willitrun·ai

Can SmolVLM 500M Instruct run on RTX 4090 24GB?

YES — Runs Great

D38Poor
Estimated from fit model

SmolVLM 500M Instruct needs ~3.8 GB VRAM. RTX 4090 24GB has 24.0 GB. With Q6_K quantization, expect ~8 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q6_K (High quality) — 3.8 GB, 8.0 tok/s, Runs well
3.8 GB required24.0 GB available
16% VRAM used

Fit status

Runs well

Decode

8.0 tok/s

TTFT

24200 ms

Safe context

3.2M

Memory

3.8 GB / 24.0 GB

Memory breakdown

Weights0.4 GB
KV Cache0.1 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsSmolVLM 500M Instruct on RTX 4090 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 8.0 tok/s decode · 24.2s TTFT (warm) · 20 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatDRuns well8.0 tok/s13200 ms1.6M
CodingDRuns well8.0 tok/s24200 ms3.2M
Agentic CodingDRuns well8.0 tok/s35200 ms5.5M
ReasoningDRuns well8.0 tok/s28600 ms3.2M
RAGDRuns well8.0 tok/s44000 ms5.5M

Inference speed

SmolVLM 500M Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for SmolVLM 500M Instruct at Q6_K across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~10 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ6_K9.5Fits
NVIDIARTX 4090 24GB
24 GBQ6_K8.0Fits
NVIDIARTX 4080 Super 16GB
16 GBQ6_K7.0Fits
NVIDIARTX 3090 24GB
24 GBQ6_K7.0Fits
NVIDIARTX 4070 12GB
12 GBQ6_K7.0Fits
NVIDIARTX 3060 12GB
12 GBQ6_K7.0Fits
NVIDIARTX 4060 8GB
8 GBQ6_K7.0Fits
RX 7900 XTX 24GB
24 GBQ6_K7.0Fits
MacBook Pro M4 Max 128GB
128 GBQ6_K7.0Fits
Mac Studio M3 Ultra 256GB
256 GBQ6_K7.0Fits
Mac Studio M2 Ultra 128GB
128 GBQ6_K7.0Fits
Mac Studio M1 Ultra 128GB
128 GBQ6_K7.0Fits
MacBook Pro M4 Max 64GB
64 GBQ6_K7.0Fits
MacBook Pro M3 Max 64GB
64 GBQ6_K7.0Fits
MacBook Pro M1 Max 64GB
64 GBQ6_K7.0Fits
MacBook Pro M4 Pro 48GB
48 GBQ6_K7.0Fits

Estimates for single-stream decoding at Q6_K; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How SmolVLM 500M Instruct (0.5B params) fits at each quantization level on RTX 4090 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.2 GB
LowC44
Q3_K_S
3
0.2 GB
LowC44
NVFP4
4
0.3 GB
MediumC44
Q4_K_M
4
0.3 GB
MediumC44
Q5_K_M
5
0.4 GB
HighC44
Q6_K
6
0.4 GB
HighC44
Q8_0
8
0.5 GB
Very HighC44
F16Best for your GPU
16
1.0 GB
MaximumC44

Get started

Copy-paste commands to run SmolVLM 500M Instruct on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "ggml-org/SmolVLM-500M-Instruct-GGUF" \ --hf-file "SmolVLM-500M-Instruct-GGUF-Q6_K.gguf" \ -c 4096 -ngl 99

Opciones de mejora

Hardware que ejecuta bien SmolVLM 500M Instruct

Mac mini M4 64GBMejor relación calidad-precio
64 GB Unified (+40)
D
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.7 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,099 MSRP

NVIDIARTX 5090 32GBOpción económica
32 GB VRAM (+8)1792 GB/s (+784)
D
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.9.5 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,999 MSRP

Frequently asked questions

Can RTX 4090 24GB run SmolVLM 500M Instruct?

Yes, RTX 4090 24GB can run SmolVLM 500M Instruct with a D grade (Runs well). Expected decode speed: 8.0 tok/s.

How much VRAM does SmolVLM 500M Instruct need?

SmolVLM 500M Instruct (0.5B parameters) requires approximately 3.8 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.

What speed will SmolVLM 500M Instruct run at on RTX 4090 24GB?

On RTX 4090 24GB, SmolVLM 500M Instruct achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24200ms using Q6_K quantization.

Can RTX 4090 24GB run SmolVLM 500M Instruct for coding?

For coding workloads, SmolVLM 500M Instruct on RTX 4090 24GB receives a D grade with 8.0 tok/s and 3.2M context.

What context window can SmolVLM 500M Instruct use on RTX 4090 24GB?

On RTX 4090 24GB, SmolVLM 500M Instruct can safely use up to 3.2M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4090 24GBSee all hardware for SmolVLM 500M Instruct
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