Can WizardLM 13B run on Intel Arc B570 10GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

WizardLM 13B needs ~22.0 GB but Intel Arc B570 10GB only has 10.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: 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) — 22.0 GB, exceeds 10.0 GB available

22.0 GB required10.0 GB available

220% VRAM needed

12.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.9 tok/s

TTFT

49039 ms

Safe context

Memory

22.0 GB / 10.0 GB

Offload

50%

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsWizardLM 13B on Intel Arc B570 10GB
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: 3.9 tok/s decode · 49.0s TTFT (warm) · 10 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 22.0 GB, but this setup only exposes 10.0 GB of usable VRAM.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

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.

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	7.7 tok/s	13803 ms	4K
Coding	F	Too heavy	3.9 tok/s	49039 ms	4K
Agentic Coding	F	Too heavy	3.9 tok/s	72550 ms	4K
Reasoning	F	Too heavy	3.9 tok/s	57955 ms	4K
RAG	F	Too heavy	3.9 tok/s	90687 ms	4K

Quantization options

How WizardLM 13B (13B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A73
Q3_K_SBest for your GPU	3	6.4 GB	Low	A73
NVFP4	4	7.3 GB	Medium	F0
Q4_K_M	4	7.9 GB	Medium	F0
Q5_K_M	5	9.4 GB	High	F0
Q6_K	6	10.7 GB	High	F0
Q8_0	8	13.9 GB	Very High	F0
F16	16	26.7 GB	Maximum	F0

Upgrade options

Hardware that runs WizardLM 13B well

Intel Arc Pro B60 24GBBudget pick

24 GB VRAM (+14)456 GB/s (+76)

Makes the model fit on the accelerator instead of staying completely out of reach.31.1 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.

~$599 MSRP

RTX 5090 32GBBiggest leap

32 GB VRAM (+22)1792 GB/s (+1412)

Makes the model fit on the accelerator instead of staying completely out of reach.146.9 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.

~$1,999 MSRP

Intel Data Center GPU Max 1550 128GBBest value

128 GB VRAM (+118)3200 GB/s (+2820)

Makes the model fit on the accelerator instead of staying completely out of reach.182 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.

~$15,000 MSRP

Gaudi 3 128GBIntel upgrade

128 GB VRAM (+118)3700 GB/s (+3320)

Makes the model fit on the accelerator instead of staying completely out of reach.182 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.

~$15,000 MSRP

Frequently asked questions

Can Intel Arc B570 10GB run WizardLM 13B?

No, WizardLM 13B requires more memory than Intel Arc B570 10GB provides.

How much VRAM does WizardLM 13B need?

WizardLM 13B (13B parameters) requires approximately 22.0 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 Intel Arc B570 10GB?

On Intel Arc B570 10GB, WizardLM 13B achieves approximately 3.9 tokens per second decode speed with a time-to-first-token of 49039ms using Q4_K_M quantization.

Can Intel Arc B570 10GB run WizardLM 13B for coding?

For coding workloads, WizardLM 13B on Intel Arc B570 10GB receives a F grade with 3.9 tok/s and 4K context.

What context window can WizardLM 13B use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, WizardLM 13B can safely use up to 4K 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 Intel Arc B570 10GB?

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.

Would CUDA be a better path than Intel Arc B570 10GB for WizardLM 13B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Arc B570 10GB See all hardware for WizardLM 13B

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Preview:

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 22.0 GB, but this setup only exposes 10.0 GB of usable VRAM.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

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.

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

7.7 tok/s

13803 ms

Coding

Too heavy

3.9 tok/s

49039 ms

Agentic Coding

Too heavy

3.9 tok/s

72550 ms

Reasoning

Too heavy

3.9 tok/s

57955 ms

RAG

Too heavy

3.9 tok/s

90687 ms

Quantization options

How WizardLM 13B (13B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A73
Q3_K_SBest for your GPU	3	6.4 GB	Low	A73
NVFP4	4	7.3 GB	Medium	F0
Q4_K_M	4	7.9 GB	Medium	F0
Q5_K_M	5	9.4 GB	High	F0
Q6_K	6	10.7 GB	High	F0
Q8_0	8	13.9 GB	Very High	F0
F16	16	26.7 GB	Maximum	F0

Frequently asked questions

Can Intel Arc B570 10GB run WizardLM 13B?

No, WizardLM 13B requires more memory than Intel Arc B570 10GB provides.

How much VRAM does WizardLM 13B need?

WizardLM 13B (13B parameters) requires approximately 22.0 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 Intel Arc B570 10GB?

On Intel Arc B570 10GB, WizardLM 13B achieves approximately 3.9 tokens per second decode speed with a time-to-first-token of 49039ms using Q4_K_M quantization.

Can Intel Arc B570 10GB run WizardLM 13B for coding?

For coding workloads, WizardLM 13B on Intel Arc B570 10GB receives a F grade with 3.9 tok/s and 4K context.

What context window can WizardLM 13B use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, WizardLM 13B can safely use up to 4K 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 Intel Arc B570 10GB?

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.

Would CUDA be a better path than Intel Arc B570 10GB for WizardLM 13B?