Can WizardLM 13B run on RTX 3070 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

WizardLM 13B needs ~22.1 GB but RTX 3070 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: 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) — 22.1 GB, exceeds 8.0 GB available

22.1 GB required8.0 GB available

276% VRAM needed

14.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.9 tok/s

TTFT

32637 ms

Safe context

Memory

22.1 GB / 8.0 GB

Offload

60%

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsWizardLM 13B on RTX 3070 8GB
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: 5.9 tok/s decode · 32.6s TTFT (warm) · 15 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 22.1 GB, but this setup only exposes 8.0 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	F	Too heavy	6.9 tok/s	15384 ms	4K
Coding	F	Too heavy	5.9 tok/s	32637 ms	4K
Agentic Coding	F	Too heavy	5.9 tok/s	47472 ms	4K
Reasoning	F	Too heavy	5.9 tok/s	38571 ms	4K
RAG	F	Too heavy	5.9 tok/s	59340 ms	4K

Quantization options

How WizardLM 13B (13B params) fits at each quantization level on RTX 3070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	5.1 GB	Low	A73
Q3_K_S	3	6.4 GB	Low	F0

Upgrade options

Hardware that runs WizardLM 13B well

RTX 4000 Ada 20GBBest value

20 GB VRAM (+12)

Makes the model fit on the accelerator instead of staying completely out of reach.19.2 tok/s decode

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

Raises estimated decode speed by about 225%.

~$1,250 MSRP

RTX 3090 24GBBudget pick

24 GB VRAM (+16)936 GB/s (+488)

Makes the model fit on the accelerator instead of staying completely out of reach.82.6 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.

~$1,499 MSRP

RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+16)1008 GB/s (+560)

Makes the model fit on the accelerator instead of staying completely out of reach.96.6 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.

~$1,599 MSRP

Frequently asked questions

Can RTX 3070 8GB run WizardLM 13B?

No, WizardLM 13B requires more memory than RTX 3070 8GB provides.

How much VRAM does WizardLM 13B need?

WizardLM 13B (13B parameters) requires approximately 22.1 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 3070 8GB?

On RTX 3070 8GB, WizardLM 13B achieves approximately 5.9 tokens per second decode speed with a time-to-first-token of 32637ms using Q4_K_M quantization.

Can RTX 3070 8GB run WizardLM 13B for coding?

For coding workloads, WizardLM 13B on RTX 3070 8GB receives a F grade with 5.9 tok/s and 4K context.

What context window can WizardLM 13B use on RTX 3070 8GB?

On RTX 3070 8GB, 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 RTX 3070 8GB?

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 RTX 3070 8GB See all hardware for WizardLM 13B

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