llmrun
AlibabaQwen 2.5Qwen2ForCausalLM

Qwen2.5 Coder 32B Instruct — Hardware Requirements & GPU Compatibility

ChatCodeFunctions

Qwen2.5 Coder 32B Instruct is a 32.8-billion parameter code-specialized model from Alibaba Cloud, instruction-tuned for programming assistance and code generation. It is trained on a large corpus of source code alongside natural language data, making it highly capable for tasks such as code completion, debugging, code explanation, and software engineering dialogue. The model supports a 128K token context window and delivers code generation quality competitive with the best open-weight coding models at any scale. It requires a GPU with at least 24GB of VRAM for quantized inference. Released under the Apache 2.0 license.

32.8B params · dense33K contextApache 2.0546.0K downloads2.2K likes1.2M quant downloads
Based on Qwen2.5 Coder 32B
A

Runs well on a 24 GB GPU

Runs from16 GB IQ2_M · 16KBest on24 GB Q4_K_M · ~32 tok/s · 8KMacs from18 GB IQ2_XS · ~10 tok/s

Score 81.9#86 of 106 open#24 of 36 that fit 24 GBApache 2.0

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

llmrun Composite Score →

81.9

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

Run Qwen2.5 Coder 32B 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 qwen2.5-coder:32b

Q4_K_M · needs ~24.3 GB · 16K context

Download: qwen2.5-coder-32b-instruct-q4_k_m.gguf19.85 GBQwen

install, OS, other quants

Ways to runOther ways

each opens step-by-step

Run in cloud

Your own private copy · Q4_K_M · 32 GB GPU

  • The easiest way to try this model
  • Running in a few minutes, easy setup
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How Much VRAM Does Qwen2.5 Coder 32B Instruct Need?

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

QuantizationVRAM+32KOn RTX 4090 (reference)
14.8 GB22.8 GB2-bit · K-quant — 2-bit quantization with K-quant improvementsFits: 32K context · ~44 tok/s
15.2 GB23.2 GB3-bit small — 3-bit small quantizationFits: 32K context · ~43 tok/s
16.8 GB24.9 GB3-bit medium — 3-bit medium quantizationFits: 16K context · ~39 tok/s
17.2 GB25.3 GB4-bit · legacy — 4-bit legacy quantizationFits: 16K context · ~38 tok/s
20.5 GB28.6 GB4-bit medium — 4-bit medium quantization — most popular sweet spotFits: 8K context · ~32 tok/s
24.2 GB32.2 GB5-bit medium — 5-bit medium quantization — good quality/size tradeoffDoes not fit: needs 27.9 GB
27.9 GB35.9 GB6-bit, very good — 6-bit quantization, very good qualityDoes not fit: needs 31.6 GB
33.6 GB41.7 GB8-bit, near-lossless — 8-bit quantization, near-losslessDoes not fit: needs 37.4 GB
est.calculated, no published file yet

Selected Q4_K_M

4-bit medium quantization

24.3GBat 16K context

Weights
19.7 GB
KV cache
4.3 GB
Overhead
0.3 GB

Fits RTX 4090 (reference) · fits up to 8K context · ~32 tok/s

≥50 tok/s needs ≥1.57 TB/s · RTX 5090 ~57

Which Hardware Can Run Qwen2.5 Coder 32B Instruct?

Q4_K_M · 20.5 GB

Qwen2.5 Coder 32B Instruct (Q4_K_M) requires 20.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 27+ GB is recommended. Using the full 33K context window can add up to 8.1 GB, bringing total usage to 28.6 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run Qwen2.5 Coder 32B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M1 Max (32 GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M
8–12 GB · RTX 4060, RTX 3060 12GBDoesn't fit — smallest quant needs 13.6 GB8 GB tier →12 GB tier →
16 GB · RTX 5060 Ti 16GBIQ2_M · 15.7 GB · ~25 tok/s · 16K16 GB tier →
24 GB · RTX 4090referenceQ4_K_M · 24.3 GB · ~32 tok/s · 8Ktight24 GB tier →
Mac 32 GB · Mac Mini M4Q4_K_M · 24.3 GB · ~4 tok/s · 16KMacs →
Mac 64 GB · Mac Studio M4 MaxQ4_K_M · 24.3 GB · ~19 tok/s · 32KMacs →

How good is Qwen2.5 Coder 32B Instruct?

The llmrun Score merges 3 public benchmark boards into a single figure, where 100 represents the average for open models.

Score vs VRAM in the 24 GB tier

  • Qwen2.5 Coder 32B Instruct
  • Other models
  • Best Score for its VRAM

#24 of 36 that fit 24 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

Here are all 5 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.

  • SWE-bench Verified

    SWE-bench · Jun 2025Independent

    47.0%

    #16/20 open

    Independent
  • LMArena Text

    LMArena · Oct 2026Arena

    1270.6

    #123/188 open

    Arena
  • SWE-bench Bash Only

    SWE-bench · Aug 2025Independent

    9.0%

    #9/9 open

    Independent
  • Aider Polyglot

    Epoch AI · Nov 2024Independent

    16.4%

    #14/18 open

    Independent
  • GSM8K

    Epoch AI · Nov 2024Lab-reported

    93.0%

    #3/62 open

    Lab-reported
All 5 Qwen2.5 Coder 32B 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 Qwen2.5 Coder 32B 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.

Similar models

10
ModelScoreVRAMtok/sWhy it is similar
Qwen3.6 27BAlibaba111.217.9 GB~38similar score
Gemma 4 26B A4B ITGoogle105.816.5 GB~171similar score
Qwen3.5 35B A3BAlibaba105.822 GB~151similar score
Qwen3.5 27BAlibaba103.817.9 GB~38similar score
Seed OSS 36B InstructByteDance-Seed102.522.8 GB~31similar score
Qwen3 30B A3B Thinking 2507Alibaba100.719.4 GB~173similar 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
Fine-tunes & variants(4)

Specifications

Publisher
Alibaba
Family
Qwen 2.5
Parameters
32.8B
Architecture
Qwen2ForCausalLM
Context Length
32,768 tokens
Vocabulary Size
152,064
Release Date
2024-11-06
License
Apache 2.0

Resources

Frequently Asked Questions

How much VRAM does Qwen2.5 Coder 32B Instruct need?

Qwen2.5 Coder 32B Instruct requires 20.5 GB of VRAM at Q4_K_M, or 66.4 GB at BF16. Full 33K context adds up to 8.1 GB (28.6 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 32.8B × 4.8 bits ÷ 8 = 19.7 GB

KV Cache + Overhead ≈ 0.8 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 ≈ 8.9 GB (at full 33K context)

VRAM usage by quantization

20.5 GB
28.6 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 4090 run Qwen2.5 Coder 32B Instruct?

Yes, at Q5_K_S (23.4 GB) or lower. Higher quantizations like Q5_K_M (24.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Qwen2.5 Coder 32B Instruct?

For Qwen2.5 Coder 32B Instruct, Q4_K_M (20.5 GB) offers the best balance of quality and VRAM usage. Q4_K_L (20.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 9.8 GB.

VRAM requirement by quantization

IQ2_XXS
9.8 GB
IQ3_S
14.8 GB
Q3_K_L
17.6 GB
Q4_K_M ★
20.5 GB
Q5_0
21.3 GB
BF16
66.4 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Qwen2.5 Coder 32B Instruct on a Mac?

Yes — MacBook Pro 14" M3 Pro (18 GB) and 108 other Macs can run Qwen2.5 Coder 32B 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 20.5 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M5 Pro (24 GB).

Can I run Qwen2.5 Coder 32B Instruct locally?

Yes — Qwen2.5 Coder 32B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Qwen2.5 Coder 32B Instruct?

At Q4_K_M, Qwen2.5 Coder 32B Instruct can reach ~234 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~32 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 ÷ 20.5 × 0.65 = ~254 tok/s

Estimated speed at Q4_K_M (20.5 GB)

~254 tok/s
~32 tok/s
~254 tok/s
~234 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 Coder 32B Instruct?

At Q4_K_M, the download is about 19.66 GB. The full-precision BF16 version is 65.53 GB. The smallest option (IQ2_XXS) is 9.01 GB.

Which GPUs can run Qwen2.5 Coder 32B Instruct?

7 consumer GPUs can run Qwen2.5 Coder 32B Instruct at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU has plenty of headroom for comfortable inference.

Which devices can run Qwen2.5 Coder 32B Instruct?

122 devices with unified memory can run Qwen2.5 Coder 32B Instruct at Q4_K_M (20.5 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.