Shanghai AI Lab8B~6 GB VRAM at Q4_K_M

InternLM 3 8B Instruct — VRAM, speed & local setup

Written by Jakub Rusinowski · Last updated

Beats Llama 3.1 8B on Chinese and English benchmarks. Particularly strong on math (GSM8K) and reasoning. The best 8B bilingual (Chinese/English) model currently available.

InternLM 3 8B Instruct needs about 6 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Logic88
Creative78
Coding84

VRAM and speed by quantization

Quoted against NVIDIA RTX 4090 (24 GB). Includes the KV cache at 8K context, so it reads higher than the headline figure.

QuantVRAMSpeed (est.)Fit
Q2_K
2.63 bpw
4.1 GB~158 tok/sFits
Q3_K_M
3.41 bpw
5 GB~134 tok/sFits
Q4_K_M
4.83 bpw
6.5 GB~105 tok/sFits
Q5_K_M
5.67 bpw
7.4 GB~93 tok/sFits
Q6_K
6.56 bpw
8.4 GB~83 tok/sFits
Q8_0
8.50 bpw
10.6 GB~67 tok/sFits
F16
16.00 bpw
18.8 GB~39 tok/sFits

Black marker = usable memory on the NVIDIA RTX 4090 (24 GB). Estimates from the memory-bandwidth roofline on the methodology page. InternLM 3 8B Instruct VRAM calculator →

Get InternLM 3 8B Instruct running

The cheapest catalogued GPU that runs InternLM 3 8B Instruct is the Intel Arc B570 (10 GB).

Buy this hardware Intel Arc B570 10GB — 10 GB VRAM · 150 W board powerDeploy in the cloud now RTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Intel Arc B570 10GB
10 GB VRAM · 150 W board power
2026 prices are volatile — check the current listing.

How to run InternLM 3 8B Instruct

Install Ollama, then run:

ollama run internlm3:8b
Weights on Hugging Face: internlm/internlm3-8b-instruct ↗

Specifications

Curated — A hand-written entry from before this catalogue recorded its sources. The figures are long-standing but their provenance is not on file.

Parameters
8 Billion
Context window
32,768
Architecture
Dense
Provider
Shanghai AI Lab
Licence
Apache 2.0
Practical deployment
Laptop
Specified at
Q4_K_M
System RAM
16 GB
Record updated
2025-01-15
LicenceApache-2.0Commercial use permitted

Commercial use permitted. No usage restrictions beyond attribution.

Quality and use cases

Scores as published by the model’s authors or an independent evaluator — quality, not throughput, and not measured by us.

Best forbilingualmathchatreasoning

Can I run InternLM 3 8B Instruct on my GPU?

Other InternLM 3 sizes

InternLM 3 8B Instruct — frequently asked questions

How much VRAM does InternLM 3 8B Instruct need?

About 6 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.

Does InternLM 3 8B Instruct run on an RTX 4090 (24 GB)?

Yes. InternLM 3 8B Instruct needs about 6 GB at Q4_K_M, inside a 24 GB card, at an estimated 105 tokens/sec.

How do I run InternLM 3 8B Instruct locally?

Install Ollama and run `ollama run internlm3:8b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does InternLM 3 come in?

InternLM 3 8B Instruct (6 GB), InternLM 3 20B Instruct (13 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.