Can WizardLM 13B run on RTX PRO 4000 Blackwell 24GB?
YES — With Offload
WizardLM 13B needs ~23.7 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q4_K_M quantization, expect ~71 tok/s.
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.
Select quantization to explore
Fit status
Runs with offload
Decode
71.2 tok/s
TTFT
2720 ms
Safe context
8K
Memory
23.7 GB / 24.0 GB
Memory breakdown
See how fast it feels
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 71.2 tok/s | 1484 ms | 8K |
| Coding | A | Runs with offload | 71.2 tok/s | 2720 ms | 8K |
| Agentic Coding | F | Too heavy | 23.9 tok/s | 11793 ms | 8K |
| Reasoning | A | Runs with offload | 71.2 tok/s | 3214 ms | 8K |
| RAG | F | Too heavy | 23.9 tok/s | 14741 ms | 8K |
Quantization options
How WizardLM 13B (13B params) fits at each quantization level on RTX PRO 4000 Blackwell 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B66 |
Q3_K_S | 3 | 6.4 GB | Low | B67 |
NVFP4 | 4 | 7.3 GB | Medium | B67 |
Q4_K_M | 4 | 7.9 GB | Medium | B68 |
Q5_K_M | 5 | 9.4 GB | High | B69 |
Q6_K | 6 | 10.7 GB | High | B70 |
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | A71 |
F16 | 16 | 26.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run WizardLM 13B on your machine.
Run
lms load WizardLM-13B-V1.0 && lms server startYour hardware
More models your RTX PRO 4000 Blackwell 24GB can run
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 30.5B | S | 85.4 tok/s | ||
| 27B | S | 37 tok/s | ||
| 27B | S | 37.1 tok/s | ||
| 30B | S | 88.3 tok/s | ||
| 35B | A | 49.1 tok/s |
Frequently asked questions
Can RTX PRO 4000 Blackwell 24GB run WizardLM 13B?
Yes, RTX PRO 4000 Blackwell 24GB can run WizardLM 13B with a A grade (Runs with offload). Expected decode speed: 71.2 tok/s.
How much VRAM does WizardLM 13B need?
WizardLM 13B (13B parameters) requires approximately 23.7 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 PRO 4000 Blackwell 24GB?
On RTX PRO 4000 Blackwell 24GB, WizardLM 13B achieves approximately 71.2 tokens per second decode speed with a time-to-first-token of 2720ms using Q4_K_M quantization.
Can RTX PRO 4000 Blackwell 24GB run WizardLM 13B for coding?
For coding workloads, WizardLM 13B on RTX PRO 4000 Blackwell 24GB receives a A grade with 71.2 tok/s and 8K context.
What context window can WizardLM 13B use on RTX PRO 4000 Blackwell 24GB?
On RTX PRO 4000 Blackwell 24GB, 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.
What should I upgrade first if WizardLM 13B feels slow on RTX PRO 4000 Blackwell 24GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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<iframe src="https://willitrunai.com/embed/wizardlm-13b-on-rtx-pro-4000-blackwell-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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