Can WizardLM 13B run on RX 9070 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

WizardLM 13B needs ~22.6 GB but RX 9070 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: MediumStack: StandardBottleneck: Memory capacity

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) — 22.6 GB, exceeds 16.0 GB available

22.6 GB required16.0 GB available

141% VRAM needed

6.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

18.9 tok/s

TTFT

10269 ms

Safe context

Memory

22.6 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsWizardLM 13B on RX 9070 16GB
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: 18.9 tok/s decode · 10.3s TTFT (warm) · 47 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 22.6 GB, but this setup only exposes 16.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload (needs ~0.3 GB host RAM)	35.8 tok/s	2951 ms	7K
Coding	F	Too heavy	18.9 tok/s	10269 ms	7K
Agentic Coding	F	Too heavy	7.8 tok/s	36006 ms	7K
Reasoning	F	Too heavy	18.9 tok/s	12136 ms	7K
RAG	F	Too heavy	7.8 tok/s	45008 ms	7K

Quantization options

How WizardLM 13B (13B params) fits at each quantization level on RX 9070 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B70
Q3_K_S	3	6.4 GB	Low	A71
NVFP4	4

Upgrade options

Hardware that runs WizardLM 13B well

RX 7900 XT 20GBBest value

20 GB VRAM (+4)800 GB/s (+160)

Makes the model fit on the accelerator instead of staying completely out of reach.33.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 78%.

~$899 MSRP

RX 7900 XTX 24GBBudget pick

24 GB VRAM (+8)960 GB/s (+320)

Makes the model fit on the accelerator instead of staying completely out of reach.87.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$999 MSRP

Radeon AI PRO R9700 32GBAMD upgrade

32 GB VRAM (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.47.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,899 MSRP

Frequently asked questions

Can RX 9070 16GB run WizardLM 13B?

No, WizardLM 13B requires more memory than RX 9070 16GB provides.

How much VRAM does WizardLM 13B need?

WizardLM 13B (13B parameters) requires approximately 22.6 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 RX 9070 16GB?

On RX 9070 16GB, WizardLM 13B achieves approximately 18.9 tokens per second decode speed with a time-to-first-token of 10269ms using Q4_K_M quantization.

Can RX 9070 16GB run WizardLM 13B for coding?

For coding workloads, WizardLM 13B on RX 9070 16GB receives a F grade with 18.9 tok/s and 7K context.

What context window can WizardLM 13B use on RX 9070 16GB?

On RX 9070 16GB, WizardLM 13B can safely use up to 7K 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 RX 9070 16GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RX 9070 16GB See all hardware for WizardLM 13B

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