Meta Llama 3 70B Instruct — Hardware Requirements & GPU Compatibility

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Meta Llama 3 70B Instruct is a 70.6-billion parameter instruction-tuned model from Meta's Llama 3 release. It is fine-tuned for dialogue, coding assistance, and complex reasoning tasks using supervised fine-tuning and RLHF. At the time of release, it was among the most capable openly available models. The model supports an 8K token context window and requires substantial VRAM for local inference, typically needing multi-GPU setups or high-VRAM professional GPUs. It has been widely adopted for local deployment in quantized formats. Released under the Meta Llama 3 Community License.

70.6B params · denseLlama 3 CommunityApr 202437.1K downloads1.5K likes5.5K quant downloads

Based on Meta Llama 3 70B

Your hardware

Verdicts below are for the reference RTX 4090. Set yours to personalise the page.

Detection reads your browser's GPU name with no data sent anywhere.

Doesn't fit a 24 GB GPU

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

Macs from 32 GB (IQ2_XS · ~12 tok/s)

Score 74.3#95 of 106 open#41 of 51 that fit ≤64 GBLlama 3 Community

Not set up for it?

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

llmrun Composite Score →

74.3

90% range 68.5–80.1
#95 of 106 open models
#41 of 51 that fit ≤64 GB

Benchmarks · tier chart ↓

Among 106 open modelsHow the Composite Score is built

CodingNot enough data yet
AgentsNot enough data yet
MathNot enough data yet
ScienceNot enough data yet
ReasoningNot enough data yet

Run Meta Llama 3 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 hf.co/QuantFactory/Meta-Llama-3-70B-Instruct-GGUF:Q4_K_M

Q4_K_M · needs ~51.3 GB · 16K context

Download: Meta-Llama-3-70B-Instruct.Q4_K_M.gguf42.52 GBQuantFactory

install, OS, other quants

Ways to runOther ways

each opens step-by-step

Run in cloud

Runs on A100 80GB (28 GB headroom) · Q4_K_M

Generation speed: ~29 tok/s; generation speed
Cost per 1M output tokens: $11.07; per 1M output tokens

Deployon Vast.aiBest valuefrom $1.14/hr

Compare GPUs →

or

How Much VRAM Does Meta Llama 3 70B Instruct Need?

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

Quantization	VRAM	+32K	+128K	File	Quality	On RTX 4090 (reference)
	23.3 GB	33.4 GB	65.8 GB	21.1 GB	2-bit · i-quant extra small — Importance-weighted 2-bit, extra small	Fits: 4K context · ~28 tok/s
	33.0 GB	43.1 GB	75.5 GB	26.4 GB	2-bit · K-quant — 2-bit quantization with K-quant improvements	Does not fit: needs 37.7 GB
	34.0 GB	44.1 GB	76.4 GB	30.9 GB	3-bit small — 3-bit small quantization	Does not fit: needs 38.7 GB
	37.8 GB	48 GB	80.3 GB	34.3 GB	3-bit medium — 3-bit medium quantization	Does not fit: needs 42.6 GB
	38.8 GB	48.9 GB	81.3 GB	40 GB	4-bit · legacy — 4-bit legacy quantization	Does not fit: needs 43.5 GB
	39.8 GB	49.9 GB	82.3 GB	37.1 GB	3-bit large — 3-bit large quantization	Does not fit: needs 44.5 GB
	43.7 GB	53.8 GB	86.2 GB	44.3 GB	4-bit · legacy+ — 4-bit legacy quantization with offset	Does not fit: needs 48.4 GB
	43.7 GB	53.8 GB	86.2 GB	40.3 GB	4-bit small — 4-bit small quantization	Does not fit: needs 48.4 GB
	46.6 GB	56.7 GB	89.1 GB	42.5 GB	4-bit medium — 4-bit medium quantization — most popular sweet spot	Does not fit: needs 51.3 GB
	48.5 GB	58.6 GB	91 GB	48.7 GB	5-bit · legacy — 5-bit legacy quantization	Does not fit: needs 53.2 GB
	53.4 GB	63.5 GB	95.9 GB	53 GB	5-bit · legacy+ — 5-bit legacy quantization with offset	Does not fit: needs 58.1 GB
	53.4 GB	63.5 GB	95.9 GB	48.7 GB	5-bit small — 5-bit small quantization	Does not fit: needs 58.1 GB
	55.3 GB	65.4 GB	97.8 GB	50 GB	5-bit medium — 5-bit medium quantization — good quality/size tradeoff	Does not fit: needs 60 GB
	64.0 GB	74.2 GB	106.5 GB	57.9 GB	6-bit, very good — 6-bit quantization, very good quality	Does not fit: needs 68.8 GB
	77.6 GB	87.7 GB	120.1 GB	75 GB	8-bit, near-lossless — 8-bit quantization, near-lossless	Does not fit: needs 82.3 GB
	155.2 GB	165.3 GB	197.7 GB	~141.1 GB	16-bit · bfloat — Brain floating point 16 — preferred for training	Does not fit: needs 159.9 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 Meta Llama 3 70B Instruct?

Q4_K_M · 46.6 GB

Meta Llama 3 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 Meta Llama 3 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

Typical hardwareQuantVRAMtok/sContext that fits

8–16 GB · RTX 4060, RTX 3060 12GB, RTX 5060 Ti 16GBDoesn't fit — smallest quant needs 28 GB8 GB tier →12 GB tier →16 GB tier →

24 GB · RTX 4090referenceDoesn't fit — smallest quant needs 28 GB24 GB tier →

Mac 32 GB · Mac Mini M4IQ2_XS · 28 GB · ~4 tok/s · 16K28~416KMacs →

Mac 64 GB · Mac Studio M4 MaxQ4_K_M · 51.3 GB · ~8 tok/s · 32K51.3~832KMacs →

How good is Meta Llama 3 70B Instruct?

The llmrun Score merges 4 public benchmark boards into a single figure, where 100 represents the average for open models.

Score vs VRAM in the ≤64 GB tier

Meta Llama 3 70B Instruct
Other models
Best Score for its VRAM
x: VRAM (GB, log scale) · y: llmrun Score

#41 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

GPQA Diamond
Epoch AI · Apr 2024Independent
40.6%
#72/100 open
Independent
LMArena Text
LMArena · Oct 2026Arena
1276.6
#120/188 open
Arena
AIME 2024/2025
Epoch AI · Apr 2024Independent
4.3%
#72/88 open
Independent
MMLU-Pro
TIGER-LabIndependent
56.2%
#64/146 open
Independent
MATH Level 5
Epoch AI · Apr 2024Independent
22.6%
#30/39 open
Independent
MMLU
Epoch AI · Apr 2024Lab-reported
79.3%
#13/85 open
Lab-reported

All 7 Meta Llama 3 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
Qwen3.8 27BAlibaba
122.0
~56 tok/s · 17.9 GB
#2
Qwen3.6 27BAlibaba
111.2
~56 tok/s · 17.9 GB
#3
Qwen3.5 122B A10BAlibaba
109.4
~62 tok/s · 63.0 GB
#4
Qwen3.6 35B A3BAlibaba
106.8
~91 tok/s · 22.0 GB
#5
Gemma 4 31B ITGoogle
106.7
~48 tok/s · 21.7 GB
#6
Gemma 4 26B A4B ITGoogle
105.8
~114 tok/s · 16.5 GB

Full ≤64 GB ranking →AMD Instinct MI210 page →

Alternatives to Meta Llama 3 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.

Best alternative at this size
Qwen2.5 72B Instruct
Alibaba
- 7.9 higher Score
- 2 GB less VRAM
Score 82.2 · 49.3 GB
Compare with Meta Llama 3 70B Instruct Open Qwen2.5 72B Instruct
Other vendor
Mistral Medium 3.5 128B
Mistral AI
- 27.8 higher Score
- 31.5 GB more VRAM
Score 102.1 · 82.8 GB
Compare with Meta Llama 3 70B Instruct Open Mistral Medium 3.5 128B

Similar models

10tok/s on RTX 4090

Model	Score	VRAM	tok/s	Why it is similar
Qwen3.5 122B A10BAlibaba	109.4	75.5 GB	—needs > 24 GB	similar score
GLM 4.5 AirZ.ai	98.6	67.6 GB	—needs > 24 GB	similar score
GPT OSS 120BOpenAI	97.5	70.8 GB	—needs > 24 GB	similar score
NVIDIA Nemotron 3 Super 120B A12B BF16NVIDIA	95.8	74.7 GB	—needs > 24 GB	similar score
Mistral Large Instruct 2411Mistral AI	82.5	79.8 GB	—needs > 24 GB	similar score
Llama 4 Scout 17B 16E InstructMeta	82.1	77 GB	—needs > 24 GB	similar score
Mistral Large Instruct 2407Mistral AI	81.7	86.4 GB	—needs > 24 GB	similar score
C4ai Command A 03 2025Cohere	79.8	78.7 GB	—needs > 24 GB	similar score
Mistral Small 3.1 24B Instruct 2503Mistral AI	79.0	18.1 GB	~43	similar score
Gemma 3 12B ITGoogle	74.2	10.9 GB	~82	similar score

Fine-tunes & variants(1)community builds of Meta Llama 3 70B Instruct

Llama3 OpenBioLLM 70B

Specifications

Publisher: Meta
Family: Llama 3
Parameters: 70.6B
Release Date: 2024-04-17
License: Llama 3 Community

Resources

Frequently Asked Questions

How much VRAM does Meta Llama 3 70B Instruct need?

Meta Llama 3 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

Q4_K_M

46.6 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 4090 run Meta Llama 3 70B Instruct?

Yes, at IQ2_XS (23.3 GB) or lower. Higher quantizations like Q2_K (33.0 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Meta Llama 3 70B Instruct?

For Meta Llama 3 70B Instruct, Q4_K_M (46.6 GB) offers the best balance of quality and VRAM usage. Q5_0 (48.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 23.3 GB.

VRAM requirement by quantization

IQ2_XS

23.3 GB~57%

Q4_0

38.8 GB~85%

Q4_K_M ★

46.6 GB~89%

Q5_0

48.5 GB~90%

Q5_K_S

53.4 GB~92%

BF16

155.2 GB~100%

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Meta Llama 3 70B Instruct on a Mac?

Yes — Mac Studio M1 Max (32 GB) and 84 other Macs can run Meta Llama 3 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 Meta Llama 3 70B Instruct locally?

Yes — Meta Llama 3 70B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Meta Llama 3 70B Instruct?

At Q4_K_M, Meta Llama 3 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)

NVIDIA B200

~112 tok/s

NVIDIA B300

~112 tok/s

AMD Instinct MI350X

~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 Meta Llama 3 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_XS) is 21.17 GB.

Which GPUs can run Meta Llama 3 70B Instruct?

No single consumer GPU has enough VRAM to run Meta Llama 3 70B Instruct at Q4_K_M (46.6 GB). Multi-GPU or professional hardware is required.

Which devices can run Meta Llama 3 70B Instruct?

52 devices with unified memory can run Meta Llama 3 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.

Run Meta Llama 3 70B Instruct

51.3 GB VRAM at 16K context

Works on macOS, Windows and Linux.

Install Ollama (Linux)
$curl -fsSL https://ollama.com/install.sh | sh
Install Ollama (macOS / Windows)
Download and run the app; it installs the ollama command.
Download Ollama ↗
Run Q4_K_M
$ollama run hf.co/QuantFactory/Meta-Llama-3-70B-Instruct-GGUF:Q4_K_M
Raise the context window
Type it inside the chat. For the API server instead, start it with OLLAMA_CONTEXT_LENGTH=16384 ollama serve.
$/set parameter num_ctx 16384
A larger context needs more memory than the weights alone.
Use the API
Ollama serves its API on http://localhost:11434.

Works on macOS, Windows and Linux.

Install LM Studio
Desktop app for macOS (Apple silicon), Windows and Linux.
lmstudio.ai ↗
Download the model
In the app press Ctrl/Cmd + Shift + M and paste https://huggingface.co/QuantFactory/Meta-Llama-3-70B-Instruct-GGUF, then pick Q4_K_M. Or use the CLI (run LM Studio once first):
$lms get https://huggingface.co/QuantFactory/Meta-Llama-3-70B-Instruct-GGUF@Q4_K_M
Open on Hugging Face ↗
Load and chat
Open the model loader, select the model and set Context Length to 16384. Leave GPU offload at maximum if it fits.
Start the local server (optional)
$lms server start

Works on macOS, Windows and Linux.

Install llama.cpp (macOS)
MacPorts, Nix and conda-forge also work.
$brew install llama.cpp
Install options ↗
Install llama.cpp (Windows)
$winget install llama.cpp
Install llama.cpp (Linux)
Download the prebuilt llama-*-bin-ubuntu-* archive for your GPU backend (CPU, Vulkan) from the releases page, unpack it and run the binaries from that folder. Homebrew (brew install llama.cpp) and conda-forge also work on Linux.
llama.cpp releases ↗
Start the server
Downloads the model on first run. The chat UI and an OpenAI-compatible API are at http://localhost:8080.
$llama-server -hf QuantFactory/Meta-Llama-3-70B-Instruct-GGUF:Q4_K_M -c 16384 -ngl 99 --port 8080

Works on macOS, Windows and Linux.

Option A: Docker Model Runner
Docker Desktop: Settings > AI > Enable Docker Model Runner. Docker Engine on Linux: install the docker-model-plugin package.
$docker model run hf.co/QuantFactory/Meta-Llama-3-70B-Instruct-GGUF:Q4_K_M
Model Runner docs ↗
The quant tag after the repo is how Hugging Face cards write it; if your Docker version rejects it, drop the tag and pick the quant by repo.
Option B: llama.cpp server image (NVIDIA)
Needs the NVIDIA Container Toolkit. Models are cached in the llama-cache volume. Open http://localhost:8080.
$docker run --gpus all -p 8080:8080 -v llama-cache:/root/.cache ghcr.io/ggml-org/llama.cpp:server-cuda -hf QuantFactory/Meta-Llama-3-70B-Instruct-GGUF:Q4_K_M -c 16384 -ngl 99 --host 0.0.0.0 --port 8080
Image tags (cuda13, vulkan, rocm) ↗
Cache path inside the image is not in the docs; remove the -v flag if the volume complains.

Linux only.

Install vLLM
Linux only; on Windows run it inside WSL2. NVIDIA GPU shown; AMD ROCm, Intel and CPU builds have their own install pages.
$uv venv --python 3.12 --seed
$source .venv/bin/activate
$uv pip install vllm --torch-backend=auto
vLLM installation ↗
Serve the model
OpenAI-compatible API at http://localhost:8000/v1.
$vllm serve meta-llama/Meta-Llama-3-70B-Instruct --max-model-len 16384
Runs the original weights, not the Q4_K_M GGUF; needs ~155 GB of GPU memory.

Works on macOS, Windows and Linux.

Install Jan
jan.ai ↗
Find the model
Open the Hub tab, paste this model ID in Search, and choose Q4_K_M if offered.
$QuantFactory/Meta-Llama-3-70B-Instruct-GGUF
Use the local API (optional)
Settings > Local API Server > Start Server. It listens on http://127.0.0.1:1337.

Rent GPUs hourly to run Q4_K_M right there. Pricing comes from partner marketplaces and varies.

Available GPU rentals for Q4_K_M, prioritized by value
GPU	Speed	$/1M tokens	Rent
A100 80GBCheapestCheapest28 GB free	~29 tok/s	$11.07	Deploy on Vast.aifrom $1.14/hr (opens in new tab)	28 GB free · ~29 tok/s · $11.07 / 1M tokens
RTX PRO 6000 Max-Q (96 GB)44 GB free	~25 tok/s	$14.79	Deploy on Vast.aifrom $1.33/hr (opens in new tab)	96 GB · 44 GB free · ~25 tok/s · $14.79 / 1M tokens
A100 80GB28 GB free	~29 tok/s	$13.55	Deploy on RunPodfrom $1.39/hr (opens in new tab)	28 GB free · ~29 tok/s · $13.55 / 1M tokens
RTX PRO 6000 (96 GB)44 GB free	~25 tok/s	$17.06	Deploy on Vast.aifrom $1.54/hr (opens in new tab)	96 GB · 44 GB free · ~25 tok/s · $17.06 / 1M tokens
RTX PRO 6000 Server (96 GB)44 GB free	~22 tok/s	$21.05	Deploy on RunPodfrom $1.69/hr (opens in new tab)	96 GB · 44 GB free · ~22 tok/s · $21.05 / 1M tokens
RTX PRO 6000 Server (96 GB)44 GB free	~22 tok/s	$21.65	Deploy on Vast.aifrom $1.74/hr (opens in new tab)	96 GB · 44 GB free · ~22 tok/s · $21.65 / 1M tokens
H100 (80 GB)28 GB free	~47 tok/s	$15.97	Deploy on RunPodfrom $2.69/hr (opens in new tab)	80 GB · 28 GB free · ~47 tok/s · $15.97 / 1M tokens
H200 (141 GB)89 GB free	~67 tok/s	$14.88	Deploy on RunPodfrom $3.59/hr (opens in new tab)	141 GB · 89 GB free · ~67 tok/s · $14.88 / 1M tokens

Compare GPUs →