Can WizardLM 13B run on RTX 3090 24GB?

YES — With Offload

A74Great

Estimated from fit model

WizardLM 13B needs ~23.7 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~83 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: Balanced

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

Q4_K_M (Medium quality) — 23.7 GB, 82.6 tok/s, Runs with offload

23.7 GB required24.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

82.6 tok/s

TTFT

2343 ms

Safe context

Memory

23.7 GB / 24.0 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsWizardLM 13B on RTX 3090 24GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 82.6 tok/s decode · 2.3s TTFT (warm) · 207 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	82.6 tok/s	1278 ms	8K
Coding	A	Runs with offload	82.6 tok/s	2343 ms	8K
Agentic Coding	F	Too heavy	26.5 tok/s	10635 ms	8K
Reasoning	A	Runs with offload	82.6 tok/s	2769 ms	8K
RAG	F	Too heavy	26.5 tok/s	13293 ms	8K

Quantization options

How WizardLM 13B (13B params) fits at each quantization level on RTX 3090 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B66
Q3_K_S	3	6.4 GB	Low	B67
NVFP4	4	7.3 GB	Medium	B67
Q4_K_M	4	7.9 GB	Medium	B68
Q5_K_M	5	9.4 GB	High	B69
Q6_K	6	10.7 GB	High	B70
Q8_0Best for your GPU	8	13.9 GB	Very High	A71
F16	16	26.7 GB	Maximum	F0

Get started

Copy-paste commands to run WizardLM 13B on your machine.

Run

lms load WizardLM-13B-V1.0 && lms server start

Your hardware

More models your RTX 3090 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	99.1 tok/s
Qwen 3.5 27B	27B	S	43 tok/s
Qwen 3.6 27B	27B	S	43.1 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	102.5 tok/s
Qwen 3.5 35B A3B	35B	A	55.5 tok/s

Frequently asked questions

Can RTX 3090 24GB run WizardLM 13B?

Yes, RTX 3090 24GB can run WizardLM 13B with a A grade (Runs with offload). Expected decode speed: 82.6 tok/s.

How much VRAM does WizardLM 13B need?

WizardLM 13B (13B parameters) requires approximately 23.7 GB of memory with Q4_K_M quantization.

What is the best quantization for WizardLM 13B?

The recommended quantization for WizardLM 13B is Q4_K_M, which balances quality and memory efficiency.

What speed will WizardLM 13B run at on RTX 3090 24GB?

On RTX 3090 24GB, WizardLM 13B achieves approximately 82.6 tokens per second decode speed with a time-to-first-token of 2343ms using Q4_K_M quantization.

Can RTX 3090 24GB run WizardLM 13B for coding?

For coding workloads, WizardLM 13B on RTX 3090 24GB receives a A grade with 82.6 tok/s and 8K context.

What context window can WizardLM 13B use on RTX 3090 24GB?

On RTX 3090 24GB, WizardLM 13B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if WizardLM 13B feels slow on RTX 3090 24GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 3090 24GB See all hardware for WizardLM 13B

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