Can WizardLM 13B run on RX 9070 XT 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

WizardLM 13B needs ~22.6 GB but RX 9070 XT 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

19.5 tok/s

TTFT

9948 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 XT 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: 19.5 tok/s decode · 9.9s TTFT (warm) · 49 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)	36.9 tok/s	2859 ms	7K
Coding	F	Too heavy	19.5 tok/s	9948 ms	7K
Agentic Coding	F	Too heavy	8.1 tok/s	34881 ms	7K
Reasoning	F	Too heavy	19.5 tok/s	11757 ms	7K
RAG	F	Too heavy	8.1 tok/s	43601 ms	7K

Inference speed

WizardLM 13B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for WizardLM 13B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~151 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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	151.4	Fits
RTX 4090 24GB	24 GB	Q4_K_M	96.6	Offloads
RX 7900 XTX 24GB	24 GB	Q4_K_M	87.2	Offloads
RTX 3090 24GB	24 GB	Q4_K_M	82.6	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	70.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	58.5	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	55.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	38.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	38.2	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	30.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	27.7	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	27.1	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.3	Fits
RTX 4070 12GB	12 GB	Q4_K_M	9.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	6.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	3.8	Too big

Estimates for single-stream decoding at Q4_K_M; 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 WizardLM 13B (13B params) fits at each quantization level on RX 9070 XT 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	7.3 GB	Medium	A72
Q4_K_M	4	7.9 GB	Medium	A72
Q5_K_M	5	9.4 GB	High	A72
Q6_KBest for your GPU	6	10.7 GB	High	A72
Q8_0	8	13.9 GB	Very High	F0
F16	16	26.7 GB	Maximum	F0

Opciones de mejora

Hardware que ejecuta bien WizardLM 13B

RX 7900 XT 20GBMejor relación calidad-precio

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

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.33.7 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 73%.

~$899 MSRP

RX 7900 XTX 24GBOpción económica

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

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.87.2 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$999 MSRP

Radeon AI PRO R9700 32GBMejora AMD

32 GB VRAM (+16)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.47.6 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,899 MSRP

Frequently asked questions

Can RX 9070 XT 16GB run WizardLM 13B?

No, WizardLM 13B requires more memory than RX 9070 XT 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 XT 16GB?

On RX 9070 XT 16GB, WizardLM 13B achieves approximately 19.5 tokens per second decode speed with a time-to-first-token of 9948ms using Q4_K_M quantization.

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

For coding workloads, WizardLM 13B on RX 9070 XT 16GB receives a F grade with 19.5 tok/s and 7K context.

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

On RX 9070 XT 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 XT 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 XT 16GB See all hardware for WizardLM 13B

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