How much VRAM does InternLM2 7B Chat need?

About 5.5 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 9.3 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 InternLM2 7B Chat run on a 8GB GPU?

Yes — at Q4_K_M and 4K context it needs about 5.5 GB, which leaves 2.5 GB spare on a RTX 5060 Ti 8G. That is the smallest card in this index that clears it comfortably; 62 of 63 do.

Which quantization of InternLM2 7B Chat should I use?

Q4_K_M has the lowest published quality loss (3.1%), and Q4_K_M is the level most people run. All 2 levels in the index are Q4_K_M, AWQ INT4.

InternLM2 7B Chat

Shanghai AI Lab

⚠ Superseded · Prefer Qwen3 8B Instruct

This index would suggest Qwen3 8B Instruct instead today — longer context (32K → 40K tokens). The numbers below are still accurate; they are just for a model you probably should not start with. Compare the two →

Strong bilingual (EN/ZH) 7B from Shanghai AI Lab. Competitive with Qwen 7B.

⬇ 1.7K HF downloads♥ 0 likesbartowski/internlm2_5-7b-chat-GGUF· stats from 9/21/2026

Consumer GPUMac / Apple SiliconCPU / VPS

33K

Max Context

Quant Variants

GGUF Q4_K_M

Best Quality

96.9%

Accuracy Retained

Calculate VRAM Hugging Face Compare

Quantization Variants

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

Measured = site benchmarks · Estimated = formula · Community = public reports

Format	Level	BPW	VRAM	PPL Loss	Speed	Source	Actions
GGUF	Q4_K_M	4.85	5.5 GB	3.1%	148 tok/s	Estimated	Calc HF
AWQ	INT4	4	4.9 GB	4.3%	215 tok/s	Estimated	Calc HF

Similar models

Compare with InternLM2 20B

20B

InternLM2 20B Chat

Shanghai AI Lab

Consumer GPUPro GPU

13.8 GBmin VRAM98.6%accuracy

Mid-size InternLM2 with excellent Chinese comprehension. Fits 24GB at Q4.

Llama 3.1 8B Instruct

Meta Llama 3.1

Consumer GPUMac / Apple Silicon

3.2 GBmin VRAM99.9%accuracy

Meta's flagship 8B model with 128K context. Best-in-class for local deployment.

Qwen2.5 7B Instruct

Alibaba Qwen2.5

Consumer GPUMac / Apple Silicon

4.8 GBmin VRAM99.3%accuracy

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

3.8B

Phi-3.5 Mini Instruct

Microsoft Phi

Consumer GPUMac / Apple Silicon

2.5 GBmin VRAM99.8%accuracy

Microsoft's tiny powerhouse. Best 4B model for on-device deployment.

Running InternLM2 7B Chat locally

At Q4_K_M and 4K of context, InternLM2 7B Chat needs about 5.5 GB — 4.5 GB of weights, 0.50 GB of KV cache and a 0.5 GB activation buffer. The smallest card in this index that clears that comfortably is the RTX 5060 Ti 8G at 8 GB, and 62 of the 63 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 3.50 GB, taking the total to 9.3 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 32K; holding all of it at Q4_K_M would need about 9 GB.

Which build to download

This index tracks 2 formats for it — GGUF, AWQ — across 2 levels. Q4_K_M carries the lowest published perplexity loss at 3.1%. The fastest level measured here is AWQ INT4 at 215 tok/s on an RTX 4090, batch 1. 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 and CUDA only.

Common questions

How much VRAM does InternLM2 7B Chat need?: About 5.5 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 9.3 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 InternLM2 7B Chat run on a 8GB GPU?: Yes — at Q4_K_M and 4K context it needs about 5.5 GB, which leaves 2.5 GB spare on a RTX 5060 Ti 8G. That is the smallest card in this index that clears it comfortably; 62 of 63 do.
Which quantization of InternLM2 7B Chat should I use?: Q4_K_M has the lowest published quality loss (3.1%), and Q4_K_M is the level most people run. All 2 levels in the index are Q4_K_M, AWQ INT4.

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: RTX 5060 Ti 8G8GB · +2.6 RTX 50608GB · +2.6 RTX 4060 Ti 8G8GB · +2.6 RTX 40608GB · +2.6 RTX 3070 Ti8GB · +2.6
Tight but possible: Mac M3 8G8GB
Ships in: GGUF AWQ
Compared here: GGUF vs AWQ
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 2026-09-21 RSS → /feed.xml