standardApache 2.032B paramsOpen weights

Qwen2.5 32B

32B parameter open-weight model. 32B with 128K context. Q4 fits in 24 GB VRAM. Near-frontier reasoning and coding performance at consumer GPU scale. Compare with Qwen3 32B — Qwen3 often wins on reasoning benchmarks; Qwen2.5 32B has strong broad task coverage.

Alibaba Qwen · Qwen 2.5

Model overview

Qwen2.5 32B sits in the sweet spot between consumer-friendly VRAM requirements and near-frontier reasoning and coding performance, fitting Q4 quantization in a single 24 GB GPU like an RTX 4090 or 3090. It's a well-rounded pick across chat, coding, reasoning, multilingual, and agentic tasks, with the Apache 2.0 license making it easy to evaluate for commercial use. Compared to the newer Qwen3 32B, Qwen2.5 32B tends to trail on dedicated reasoning benchmarks but holds its own with strong general task coverage — worth keeping both in mind if you're choosing between the two generations for a 24 GB card.

Check your GPU fitWhat is VRAM?What is quantization?Local LLM quickstart

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedJune 2026SourcesHuggingFace model card (Qwen/Qwen2.5-32B-Instruct), official docs, OpenSourcesAI editorial review.

VRAM figures are empirical estimates. Actual usage varies by runtime, context length, and system configuration. Verify on your specific hardware before production use.

Will Qwen2.5 32B run on your machine?

Qwen2.5 32B is 32B parameters and needs 21.5 GB of VRAM at Q4_K_M — 20 GB of weights plus 1.5 GB of runtime overhead for the inference server itself.

VRAM by quantization

QuantizationWeightsNeeds (with overhead)Quality
Q4_K_M20 GB21.5 GBgood
Q8_034 GB35.5 GBhigh
FP1666 GB67.5 GBreference

Fit on common hardware at Q4_K_M

HardwareMemory the model can useSystem RAMVerdict
CPU OnlyNone (CPU only)16 GBToo large
RTX 4060 Laptop8 GB16 GBToo large
RTX 3060 (12GB)12 GB32 GBCPU offload
RTX 4060 Ti (16GB)16 GB32 GBCPU offload
RTX 309024 GB64 GBComfortable
Apple Silicon (Unified Memory) 36 GB27 GB of 36 GB36 GBComfortable
RTX 509032 GB64 GBComfortable

Comfortable means VRAM clears the requirement by 2 GB or more. Tight means it covers the requirement with no margin. CPU offload means the model does not fit in VRAM but system RAM is at least 1.6× the weights, so it will run at reduced speed — expect roughly 1–5 tokens per second. Figures are weights plus a fixed runtime overhead and exclude KV-cache growth, which scales with context length.

Apple Silicon shares one pool of memory between the system and the GPU, so a model cannot use all of it. These rows apply the same 75% usable fraction the Compatibility Checker uses, which is why a 36 GB Mac is graded on less than 36 GB.

Check Qwen2.5 32B against your exact hardwareHow these verdicts are calculated

Need more hardware for Qwen2.5 32B? Open the PC Builder for the 30B / 32B tier →

Related models

Laguna XS 2.133.4B parameter model — shared focus: coding, agents, tool-use.North Mini Code 1.030.5B parameter model — shared focus: coding, agents, tool-use.Qwen3-Coder 30B (A3B)30.5B parameter model — shared focus: coding, agents, tool-use.Qwen2.5 14BBuilders hosting local AI on a single RTX 4080 (16 GB) or RTX 4090 (24 GB) who want near-frontier multilingual reasoning, mathematical accuracy, and agentic coding performance without multi-GPU infrastructure. The sweet spot between consumer accessibility and frontier-class open-weight capability for 2026 local deployment.

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