What can you run on an 8GB graphics card?

With 8GB VRAM you can run 7 of the 30 open-source models we track — up to about 10B parameters at Q4 quantization. The most popular that fits is Qwen3.5-9B, needing 6.8 GB and generating around ~31 tok/s on typical hardware of this size.

Typical machines: RTX 4060 · RTX 3070 · RTX 3060 Ti · RX 7600. After the operating system takes its share, about 7.7 GB is free for a model.

Having the memory is not the same as having the speed

8GB VRAM decides whether a model loads. How fast it answers is decided by memory bandwidth — how quickly your machine can read the model, which it must do once for every word it writes. Machines with 8GB VRAM span 272–448 GB/s, so the fastest is about 1.6× quicker than the slowest with identical capacity. The table quotes a RTX 4060; pick yours below to see the difference. Why bandwidth and not the processor →

272 GB/sGeForce RTX 4060 (8GB)· NVIDIA published specification: 272 GB/s GDDR6, 128-bit
288 GB/sRadeon RX 7600 (8GB)· AMD published specification: 288 GB/s GDDR6, 128-bit
448 GB/sGeForce RTX 3070 or 3060 Ti (8GB)· NVIDIA published specification: 448 GB/s GDDR6, 256-bit

Models that run on 8GB VRAM in 2026

ModelSizeMemoryEst. speedFeels like
Qwen3.5-9BAlibaba / Qwen9.7B6.8 GBTight~31 tok/sFast
Qwen3.5-4BAlibaba / Qwen4.7B3.8 GBRuns well~65 tok/sFast
Qwen3.5-0.8BAlibaba / Qwen873M1.3 GBRuns well~332 tok/sFast
Qwen3.5-2BAlibaba / Qwen2.3B2.0 GBRuns well~138 tok/sFast
Olmo-3-7B-InstructAi27.3B7.2 GBTight~40 tok/sFast
GLM-4.6V-FlashZhipu AI10B7.0 GBTight~29 tok/sFast
ERNIE-4.5-0.3B-PTBaidu AI361M853 MBRuns well~735 tok/sFast

All figures at Q4_K_M quantization, 4K context. “Memory” includes the model, its context cache and runtime overhead. Speed is calculated, not benchmarked: 272 GB/s × 0.65 ÷ bytes read per token, assuming a RTX 4060. Scale it by your own machine's bandwidth from the list above. Mixture-of-experts models read only their active parameters per token, which is why some large ones outrun smaller dense models.

Image generators that also fit

These make pictures rather than text. Their memory use scales with the resolution you generate at, not conversation length, so treat these figures as a floor rather than a ceiling.

What you'd need more memory for

The next models up, and what they ask for.

gpt-oss-20bneeds 13 GB
Qwen3.5-27Bneeds 19 GB
GLM-4.7-Flashneeds 22 GB

See what 12GB gets you →

Other hardware

Verified against HuggingFace on 2026-08-01New to this? Start here →Runs on a laptop and up · last 12 months only