WizardLM 13B needs ~23.7 GB VRAM. RTX 5090 Laptop 24GB has 24.0 GB. With Q4_K_M quantization, expect ~95 tok/s.
Operating mode
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
94.9 tok/s
TTFT
2040 ms
Safe context
8K
Memory
23.7 GB / 24.0 GB
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 94.9 tok/s | 1113 ms | 8K |
| Coding | A | Runs with offload | 94.9 tok/s | 2040 ms | 8K |
| Agentic Coding | F | Too heavy | 30.4 tok/s | 9258 ms | 8K |
| Reasoning | A | Runs with offload | 94.9 tok/s | 2411 ms | 8K |
| RAG | F | Too heavy | 30.4 tok/s | 11572 ms | 8K |
How WizardLM 13B (13B params) fits at each quantization level on RTX 5090 Laptop 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 |
Copy-paste commands to run WizardLM 13B on your machine.
Run
lms load WizardLM-13B-V1.0 && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 113.8 tok/s | |
| Qwen 3.5 27B | 27B | S | 49.4 tok/s |
Yes, RTX 5090 Laptop 24GB can run WizardLM 13B with a A grade (Runs with offload). Expected decode speed: 94.9 tok/s.
WizardLM 13B (13B parameters) requires approximately 23.7 GB of memory with Q4_K_M quantization.
The recommended quantization for WizardLM 13B is Q4_K_M, which balances quality and memory efficiency.
On RTX 5090 Laptop 24GB, WizardLM 13B achieves approximately 94.9 tokens per second decode speed with a time-to-first-token of 2040ms using Q4_K_M quantization.
For coding workloads, WizardLM 13B on RTX 5090 Laptop 24GB receives a A grade with 94.9 tok/s and 8K context.
On RTX 5090 Laptop 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/wizardlm-13b-on-rtx-5090-laptop-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
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 |
| Qwen 3.6 27B | 27B | S | 49.5 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | S | 117.7 tok/s |
| Qwen 3.5 35B A3B | 35B | A | 63.8 tok/s |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.