?

Yes — Llama 3.1 70B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Llama 3.1 70B Instruct?

At Q4_K_M, Llama 3.1 70B Instruct can reach ~103 tok/s on AMD Instinct MI350X. 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 ÷ 46.6 × 0.65 = ~112 tok/s

Estimated speed at Q4_K_M (46.6 GB)

~112 tok/s
~112 tok/s
~103 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 70B Instruct?

At Q4_K_M, the download is about 42.33 GB. The full-precision BF16 version is 141.11 GB. The smallest option (IQ2_XXS) is 19.40 GB.

Which GPUs can run Llama 3.1 70B Instruct?

No single consumer GPU has enough VRAM to run Llama 3.1 70B Instruct at Q4_K_M (46.6 GB). Multi-GPU or professional hardware is required.

Which devices can run Llama 3.1 70B Instruct?

52 devices with unified memory can run Llama 3.1 70B Instruct at Q4_K_M (46.6 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.