MacBook Pro M3 24GBBudget pick
24 GB Unified (+8)
C
This setup is broadly balanced for this model.21 tok/s decode
~$1,099 MSRP
Qwen 2.5 1.5B needs ~3.8 GB VRAM. RTX 4080 Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~24 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
24.0 tok/s
TTFT
8067 ms
Safe context
131K
Memory
3.8 GB / 16.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 24.0 tok/s | 4400 ms | 131K |
| Coding | C | Runs well | 24.0 tok/s | 8067 ms | 131K |
| Agentic Coding | C | Runs well | 24.0 tok/s | 11733 ms | 131K |
| Reasoning | C | Runs well | 24.0 tok/s | 9533 ms | 131K |
| RAG | C | Runs well | 24.0 tok/s | 14667 ms | 131K |
Inference speed
Estimated decode speed (tokens/sec) for Qwen 2.5 1.5B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~29 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 | 28.5 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 24.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 24.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 21.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 21.0 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 21.0 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 21.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 21.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 21.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 21.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 21.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 21.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 21.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 21.0 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 21.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.0 | Fits |
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 Qwen 2.5 1.5B (1.5B params) fits at each quantization level on RTX 4080 Super 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | C53 |
Q3_K_S | 3 | 0.7 GB | Low | C53 |
NVFP4 | 4 | 0.8 GB | Medium | C54 |
Q4_K_M | 4 | 0.9 GB | Medium | C54 |
Q5_K_M | 5 | 1.1 GB | High | C54 |
Q6_K | 6 | 1.2 GB | High | C54 |
Q8_0 | 8 | 1.6 GB | Very High | C54 |
F16Best for your GPU | 16 | 3.1 GB | Maximum | B55 |
Copy-paste commands to run Qwen 2.5 1.5B on your machine.
Run
ollama run qwen2.5:1.5bUpgrade options
Yes, RTX 4080 Super 16GB can run Qwen 2.5 1.5B with a C grade (Runs well). Expected decode speed: 24.0 tok/s.
Qwen 2.5 1.5B (1.5B parameters) requires approximately 3.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 2.5 1.5B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4080 Super 16GB, Qwen 2.5 1.5B achieves approximately 24.0 tokens per second decode speed with a time-to-first-token of 8067ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 1.5B on RTX 4080 Super 16GB receives a C grade with 24.0 tok/s and 131K context.
On RTX 4080 Super 16GB, Qwen 2.5 1.5B 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-2.5-1.5b-on-rtx-4080-super-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: