AlibabaQwen 2.5Qwen2ForCausalLM

Qwen2.5 72B Instruct — Hardware Requirements & GPU Compatibility

ChatFunctions

Qwen2.5 72B Instruct is the flagship model of the Qwen 2.5 series from Alibaba Cloud, with 72.7 billion parameters. It is instruction-tuned for conversational use and excels across reasoning, coding, mathematics, and multilingual tasks. Qwen2.5 72B delivers performance competitive with leading open-weight 70B-class models while supporting a 128K token context window and structured output generation. The model uses a Transformer architecture with grouped-query attention and was pretrained on a diverse multilingual corpus of over 18 trillion tokens. Running it locally requires high-VRAM GPUs or multi-GPU setups, though quantized formats make it accessible on workstation-class hardware. Released under the Apache 2.0 license.

72.7B params · dense33K contextOtherSep 2024185.7K downloads1.0K likes365.5K quant downloads

Based on Qwen2.5 72B

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 → Snapdragon X Elite Copilot+ PC · IQ2_XXS · 25.7 GB · ~4 tok/s

Macs from 32 GB (IQ2_S · ~12 tok/s)

Score 82.2#84 of 106 open#30 of 51 that fit ≤64 GBOther

Not set up for it?

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

llmrun Composite Score →

82.2

90% range 77.8–86.5
#84 of 106 open models
#30 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
Reasoning88.090% range 79–97

Run Qwen2.5 72B 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.

Ways to runOther ways

each opens step-by-step

Run in cloud

Your own private copy · Q4_K_M · 96 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

Run Qwen2.5 72B Instruct

49.3 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 qwen2.5:72b
Official Ollama library build. For the exact Q4_K_M file from bartowski/Qwen2.5-72B-Instruct-GGUF: ollama run hf.co/bartowski/Qwen2.5-72B-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/bartowski/Qwen2.5-72B-Instruct-GGUF, then pick Q4_K_M. Or use the CLI (run LM Studio once first):
$lms get https://huggingface.co/bartowski/Qwen2.5-72B-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 bartowski/Qwen2.5-72B-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/bartowski/Qwen2.5-72B-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 bartowski/Qwen2.5-72B-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 Qwen/Qwen2.5-72B-Instruct --max-model-len 16384
Runs the original weights, not the Q4_K_M GGUF; needs ~160 GB of GPU memory.

macOS only.

MLX supports only macOS, so follow these steps for that setup.

Install mlx-lm
$pip install mlx-lm
Chat in the terminal
$mlx_lm.chat --model mlx-community/Qwen2.5-72B-Instruct-4bit
Downloads the model on first run. The MLX repo has its own quantisation; it may not match the quant selected above.
Or start a local server
Listens on http://localhost:8080.
$mlx_lm.server --model mlx-community/Qwen2.5-72B-Instruct-4bit

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.
$bartowski/Qwen2.5-72B-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 80GBCheapestCheapest30 GB free	~30 tok/s	$10.62	Deploy on Vast.aifrom $1.14/hr (opens in new tab)	30 GB free · ~30 tok/s · $10.62 / 1M tokens
RTX PRO 6000 Max-Q (96 GB)46 GB free	~26 tok/s	$14.17	Deploy on Vast.aifrom $1.33/hr (opens in new tab)	96 GB · 46 GB free · ~26 tok/s · $14.17 / 1M tokens
A100 80GB30 GB free	~30 tok/s	$13.00	Deploy on RunPodfrom $1.39/hr (opens in new tab)	30 GB free · ~30 tok/s · $13.00 / 1M tokens
RTX PRO 6000 (96 GB)46 GB free	~26 tok/s	$16.34	Deploy on Vast.aifrom $1.54/hr (opens in new tab)	96 GB · 46 GB free · ~26 tok/s · $16.34 / 1M tokens
RTX PRO 6000 Server (96 GB)46 GB free	~23 tok/s	$20.15	Deploy on RunPodfrom $1.69/hr (opens in new tab)	96 GB · 46 GB free · ~23 tok/s · $20.15 / 1M tokens
RTX PRO 6000 Server (96 GB)46 GB free	~23 tok/s	$20.72	Deploy on Vast.aifrom $1.74/hr (opens in new tab)	96 GB · 46 GB free · ~23 tok/s · $20.72 / 1M tokens
H100 (80 GB)30 GB free	~49 tok/s	$15.28	Deploy on RunPodfrom $2.69/hr (opens in new tab)	80 GB · 30 GB free · ~49 tok/s · $15.28 / 1M tokens
H200 (141 GB)91 GB free	~70 tok/s	$14.25	Deploy on RunPodfrom $3.59/hr (opens in new tab)	141 GB · 91 GB free · ~70 tok/s · $14.25 / 1M tokens

Compare GPUs →

Your own private copy of Qwen2.5 72B Instruct · Q4_K_M · OpenAI-compatible API

The pod’s first start downloads ~44 GB, billed while it loads.

Open the pod on RunPod
Deploy on RunPodRTX PRO 6000 · $2.49/hr (opens in new tab)
New to RunPod? Sign up and add credit first. If that GPU is gone, pick another with at least 96 GB.
2
Set the model and your key
Press Set overrides → Environment variables. The first variable is already there; set its value:
Key
LLAMA_ARG_HF_REPO
Value
bartowski/Qwen2.5-72B-Instruct-GGUF:Q4_K_M
Press Add Environment variable for your key:
Key
LLAMA_API_KEY
Value
Make up your own key
A long password works. You’ll paste it below as the API key. Without it, anyone who finds the pod’s address can use it.Then Set overrides.
Deploy
Press Deploy Pod. Usually ready in 15–60 min.

Use it

RunPod → your pod → Connect → HTTP 8080, copy the link. The key is the one you made up in step 2.

Endpoint URL

API key

Chats and keys stay only in this browser.

https://<POD_ID>-8080.proxy.runpod.net/v1

RunPod → your pod → Connect → HTTP 8080 gives this address. The API key is the one from step 2.

from openai import OpenAI
client = OpenAI(base_url="https://<POD_ID>-8080.proxy.runpod.net/v1", api_key="YOUR_KEY")
r = client.chat.completions.create(
    model="any",
    messages=[{"role": "user", "content": "Hello!"}])
print(r.choices[0].message.content)

Works with any OpenAI client.

https://<POD_ID>-8080.proxy.runpod.net/v1

RunPod → your pod → Connect → HTTP 8080 gives this address. The API key is the one from step 2.

Free on Windows, macOS, Linux, iOS, Android and the web.

Open the web app ↗ (opens in a new tab)Download ↗ (opens in a new tab)

1. Settings → Model Provider → Add → Add Custom Provider
2. API Mode OpenAI API Compatible; paste the API key and the base URL as API Host
3. Model → Fetch, pick the model
4. New chat → pick the provider → start chatting

Free and open source for Windows, macOS and Linux.

Download Jan ↗ (opens in a new tab)

1.Settings → next to Model Providers press +
2.API format OpenAI-compatible; Base URL is the base URL above (with /v1); API key is your key; Create
3.If no model appears, click + in Models and type any
4.New chat → pick the model → start chatting

How Much VRAM Does Qwen2.5 72B Instruct Need?

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

VRAM	+32K	+128K	File	Quality	On RTX 4090 (reference)
21.0 GB	31 GB	—	25.5 GB	2-bit · i-quant extreme compression — Importance-weighted 2-bit, extreme compression — significant quality loss	Fits: 8K context · ~31 tok/s
22.8 GB	32.9 GB	—	27.1 GB	2-bit · i-quant extra small — Importance-weighted 2-bit, extra small	Fits: 4K context · ~29 tok/s
23.7 GB	33.8 GB	—	27.9 GB	2-bit · i-quant small — Importance-weighted 2-bit, small	Fits: 2K context · ~28 tok/s
25.5 GB	35.6 GB	—	29.3 GB	2-bit · i-quant medium — Importance-weighted 2-bit, medium	Does not fit: needs 30.2 GB
29.1 GB	39.2 GB	—	31.8 GB	3-bit · i-quant — Importance-weighted 3-bit	Does not fit: needs 33.8 GB
30.1 GB	40.1 GB	—	29.6 GB	2-bit · small K-quant — 2-bit small K-quant	Does not fit: needs 34.8 GB
31.0 GB	41 GB	—	32.8 GB	3-bit · i-quant extra small — Importance-weighted 3-bit, extra small	Does not fit: needs 35.7 GB
31.9 GB	41.9 GB	—	34.5 GB	3-bit · i-quant small — Importance-weighted 3-bit, small	Does not fit: needs 36.6 GB
31.9 GB	41.9 GB	—	29.8 GB	2-bit · K-quant — 2-bit quantization with K-quant improvements	Does not fit: needs 36.6 GB
32.8 GB	42.9 GB	—	34.5 GB	3-bit small — 3-bit small quantization	Does not fit: needs 37.5 GB
33.7 GB	43.8 GB	—	35.5 GB	3-bit · i-quant medium — Importance-weighted 3-bit, medium	Does not fit: needs 38.4 GB
36.4 GB	46.5 GB	—	37.7 GB	3-bit medium — 3-bit medium quantization	Does not fit: needs 41.1 GB
37.3 GB	47.4 GB	—	41.4 GB	4-bit · legacy — 4-bit legacy quantization	Does not fit: needs 42 GB
38.2 GB	48.3 GB	—	39.5 GB	3-bit large — 3-bit large quantization	Does not fit: needs 42.9 GB
40.0 GB	50.1 GB	—	39.7 GB	4-bit · i-quant compact — Importance-weighted 4-bit, compact	Does not fit: needs 44.8 GB
41.9 GB	51.9 GB	—	43.9 GB	4-bit small — 4-bit small quantization	Does not fit: needs 46.6 GB
41.9 GB	51.9 GB	—	41.3 GB	4-bit · i-quant non-linear — Importance-weighted 4-bit, non-linear	Does not fit: needs 46.6 GB
41.9 GB	51.9 GB	—	45.7 GB	4-bit · legacy+ — 4-bit legacy quantization with offset	Does not fit: needs 46.6 GB
44.6 GB	54.7 GB	—	47.4 GB	4-bit medium — 4-bit medium quantization — most popular sweet spot	Does not fit: needs 49.3 GB
46.4 GB	56.5 GB	—	50.3 GB	5-bit · legacy — 5-bit legacy quantization	Does not fit: needs 51.1 GB
51.0 GB	61 GB	—	51.4 GB	5-bit small — 5-bit small quantization	Does not fit: needs 55.7 GB
52.8 GB	62.8 GB	—	54.4 GB	5-bit medium — 5-bit medium quantization — good quality/size tradeoff	Does not fit: needs 57.5 GB
61.0 GB	71 GB	—	64.3 GB	6-bit, very good — 6-bit quantization, very good quality	Does not fit: needs 65.7 GB
73.7 GB	83.8 GB	—	77.3 GB	8-bit, near-lossless — 8-bit quantization, near-lossless	Does not fit: needs 78.4 GB
146.4 GB	156.5 GB	—	~145.4 GB	16-bit · bfloat — Brain floating point 16 — preferred for training	Does not fit: needs 151.1 GB

est.calculated, no published file yet

Selected Q4_K_M

4-bit medium quantization

49.3GBat 16K context

Weights: 43.6 GB
KV cache: 5.4 GB
Overhead: 0.3 GB

Doesn't fit RTX 4090 (reference) · needs 25.3 GB more

≥10 tok/s needs ≥683 GB/s · M3 Ultra ~13, M2 Ultra ~13

Or .

Which Hardware Can Run Qwen2.5 72B Instruct?

Q4_K_M · 44.6 GB

Qwen2.5 72B Instruct (Q4_K_M) requires 44.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 58+ GB is recommended. Using the full 33K context window can add up to 10.1 GB, bringing total usage to 54.7 GB. No consumer GPU has enough memory. 52 devices with unified memory can run Qwen2.5 72B 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 25.7 GB8 GB tier →12 GB tier →16 GB tier →

24 GB · RTX 4090referenceDoesn't fit — smallest quant needs 25.7 GB24 GB tier →

Mac 32 GB · Mac Mini M4IQ2_S · 28.4 GB · ~4 tok/s · 16K28.4~416KMacs →

Mac 64 GB · Mac Studio M4 MaxQ4_K_M · 49.3 GB · ~9 tok/s · 32K49.3~932KMacs →

How good is Qwen2.5 72B Instruct?

The llmrun Score merges 8 public benchmark boards into a single figure, where 100 represents the average for open models.

Score vs VRAM in the ≤64 GB tier

Qwen2.5 72B Instruct
Other models
Best Score for its VRAM
x: VRAM (GB, log scale) · y: llmrun Score

#30 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 · Sep 2024Independent
49.1%
#56/100 open
Independent
LMArena Text
LMArena · Oct 2026Arena
1302.9
#107/188 open
Arena
AIME 2024/2025
Epoch AI · Sep 2024Independent
8.1%
#62/88 open
Independent
MMLU-Pro
TIGER-LabIndependent
71.6%
#35/146 open
Independent
MATH Level 5
Epoch AI · Sep 2024Independent
63.2%
#11/39 open
Independent
MMLU
Epoch AI · Sep 2024Lab-reported
85.3%
#3/85 open
Lab-reported

All 12 Qwen2.5 72B 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 Qwen2.5 72B Instruct

Qwen 2.5 family · 44 models →

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

Runner-up at this size
Llama 3.3 70B Instruct
Meta
- 2.2 lower Score
- 2 GB more VRAM
Score 80.0 · 51.3 GB
Compare with Qwen2.5 72B Instruct Open Llama 3.3 70B Instruct
Other vendor
Mistral Medium 3.5 128B
Mistral AI
- 19.9 higher Score
- 33.5 GB more VRAM
Score 102.1 · 82.8 GB
Compare with Qwen2.5 72B 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
Phi 4Microsoft	83.9	12.5 GB	~69	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
Gemma 3 27B ITGoogle	79.4	21.7 GB	~36	similar score

Fine-tunes & variants(3)community builds of Qwen2.5 72B Instruct

Specifications

Publisher: Alibaba
Family: Qwen 2.5
Parameters: 72.7B
Architecture: Qwen2ForCausalLM
Context Length: 32,768 tokens
Vocabulary Size: 152,064
Release Date: 2024-09-16
License: Other

Resources

Frequently Asked Questions

How much VRAM does Qwen2.5 72B Instruct need?

Qwen2.5 72B Instruct requires 44.6 GB of VRAM at Q4_K_M, or 146.4 GB at BF16. Full 33K context adds up to 10.1 GB (54.7 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 72.7B × 4.8 bits ÷ 8 = 43.6 GB

KV Cache + Overhead ≈ 1 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.

KV Cache + Overhead ≈ 11.1 GB (at full 33K context)

VRAM usage by quantization

Q4_K_M

44.6 GB

Q4_K_M + full context

54.7 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 4090 run Qwen2.5 72B Instruct?

Yes, at IQ2_S (23.7 GB) or lower. Higher quantizations like IQ2_M (25.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Qwen2.5 72B Instruct?

For Qwen2.5 72B Instruct, Q4_K_M (44.6 GB) offers the best balance of quality and VRAM usage. Q5_0 (46.4 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 21.0 GB.

VRAM requirement by quantization

IQ2_XXS

21.0 GB~53%

IQ3_XS

31.0 GB~73%

Q4_0

37.3 GB~85%

Q4_K_M ★

44.6 GB~89%

Q5_0

46.4 GB~90%

BF16

146.4 GB~100%

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Qwen2.5 72B Instruct on a Mac?

Yes — Mac Studio M1 Max (32 GB) and 84 other Macs can run Qwen2.5 72B Instruct, the smallest at IQ2_S. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 44.6 GB of usable unified memory (RAM minus macOS overhead), from Mac Pro M2 Ultra (64 GB).

Can I run Qwen2.5 72B Instruct locally?

Yes — Qwen2.5 72B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Qwen2.5 72B Instruct?

At Q4_K_M, Qwen2.5 72B Instruct can reach ~108 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 ÷ 44.6 × 0.65 = ~117 tok/s

Estimated speed at Q4_K_M (44.6 GB)

NVIDIA B200

~117 tok/s

NVIDIA B300

~117 tok/s

AMD Instinct MI350X

~108 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 Qwen2.5 72B Instruct?

At Q4_K_M, the download is about 43.62 GB. The full-precision BF16 version is 145.41 GB. The smallest option (IQ2_XXS) is 19.99 GB.

Which GPUs can run Qwen2.5 72B Instruct?

No single consumer GPU has enough VRAM to run Qwen2.5 72B Instruct at Q4_K_M (44.6 GB). Multi-GPU or professional hardware is required.

Which devices can run Qwen2.5 72B Instruct?

52 devices with unified memory can run Qwen2.5 72B Instruct at Q4_K_M (44.6 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.

Qwen2.5 72B Instruct — Hardware Requirements & GPU Compatibility

Doesn't fit a 24 GB GPU

llmrun Composite Score →

Run Qwen2.5 72B Instruct

Recommended

Ways to runOther ways

Run in cloud

How Much VRAM Does Qwen2.5 72B Instruct Need?

Selected Q4_K_M

Which Hardware Can Run Qwen2.5 72B Instruct?

How good is Qwen2.5 72B Instruct?

Score vs VRAM in the ≤64 GB tier

Benchmarks

In the ≤64 GB class

Alternatives to Qwen2.5 72B Instruct

Llama 3.3 70B Instruct

Mistral Medium 3.5 128B

Similar models

Specifications

Resources

Frequently Asked Questions