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How much VRAM does Qwen 2.5 32B need at Q4_K_M? Single 24GB GPU sweet spot

Qwen 2.5 32B Q4_K_M at reduced context is a sweet spot for 24 GB cards, but at its full native 128K context, the KV cache grows to 34.4 GB, pushing the total to 57.5 GB and requiring pooled cards or unified workstations.

Qwen 2.5 32B at Q4_K_M with native 128K context needs about 57.5 GB of VRAM, making it a fit for pooled cards or unified memory workstations.

By TechCompare · Updated

Total VRAM required
57.5 GB
Qwen 2.5 32B at Q4_K_M
Weights
17.9 GB
32B params
KV cache
34.4 GB
128K tokens, FP16 KV

Calculator

Estimated VRAM required

57.5 GB

32B params at Q4_K_M, 131,072 token context, batch 1, inference.

Weights
17.9 GB
KV cache
34.4 GB
Overhead
5.2 GB

Estimate accuracy: Weights within ~2%. KV cache within ~5% for standard GQA models, ~10% for MLA or hybrid state estimates. Real VRAM may vary with framework (vLLM vs llama.cpp vs Transformers), Flash Attention, and driver overhead.

KV cache exceeds model weights: Consider lowering the context length to save on VRAM. Contexts between 8K and 64K are generally more typical for local setups.

Hardware that fits

Apple entries show installed unified memory separately from a model budget based on an 8 GB reserve. That budget is a planning heuristic, not a fixed hardware limit.

A100 80GB
Datacenter
80 GB
72% used
H100 80GB
Datacenter
80 GB
72% used
Apple M3 Ultra 96GB
Unified
~88 GB
65% of estimate

Just barely too small

Apple M3 Max 64GB
Unified
~56 GB
short by 1.5 GB
Same 32B @ Q4_K_M, 131,072 context scenario, pre-loaded. Change the quant, shrink the context, or try a different preset.
Edit in the full tool

How this is calculated

32B at Q4_K_M is roughly 17.9 GB of weights, 34.4 GB of KV cache, and 5.2 GB of activation overhead, totaling 57.5 GB.

Verdict

The math is 17.9 GB of Q4 weights, 34.4 GB of FP16 KV cache at the full 128K context (8 KV heads at head_dim 128 across 64 layers), and 5.2 GB of overhead totaling 57.5 GB. The cache exceeds the weights at native context, which is the constraint. At 32K context, the FP16 cache is about 8.6 GB and modeled overhead is 2.7 GB, bringing the total to about 29.2 GB, above a 24 GB card's capacity. At 8K, the cache falls to 2.1 GB and overhead to 2.0 GB, for about 22.1 GB total. That smaller context is the modeled fit for a 24 GB card, with limited headroom and runtime-dependent memory use. That's why Qwen 32B is the popular single-24-GB-card pick: load it at Q4_K_M with a sane context cap and you get strong reasoning and code quality that genuinely clears what 8B-class models can deliver.

More Qwen scenarios

MiMo-V2.6-Pro at Q4_K_M
Budget a 1.02T expert pool, hybrid attention, and a 1M text context.
View details ➜
MiMo-V2.6-Flash at Q4_K_M
309B total weights and separate global and sliding cache widths.
View details ➜
GLM-5.3-Flash at Q4_K_M
Model the 34 KDA layers, 11 sparse layers, and indexer storage.
View details ➜

Related guides

BPE tokenization explained
How tokenizers chunk text into the units VRAM serves.
View guide ➜

Frequently asked questions

Can I run Qwen 2.5 32B on an RTX 3090?
Yes, at Q4_K_M and reduced context lengths (like 8K context, which uses ~22 GB total). For native 128K context, it will exceed 24 GB and require multiple cards or unified systems.
Is Qwen 2.5 32B better than Llama 3.1 8B?
Substantially, especially for code, math, and multi-step reasoning. The 4x parameter count translates to a meaningful capability jump that matches the memory cost.
Is Qwen 2.5 32B better than Llama 3.1 8B at the same memory?
Yes across most benchmarks. Qwen 2.5 32B at Q4 fits in roughly 22.1 GB of VRAM resident at 8K context, larger than Llama 3.1 8B's 6 GB at the same window. With 32B parameters and modern training, Qwen matches or beats Llama 8B on reasoning, code, and multilingual tasks. Pick Qwen 32B if you can afford the extra memory, and pick Llama 8B if you need to run on a 6 GB card.
Is it worth moving from an 8B model to a 32B model?
If your GPU can hold it, generally yes. The 4x parameter jump buys noticeably better reasoning, instruction following, and multilingual ability on hard tasks, which shows up less on casual chat and more on edge cases like tricky code refactors or long-format reasoning. The honest trade is memory and speed: a 32B at Q4 with 8K context and FP16 KV cache needs roughly 22.1 GB resident and generates slower, so a 6-12 GB card is better served staying on an 8B quant.

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