StarCoder 7B needs ~15.1 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~15 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
Tight fit
Decode
15.2 tok/s
TTFT
12718 ms
Safe context
8K
Memory
15.1 GB / 17.3 GB
This setup is broadly balanced for this model.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 15.2 tok/s | 6937 ms | 8K |
| Coding | A | Tight fit | 15.2 tok/s | 12718 ms | 8K |
| Agentic Coding | F | Too heavy | 10.5 tok/s | 26711 ms | 8K |
| Reasoning | A | Tight fit | 15.2 tok/s | 15030 ms | 8K |
| RAG | F | Too heavy | 10.5 tok/s | 33389 ms | 8K |
Inference speed
Estimated decode speed (tokens/sec) for StarCoder 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 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 | 98.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.0 | Tight |
| RTX 3090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 56.2 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 51.5 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 48.0 | Heavy offload |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 45.3 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 30.2 | Heavy offload |
| RTX 4060 8GB | 8 GB | Q4_K_M | 11.4 | 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 StarCoder 7B (7B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B70 |
Q3_K_S | 3 | 3.4 GB | Low | A70 |
NVFP4 | 4 | 3.9 GB | Medium | A70 |
Q4_K_M | 4 | 4.3 GB | Medium | A71 |
Q5_K_M | 5 | 5.0 GB | High | A71 |
Q6_K | 6 | 5.7 GB | High | A72 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | A74 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Copy-paste commands to run StarCoder 7B on your machine.
Run
lms load starcoder-7b && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 12.7 tok/s | |
 Magistral Small 2507 | 24B | B | 3.7 tok/s | |
| Devstral Small 2 24B Instruct | 24B | B | 3.7 tok/s | |
| Qwen 3 14B | 14B | S | 8.2 tok/s | |
| Qwen 3 8B | 8B | S | 14.3 tok/s |
Yes, Mac mini M2 24GB can run StarCoder 7B with a A grade (Tight fit). Expected decode speed: 15.2 tok/s.
StarCoder 7B (7B parameters) requires approximately 15.1 GB of memory with Q4_K_M quantization.
The recommended quantization for StarCoder 7B is Q4_K_M, which balances quality and memory efficiency.
On Mac mini M2 24GB, StarCoder 7B achieves approximately 15.2 tokens per second decode speed with a time-to-first-token of 12718ms using Q4_K_M quantization.
For coding workloads, StarCoder 7B on Mac mini M2 24GB receives a A grade with 15.2 tok/s and 8K context.
On Mac mini M2 24GB, StarCoder 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/starcoder-7b-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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