Best LLMs for 8 GB VRAM
Entry-level for LLMs (RTX 4060, RX 7600, Apple M-series base) — 7B models at Q4, small models at Q8
8 GB is an entry-level tier for local AI. You can run small 7B models at lower quantization levels, which is great for experimenting but comes with quality and speed trade-offs.
With 8 GB, you're limited to smaller models and lower quantization levels, but it's still enough for a meaningful local AI experience. Phi 3 Mini (3.8B) and similar compact models run well at Q4_K_M. For 7B models like Mistral 7B and Llama 3 8B, you'll need Q2_K or Q3_K_M quantization, which reduces output quality. Think of this tier as ideal for learning and experimentation rather than production workloads.
Runs Well
- 3B–4B models at Q4–Q5 quality
- 7B models at Q2–Q3 (usable but reduced quality)
- Quick experiments and learning
Challenging
- 7B models at Q4+ (VRAM too tight)
- Any model above 7B parameters
- Long context windows even with small models
GPUs with ~8.0 GB VRAM
All 8 GPUsNVIDIA GeForce RTX 3070 Ti
NVIDIA · Ampere
NVIDIA GeForce RTX 3070
NVIDIA · Ampere
NVIDIA GeForce RTX 3060 Ti
NVIDIA · Ampere
AMD Radeon RX 7600
AMD · RDNA 3
Intel Arc A750
Intel · Alchemist
NVIDIA GeForce RTX 4060 Ti 8GB
NVIDIA · Ada Lovelace
Models That Fit in 8 GB VRAM
Speed estimated for NVIDIA GeForce RTX 3080
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
| Q4_K_M | 0.7 GB8% | 748.8 t/s | 33K | EASY RUN | D29 | |
| Q4_K_M | 7.9 GB99% | 62.4 t/s | 33K | TOO HEAVY | D15 |
Frequently Asked Questions
- What models can I run with 8.0 GB VRAM?
With 8.0 GB VRAM, you can run small 7B models at Q2-Q3 quality, and compact 3B-4B models at Q4-Q5.
- Is 8.0 GB enough for local AI?
8.0 GB is a basic tier for local AI. While limited, you can still run small models and experiment with quantized 7B models for learning and basic chat tasks.
- What GPU should I get for 8.0 GB VRAM?
There are several GPUs with approximately 8.0 GB VRAM at different price points. Popular choices include NVIDIA GeForce RTX 3070 Ti, NVIDIA GeForce RTX 3070, NVIDIA GeForce RTX 3060 Ti. Memory bandwidth also matters — higher bandwidth means faster token generation. Check the GPU cards above for specific specs and pricing.
- What quantization works best with 8.0 GB?
For 8.0 GB, Q4_K_M is typically the best starting quantization — it offers a good balance of model quality and VRAM usage. For the smallest models, Q5_K_M provides a noticeable quality improvement. Use Q2_K or Q3_K_M only when you need to squeeze in a model that's otherwise too large.