 |
VOOZH | about |
WizardLM 13B needs ~23.0 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~20 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
3.0 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1 GB host RAM)
Decode
19.7 tok/s
TTFT
9817 ms
Safe context
8K
Memory
23.0 GB / 20.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 35.4 tok/s | 2982 ms | 8K |
| Coding | B | Very compromised | 19.7 tok/s | 9817 ms | 8K |
| Agentic Coding | F | Too heavy | 8.1 tok/s | 34946 ms | 8K |
| Reasoning | B | Very compromised | 19.7 tok/s | 11601 ms | 8K |
| RAG | F | Too heavy | 8.1 tok/s | 43682 ms | 8K |
How WizardLM 13B (13B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B68 |
Q3_K_S | 3 | 6.4 GB | Low | B68 |
NVFP4 | 4 |
Copy-paste commands to run WizardLM 13B on your machine.
Run
lms load WizardLM-13B-V1.0 && lms server startUpgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 311%.
~$1,499 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 415%.
~$1,599 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 261%.
~$1,599 MSRP
| Medium |
| B69 |
Q4_K_M | 4 | 7.9 GB | Medium | B70 |
Q5_K_M | 5 | 9.4 GB | High | A71 |
Q6_K | 6 | 10.7 GB | High | A71 |
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | A71 |
F16 | 16 | 26.7 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
