WizardLM 13B needs ~24.5 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~9 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
1.5 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.5 GB host RAM)
Decode
8.6 tok/s
TTFT
22545 ms
Safe context
8K
Memory
24.5 GB / 23.0 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.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
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 0.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 9.6 tok/s | 11014 ms | 8K |
| Coding | B | Runs with offload (needs ~0.5 GB host RAM) | 8.6 tok/s | 22545 ms | 8K |
| Agentic Coding | F | Too heavy | 5.2 tok/s | 53748 ms | 8K |
| Reasoning | B | Runs with offload (needs ~0.5 GB host RAM) | 8.6 tok/s | 26644 ms | 8K |
| RAG | F | Too heavy | 5.2 tok/s | 67185 ms |
Inference speed
Estimated decode speed (tokens/sec) for WizardLM 13B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~151 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 151.4 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 96.6 | Offloads |
How WizardLM 13B (13B params) fits at each quantization level on Mac mini M4 32GB (23.0 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 startUpgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 171%.
~$1,599 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 580%.
~$3,999 MSRP
Yes, Mac mini M4 32GB can run WizardLM 13B with a B grade (Runs with offload (needs ~0.5 GB host RAM)). Expected decode speed: 8.6 tok/s.
WizardLM 13B (13B parameters) requires approximately 24.5 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 Mac mini M4 32GB, WizardLM 13B achieves approximately 8.6 tokens per second decode speed with a time-to-first-token of 22545ms using Q4_K_M quantization.
For coding workloads, WizardLM 13B on Mac mini M4 32GB receives a B grade with 8.6 tok/s and 8K context.
On Mac mini M4 32GB, 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-m4-mini-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 8K |
| 24 GB |
| Q4_K_M |
| 87.2 |
| Offloads |
| RTX 3090 24GB | 24 GB | Q4_K_M | 82.6 | Offloads |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 70.2 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 58.5 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 55.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 38.2 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 38.2 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 30.3 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 27.7 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 27.1 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 23.3 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 9.5 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 6.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 3.8 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
7.3 GB |
| Medium |
| B68 |
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 | A70 |
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.
Not always. Mac mini M4 32GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.