How much VRAM does SmolVLM 500M Instruct need?

SmolVLM 500M Instruct (0.5B parameters) requires approximately 4.8 GB of memory with Q6_K quantization.

What is the best quantization for SmolVLM 500M Instruct?

The recommended quantization for SmolVLM 500M Instruct is Q6_K, which balances quality and memory efficiency.

Can it run?

Yes, RTX 3090 24GB can run SmolVLM 500M Instruct with a C grade (Runs well). Expected decode speed: 707.6 tok/s.

CUsable

Runs well

Using Q6_K in Ollama

Capabilities:

Fit status

Runs well

Decode

707.6 tok/s

TTFT

350 ms

Safe context

80K

Memory

4.8 GB / 24.0 GB

Weights0.4 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom2.4 GB

Workload	Grade	Fit	Decode	TTFT	Context
Agentic Coding	C	Runs well	688.0 tok/s	409 ms	160K
Chat	C	Runs well	688.0 tok/s	350 ms	40K
Coding	C	Runs well	707.6 tok/s	350 ms	80K
RAG	C	Runs well	688.0 tok/s	512 ms	160K
Reasoning	C	Runs well	688.0 tok/s	350 ms	80K

How SmolVLM 500M Instruct (0.5B params) fits at each quantization level on RTX 3090 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.2 GB	Low	D30
Q3_K_S	3	0.2 GB	Low	D30
NVFP4	4

HuggingFace

huggingface-cli download hf-ggml-org--smolvlm-500m-instruct-gguf

Upgrade options

Mac mini M4 64GBBudget pick

C90.7 tok/s decode

~$1,099 MSRP

MacBook Pro M4 Pro 64GBBest value

C220.7 tok/s decode

~$1,599 MSRP