Can WizardLM 13B run on RTX 5090 32GB?

YES — Runs Great

A78Great

Estimated from fit model

WizardLM 13B needs ~24.5 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~151 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 24.5 GB, 151.4 tok/s, Runs well

24.5 GB required32.0 GB available

77% VRAM used

Fit status

Runs well

Decode

151.4 tok/s

TTFT

1279 ms

Safe context

Memory

24.5 GB / 32.0 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsWizardLM 13B on RTX 5090 32GB
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: 151.4 tok/s decode · 1.3s TTFT (warm) · 379 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	151.4 tok/s	697 ms	8K
Coding	A	Runs well	151.4 tok/s	1279 ms	8K
Agentic Coding	B	Very compromised (needs ~1 GB host RAM)	87.3 tok/s	3225 ms	8K
Reasoning	A	Runs well	151.4 tok/s	1511 ms	8K
RAG	B	Very compromised (needs ~1 GB host RAM)	87.3 tok/s	4031 ms	8K

Quantization options

How WizardLM 13B (13B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B64
Q3_K_S	3	6.4 GB	Low	B65
NVFP4	4	7.3 GB	Medium	B65
Q4_K_M	4	7.9 GB	Medium	B65
Q5_K_M	5	9.4 GB	High	B66
Q6_K	6	10.7 GB	High	B67
Q8_0	8	13.9 GB	Very High	B68
F16Best for your GPU	16	26.7 GB	Maximum	B69

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 5090 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	181.6 tok/s
Qwen 3.5 27B	27B	S	78.7 tok/s
Qwen 3.6 27B	27B	S	79 tok/s
Qwen 3.6 35B A3B	35B	S	128.2 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	187.8 tok/s

Frequently asked questions

Can RTX 5090 32GB run WizardLM 13B?

Yes, RTX 5090 32GB can run WizardLM 13B with a A grade (Runs well). Expected decode speed: 151.4 tok/s.

How much VRAM does WizardLM 13B need?

WizardLM 13B (13B parameters) requires approximately 24.5 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 5090 32GB?

On RTX 5090 32GB, WizardLM 13B achieves approximately 151.4 tokens per second decode speed with a time-to-first-token of 1279ms using Q4_K_M quantization.

Can RTX 5090 32GB run WizardLM 13B for coding?

For coding workloads, WizardLM 13B on RTX 5090 32GB receives a A grade with 151.4 tok/s and 8K context.

What context window can WizardLM 13B use on RTX 5090 32GB?

On RTX 5090 32GB, 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.

See all results for RTX 5090 32GB See all hardware for WizardLM 13B

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