How much VRAM does Qwen2.5-Coder 7B Instruct need?

About 5.1 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 6.8 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.

Will Qwen2.5-Coder 7B Instruct run on a 8GB GPU?

Yes — at Q4_K_M and 4K context it needs about 5.1 GB, which leaves 0.9 GB spare on a Mac M1 8G, or on any of the other 8 GB cards here (11 in all). That is the smallest size in this index that clears it comfortably; 78 of 78 do.

Which quantization of Qwen2.5-Coder 7B Instruct should I use?

EXL2 4.65bpw has the lowest published quality loss (2.0%), and Q4_K_M is the level most people run. All 3 levels in the index are Q4_K_M, AWQ INT4, EXL2 4.65bpw.

Qwen2.5-Coder 7B Instruct

Alibaba Qwen2.5

Best 7B coding model. Ideal for local dev assistants on 8–16GB VRAM.

⬇ 348.6K HF downloads♥ 633 likesQwen/Qwen2.5-Coder-7B-Instruct-GGUF· stats from 11/7/2026

Consumer GPUMac / Apple SiliconCPU / VPS

131K

Max Context

Quant Variants

EXL2 4.65bpw

Best Quality

+2.0%

Perplexity rise (best build)

Calculate VRAM Hugging Face Compare

Quantization Variants

Per-quant VRAM, quality loss, and inference speed on RTX 4090

Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports

Format	Level	BPW	VRAM	PPL Loss	Speed	Source	Actions
GGUF	Q4_K_M	4.85	5.1 GB	2.8%	158 tok/s	Estimated	Calc HF
AWQ	INT4	4	4.2 GB	4.0%	225 tok/s	Estimated	Calc HF
EXL2	4.65bpw	4.65	4.9 GB	2.0%	248 tok/s	Estimated	Calc HF

Which file each size is for

A file size is not a memory figure: the VRAM column is the calculator's total at 4K context — weights plus KV cache plus a 10% activation buffer. Units differ on purpose: file sizes are decimal GB, as Hugging Face lists them; the VRAM column is GiB (1024³ bytes) with 2% added to the weights, so 7.12 GB of file counts as about 6.8 GiB there. A vision projector (mmproj), where the repo ships one, is a separate download and is in neither number.

GGUF Q4_K_MSized at the calculator's generic rate for this level; no file was checked.
AWQ INT4Sized at the calculator's generic rate for this level; no file was checked.
EXL2 4.65bpwSized at the calculator's generic rate for this level; no file was checked.

Similar models

Compare with Qwen2.5 7B

Qwen2.5 7B Instruct

Alibaba Qwen2.5

Consumer GPUMac / Apple Silicon

Q4_K_M5.1 GB+3.0%perplexity rise

Alibaba's highly optimized 7B. Punches well above its weight, especially in coding.

14B

Qwen2.5 14B Instruct

Alibaba Qwen2.5

Consumer GPUMac / Apple Silicon

Q4_K_M10.1 GB+2.9%perplexity rise

The sweet spot between performance and resource usage. 16GB VRAM with Q4.

32B

Qwen2.5 32B Instruct

Alibaba Qwen2.5

Consumer GPUPro GPU

Q4_K_M21.7 GB+2.7%perplexity rise

Near-GPT-4 reasoning on a 24GB VRAM card (Q4_K_S). Groundbreaking value.

32B

Qwen2.5-Coder 32B Instruct

Alibaba Qwen2.5

Consumer GPUPro GPU

Q4_K_M21.7 GB+2.5%perplexity rise

Top-tier open coding model. HumanEval competitive with GPT-4o on 32B scale.

Running Qwen2.5-Coder 7B Instruct locally

At Q4_K_M and 4K of context, Qwen2.5-Coder 7B Instruct needs about 5.1 GB — 4.4 GB of weights, 0.22 GB of KV cache and a 0.5 GB activation buffer. The smallest cards in this index that clear that comfortably are 8 GB ones — 11 of them, from the Mac M1 8G up — and 78 of the 78 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.

What longer context costs

Going from 4K to 32K adds about 1.53 GB, taking the total to 6.8 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 128K; holding all of it at Q4_K_M would need about 13 GB.

Which build to download

This index tracks 3 formats for it — GGUF, AWQ, EXL2 — across 3 levels. EXL2 4.65bpw carries the lowest published perplexity loss at 2.0%. The fastest level listed is EXL2 4.65bpw at about 248 tok/s on an RTX 4090, batch 1 — an estimate, not a run on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.

Common questions

How much VRAM does Qwen2.5-Coder 7B Instruct need?: About 5.1 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 6.8 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
Will Qwen2.5-Coder 7B Instruct run on a 8GB GPU?: Yes — at Q4_K_M and 4K context it needs about 5.1 GB, which leaves 0.9 GB spare on a Mac M1 8G, or on any of the other 8 GB cards here (11 in all). That is the smallest size in this index that clears it comfortably; 78 of 78 do.
Which quantization of Qwen2.5-Coder 7B Instruct should I use?: EXL2 4.65bpw has the lowest published quality loss (2.0%), and Q4_K_M is the level most people run. All 3 levels in the index are Q4_K_M, AWQ INT4, EXL2 4.65bpw.

Where this model fits

Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.

Runs comfortably on: Mac M1 8G8GB · +0.9 Mac M3 8G8GB · +0.9 RTX 40608GB · +2.9 RTX 4060 Ti 8G8GB · +2.9 Radeon RX 9060 XT 8G8GB · +2.9
Ships in: GGUF AWQ EXL2
Compared here: GGUF vs AWQ GGUF vs EXL2 AWQ vs EXL2
Try it yourself: Size it in the calculator Generate the run command

How to actually run this

Deployment guides for this model and this class of hardware.

This page's figures change when the model or the runtime does.Last updated 2026-11-07 RSS → /feed.xml