Mac mini M4 64GBBudget pick
64 GB Unified (+32)
C
Adds memory headroom for longer context windows and future model growth.7 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Qwen 2.5 0.5B needs ~4.9 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~7 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 well
Decode
7.0 tok/s
TTFT
27657 ms
Safe context
131K
Memory
4.9 GB / 32.0 GB
This model fits, but memory bandwidth is the part holding decode speed back.
Throughput will feel slow
Estimated decode speed is only 7.0 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Runs well | 7.0 tok/s | 15086 ms | 131K |
| Coding | D | Runs well | 7.0 tok/s | 27657 ms | 131K |
| Agentic Coding | D | Runs well | 7.0 tok/s | 40229 ms | 131K |
| Reasoning | D | Runs well | 7.0 tok/s | 32686 ms | 131K |
| RAG | D | Runs well | 7.0 tok/s | 50286 ms | 131K |
How Qwen 2.5 0.5B (0.5B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.2 GB | Low | C45 |
Q3_K_S | 3 | 0.2 GB | Low | C45 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 2.5 0.5B on your machine.
Run
ollama run qwen2.5:0.5bUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Yes, NVIDIA V100 32GB can run Qwen 2.5 0.5B with a D grade (Runs well). Expected decode speed: 7.0 tok/s.
Qwen 2.5 0.5B (0.5B parameters) requires approximately 4.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 2.5 0.5B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA V100 32GB, Qwen 2.5 0.5B achieves approximately 7.0 tokens per second decode speed with a time-to-first-token of 27657ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 0.5B on NVIDIA V100 32GB receives a D grade with 7.0 tok/s and 131K context.
On NVIDIA V100 32GB, Qwen 2.5 0.5B can safely use up to 131K tokens of context. The model's official context limit is 131K, 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/qwen-2.5-0.5b-on-v100-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| C45 |
Q4_K_M | 4 | 0.3 GB | Medium | C45 |
Q5_K_M | 5 | 0.4 GB | High | C45 |
Q6_K | 6 | 0.4 GB | High | C45 |
Q8_0 | 8 | 0.5 GB | Very High | C45 |
F16Best for your GPU | 16 | 1.0 GB | Maximum | C45 |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.