WizardLM 13B needs ~23.6 GB but Mac mini M2 24GB only has 17.3 GB. Try a smaller quantization or lighter model.
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
6.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.3 tok/s
TTFT
36308 ms
Safe context
8K
Memory
23.6 GB / 17.3 GB
Offload
30%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 23.6 GB, but this setup only exposes 17.3 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload (needs ~0.1 GB host RAM) | 7.9 tok/s | 13321 ms | 8K |
| Coding | F | Too heavy | 5.3 tok/s | 36308 ms | 8K |
| Agentic Coding | F | Too heavy | 3.7 tok/s | 76345 ms | 8K |
| Reasoning | F | Too heavy | 5.3 tok/s | 42909 ms | 8K |
| RAG | F | Too heavy | 3.7 tok/s | 95431 ms | 8K |
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 |
RX 7900 XTX 24GB | 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.
How WizardLM 13B (13B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B69 |
Q3_K_S | 3 | 6.4 GB | Low | B70 |
NVFP4 | 4 | 7.3 GB | Medium | A71 |
Q4_K_M | 4 | 7.9 GB | Medium | A71 |
Q5_K_M | 5 | 9.4 GB | High | A72 |
Q6_KBest for your GPU | 6 | 10.7 GB | High | A71 |
Q8_0 | 8 | 13.9 GB | Very High | F0 |
F16 | 16 | 26.7 GB | Maximum | F0 |
Opciones de mejora
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 62%.
~$799 MSRP
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,099 MSRP
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 62%.
~$1,099 MSRP
RTX 5090 32GBMayor saltoHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,999 MSRP
No, WizardLM 13B requires more memory than Mac mini M2 24GB provides.
WizardLM 13B (13B parameters) requires approximately 23.6 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 M2 24GB, WizardLM 13B achieves approximately 5.3 tokens per second decode speed with a time-to-first-token of 36308ms using Q4_K_M quantization.
For coding workloads, WizardLM 13B on Mac mini M2 24GB receives a F grade with 5.3 tok/s and 8K context.
On Mac mini M2 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.
Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
Not always. Mac mini M2 24GB 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.
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<iframe src="https://willitrunai.com/embed/wizardlm-13b-on-m2-24gb" 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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