Qwen2.5 Coder 3B — Hardware Requirements & GPU Compatibility
ChatCodeFunctions
Qwen2.5-Coder-3B is Alibaba's 3.1-billion-parameter base (pretrained, not instruction-tuned) code language model, one of six sizes in the Qwen2.5-Coder family built on Qwen2.5-3B. It was pretrained on 5.5 trillion tokens of source code, text-code grounding data, and synthetic data, and is intended as a foundation for further fine-tuning, reinforcement learning, or fill-in-the-middle code-completion tasks rather than direct chat use; Alibaba explicitly advises against using it for conversations as-is. The family's larger 32B model is described as matching GPT-4o-level coding ability, though this 3B checkpoint targets lightweight, resource-constrained deployment. At just over 3 billion parameters, it runs easily on a single modest consumer GPU or even a CPU. Context length is 32,768 tokens. It is released under the Qwen Research License, a non-commercial license restricted to research and evaluation use; commercial use requires a separate license from Alibaba Cloud. It was published in November 2024.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~294 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~32 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen2.5 Coder 3B
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 run hf.co/bartowski/Qwen2.5-Coder-3B-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 21.2 GB free · 16K context
Download: Qwen2.5-Coder-3B-Q4_K_M.gguf1.93 GBbartowski
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 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
Runs on RTX 3080 Ti (12 GB) (9 GB headroom) · Q4_K_M
- Generation speed
- ~266 tok/s
- generation speed
- Cost per 1M output tokens
- $0.13
- per 1M output tokens
How Much VRAM Does Qwen2.5 Coder 3B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.7 GB | 2.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~388 tok/s | |
| 1.7 GB | 2.9 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~379 tok/s | |
| 1.9 GB | 3 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~349 tok/s | |
| 1.9 GB | 3.1 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~341 tok/s | |
| 2.2 GB | 3.4 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~294 tok/s | |
| 2.6 GB | 3.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~255 tok/s | |
| 2.9 GB | 4.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~224 tok/s | |
| 3.5 GB | 4.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~189 tok/s |
Selected Q4_K_M
4-bit medium quantization2.8GBat 16K context
- Weights
- 1.9 GB
- KV cache
- 0.6 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 21.2 GB free · ~294 tok/s
≥50 tok/s needs ≥171 GB/s · RTX 5090 ~522, RTX 3090 Ti ~294
Which Hardware Can Run Qwen2.5 Coder 3B?
Q4_K_M · 2.2 GBQwen2.5 Coder 3B (Q4_K_M) requires 2.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 33K context window can add up to 1.1 GB, bringing total usage to 3.4 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Qwen2.5 Coder 3B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 5.2 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~222Simulate output on RTX 3080RTX 2080 Ti~180Simulate output on RTX 2080 TiRTX 3070 Ti~177Simulate output on RTX 3070 TiM3*8 GB~32Simulate output on iMac 24" M3 (8 GB)Orin Nano~30Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~21Simulate output on iPhone 1720 GB
fits · 17.2 GB free · up to 32K ctx
1 GPU
RX 7900 XT~215Simulate output on RX 7900 XT32–40 GB
fits · 29.2 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~522Simulate output on RTX 5090M5 Max36 GB~144Simulate output on Mac Studio M5 Max (36 GB)M532 GB~48Simulate output on MacBook Air 13" M5 (32 GB)Orin~60Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~39Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Qwen2.5 Coder 3B?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
Here are all 4 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- GSM8K
Epoch AI · Sep 2024Lab-reported
Lab-reported75.7%
#16/62 open
- HellaSwag
Epoch AI · Sep 2024Lab-reported
Lab-reported70.9%
#31/47 open
- ARC Challenge
Epoch AI · Sep 2024Lab-reported
Lab-reported52.9%
#30/56 open
- WinoGrande
Epoch AI · Sep 2024Lab-reported
Lab-reported67.4%
#28/51 open
Alternatives to Qwen2.5 Coder 3B
Qwen 2.5 family · 44 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| D1 3BLiquid AI | — | 2.6 GB | ~285 | same size |
| Dots.mocrdots-studio | — | 2.6 GB | ~301 | same size |
| Dots.ocrdots-studio | — | 2.6 GB | ~301 | same size |
| SmolLM3 3BHugging Face | — | 3.4 GB | ~285 | same size |
| Llama 3.2 3B InstructMeta | — | 4.1 GB | ~309 | same size |
| Starcoder2 3BBigCode | — | 2.6 GB | ~301 | same size |
Fine-tunes & variants(2)
Specifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 3.1B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2024-11-08
- License
- Other
Frequently Asked Questions
How much VRAM does Qwen2.5 Coder 3B need?
Qwen2.5 Coder 3B requires 2.2 GB of VRAM at Q4_K_M, or 6.5 GB at BF16. Full 33K context adds up to 1.1 GB (3.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.1B × 4.8 bits ÷ 8 = 1.9 GB
KV Cache + Overhead ≈ 0.3 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.
KV Cache + Overhead ≈ 1.5 GB (at full 33K context)
VRAM usage by quantization
What's the best quantization for Qwen2.5 Coder 3B?
For Qwen2.5 Coder 3B, Q4_K_M (2.2 GB) offers the best balance of quality and VRAM usage. Q4_K_L (2.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.2 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen2.5 Coder 3B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Qwen2.5 Coder 3B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.2 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen2.5 Coder 3B locally?
Yes — Qwen2.5 Coder 3B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen2.5 Coder 3B?
At Q4_K_M, Qwen2.5 Coder 3B can reach ~2153 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~294 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 ÷ 2.23 × 0.65 = ~2332 tok/s
Estimated speed at Q4_K_M (2.2 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen2.5 Coder 3B?
At Q4_K_M, the download is about 1.85 GB. The full-precision BF16 version is 6.17 GB. The smallest option (IQ2_XXS) is 0.85 GB.
Which GPUs can run Qwen2.5 Coder 3B?
52 consumer GPUs can run Qwen2.5 Coder 3B 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 Qwen2.5 Coder 3B?
163 devices with unified memory can run Qwen2.5 Coder 3B at Q4_K_M (2.2 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.